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Curriculum Vitae (english)
Curriculum Vitae (italiano)
Pubblicazioni

Graziana CAVONE

Post-Doc Research Fellow

Graziana Cavone received the Laurea degree summa cum laude in Control Engineering in 2013 from the Polytechnic of Bari, Italy and the Ph.D. degree (excellent with honors) in Electronic and Computer Engineering in 2018 from the University of Cagliari, Italy. She currently is a post-doc Research Fellow at Polytechnic of Bari. She has been Research Fellow in 2014 at Polytechnic of Bari, Italy, and Visiting Ph.D. Student in 2016-2017 at Delft University of Technology, the Netherlands. Her research interests include modelling, simulation, optimization, and control of discrete-event and hybrid systems, distributed control, automated manufacturing systems, intelligent transportation, smart cities.

She is the Local Arrangements chair of the 2021 Mediterranean Conference on Control and Automation. She is Associate Editor for the international Journal Results in Control and Optimization (RICO). She was member of the International Program Committeeof 20+ international conferences and Guest Editor for special issues on international journals. She was awarded a research grant by the National Science Foundation of China for year 2020.


Publications

2022

  • Cavone, G., Van Den Boom, T., Blenkers, L., Dotoli, M., Seatzu, C. & De Schutter, B. (2022) An MPC-Based Rescheduling Algorithm for Disruptions and Disturbances in Large-Scale Railway Networks. IN IEEE Transactions on Automation Science and Engineering, 19.99-112. doi:10.1109/TASE.2020.3040940
    [BibTeX] [Abstract] [Download PDF]
    Railways are a well-recognized sustainable transportation mode that helps to satisfy the continuously growing mobility demand. However, the management of railway traffic in large-scale networks is a challenging task, especially when both a major disruption and various disturbances occur simultaneously. We propose an automatic rescheduling algorithm for real-time control of railway traffic that aims at minimizing the delays induced by the disruption and disturbances, as well as the resulting cancellations of train runs and turn-backs (or short-turns) and shuntings of trains in stations. The real-time control is based on the Model Predictive Control (MPC) scheme where the rescheduling problem is solved by mixed integer linear programming using macroscopic and mesoscopic models. The proposed resolution algorithm combines a distributed optimization method and bi-level heuristics to provide feasible control actions for the whole network in short computation time, without neglecting physical limitations nor operations at disrupted stations. A realistic simulation test is performed on the complete Dutch railway network. The results highlight the effectiveness of the method in properly minimizing the delays and rapidly providing feasible feedback control actions for the whole network. Note to Practitioners – This article aims at contributing to the enhancement of the core functionalities of Automatic Train Control (ATC) systems and, in particular, of the Automatic Train Supervision (ATS) module, which is included in ATC systems. In general, the ATS module allows to automate the train traffic supervision and consequently the rescheduling of the railway traffic in case of unexpected events. However, the implementation of an efficient rescheduling technique that automatically and rapidly provides the control actions necessary to restore the railway traffic operations to the nominal schedule is still an open issue. Most literature contributions fail in providing rescheduling methods that successfully determine high-quality solutions in less than one minute and include real-time information regarding the large-scale railway system state. This research proposes a semi-heuristic control algorithm based on MPC that, on the one hand, overcomes the limitations of manual rescheduling (i.e., suboptimal, stressful, and delayed decisions) and, on the other hand, offers the advantages of online and closed-loop control of railway traffic based on continuous monitoring of the traffic state to rapidly restore railway traffic operations to the nominal schedule. The semi-heuristic procedure permits to significantly reduce the computation time necessary to solve the rescheduling problem compared with an exact procedure; moreover, the use of a distributed optimization approach permits the application of the algorithm to large instances of the rescheduling problem, and the inclusion of both the traffic and rolling stock constraints related to the disrupted area. The method is tested on a realistic simulation environment, thus still requires further refinements for the integration into a real ATS system. Further developments will also consider the occurrence of various simultaneous disruptions in the network. © 2004-2012 IEEE.
    @ARTICLE{Cavone202299,
    author={Cavone, G. and Van Den Boom, T. and Blenkers, L. and Dotoli, M. and Seatzu, C. and De Schutter, B.},
    title={An MPC-Based Rescheduling Algorithm for Disruptions and Disturbances in Large-Scale Railway Networks},
    journal={IEEE Transactions on Automation Science and Engineering},
    year={2022},
    volume={19},
    number={1},
    pages={99-112},
    doi={10.1109/TASE.2020.3040940},
    note={cited By 1},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098771253&doi=10.1109%2fTASE.2020.3040940&partnerID=40&md5=155dc1581a4c1e8c1c487e2eb7c75cbd},
    affiliation={Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy; Delft Center for Systems and Control, Delft University of Technology, Delft, Netherlands; Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy},
    abstract={Railways are a well-recognized sustainable transportation mode that helps to satisfy the continuously growing mobility demand. However, the management of railway traffic in large-scale networks is a challenging task, especially when both a major disruption and various disturbances occur simultaneously. We propose an automatic rescheduling algorithm for real-time control of railway traffic that aims at minimizing the delays induced by the disruption and disturbances, as well as the resulting cancellations of train runs and turn-backs (or short-turns) and shuntings of trains in stations. The real-time control is based on the Model Predictive Control (MPC) scheme where the rescheduling problem is solved by mixed integer linear programming using macroscopic and mesoscopic models. The proposed resolution algorithm combines a distributed optimization method and bi-level heuristics to provide feasible control actions for the whole network in short computation time, without neglecting physical limitations nor operations at disrupted stations. A realistic simulation test is performed on the complete Dutch railway network. The results highlight the effectiveness of the method in properly minimizing the delays and rapidly providing feasible feedback control actions for the whole network. Note to Practitioners - This article aims at contributing to the enhancement of the core functionalities of Automatic Train Control (ATC) systems and, in particular, of the Automatic Train Supervision (ATS) module, which is included in ATC systems. In general, the ATS module allows to automate the train traffic supervision and consequently the rescheduling of the railway traffic in case of unexpected events. However, the implementation of an efficient rescheduling technique that automatically and rapidly provides the control actions necessary to restore the railway traffic operations to the nominal schedule is still an open issue. Most literature contributions fail in providing rescheduling methods that successfully determine high-quality solutions in less than one minute and include real-time information regarding the large-scale railway system state. This research proposes a semi-heuristic control algorithm based on MPC that, on the one hand, overcomes the limitations of manual rescheduling (i.e., suboptimal, stressful, and delayed decisions) and, on the other hand, offers the advantages of online and closed-loop control of railway traffic based on continuous monitoring of the traffic state to rapidly restore railway traffic operations to the nominal schedule. The semi-heuristic procedure permits to significantly reduce the computation time necessary to solve the rescheduling problem compared with an exact procedure; moreover, the use of a distributed optimization approach permits the application of the algorithm to large instances of the rescheduling problem, and the inclusion of both the traffic and rolling stock constraints related to the disrupted area. The method is tested on a realistic simulation environment, thus still requires further refinements for the integration into a real ATS system. Further developments will also consider the occurrence of various simultaneous disruptions in the network. © 2004-2012 IEEE.},
    author_keywords={Mixed Integer Linear (MIL) Programming (MILP); Model Predictive Control (MPC); railway traffic disruption; rescheduling algorithms},
    document_type={Article},
    source={Scopus},
    }

2021

  • Luo, J., Tong, Y., Cavone, G. & Dotoli, M. (2021) A Service-Oriented Metro Traffic Regulation Method for Improving Operation Performance IN IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC., 3533-3538. doi:10.1109/ITSC48978.2021.9564503
    [BibTeX] [Abstract] [Download PDF]
    For high density metro traffic, nowadays the time-variant passenger flow is the main cause of train delays and stranded passengers. In this work a novel framework that integrates a passenger flow module (PFM) and a train operation module (TOM) is proposed with the aim of simultaneously minimizing traffic delays and passengers’ discomfort. The two modules interact with each other so that the headway time is automatically adjusted when a platform is overcrowded, and the train traffic is immediately regulated according to the new headway time. As a result, the number of passengers on the platform and their total waiting time can be significantly reduced. Numerical results are provided to show the effectiveness of the proposed method in improving the operation performance while minimizing the passengers’ discomfort. © 2021 IEEE.
    @CONFERENCE{Luo20213533,
    author={Luo, J. and Tong, Y. and Cavone, G. and Dotoli, M.},
    title={A Service-Oriented Metro Traffic Regulation Method for Improving Operation Performance},
    journal={IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC},
    year={2021},
    volume={2021-September},
    pages={3533-3538},
    doi={10.1109/ITSC48978.2021.9564503},
    note={cited By 0},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118448237&doi=10.1109%2fITSC48978.2021.9564503&partnerID=40&md5=75a73b3639d1cc8614a800ad9d1f66bb},
    affiliation={School of Information Science and Technology, Southwest Jiaotong University, Chengdu, 611756, China; Polytechnic of Bari, Department of Electrical and Information Engineering, Bari, 70125, Italy},
    abstract={For high density metro traffic, nowadays the time-variant passenger flow is the main cause of train delays and stranded passengers. In this work a novel framework that integrates a passenger flow module (PFM) and a train operation module (TOM) is proposed with the aim of simultaneously minimizing traffic delays and passengers' discomfort. The two modules interact with each other so that the headway time is automatically adjusted when a platform is overcrowded, and the train traffic is immediately regulated according to the new headway time. As a result, the number of passengers on the platform and their total waiting time can be significantly reduced. Numerical results are provided to show the effectiveness of the proposed method in improving the operation performance while minimizing the passengers' discomfort. © 2021 IEEE.},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Bozza, A., Cavone, G., Carli, R., Mazzoccoli, L. & Dotoli, M. (2021) An MPC-based Approach for the Feedback Control of the Cold Sheet Metal Forming Process IN IEEE International Conference on Automation Science and Engineering., 286-291. doi:10.1109/CASE49439.2021.9551602
    [BibTeX] [Abstract] [Download PDF]
    In the automotive sector the cold forming of metal sheets is one of the main production activities. However, it is also one of the main source of production wastes. The generally adopted strategy to reduce the number of abnormal stamped parts is the feedback control of the stamping press (i.e., the machine control), while the feedback control of the stamping process is rarely considered. The process control, differently from the press control, can allow the monitoring of the state of the stamped part during the formation phase and the provision of corrective actions in case of abnormal behaviors of the metal sheet, thus ensuring a more precise control of the process. In this context, this paper presents a novel methodology for the cold metal forming process control based on Model Predictive Control (MPC). Firstly, a dynamical model of the system is defined that describes the draw-in of n critical points of the metal sheet as a function of the Blank Holder Force (BHF) and the punch stroke. Then, two different MPC-based real-time controllers are built for two different types of press configuration: the monolithic and the differential one. In the first case, a mono-MPC control system evaluates the draw-in of n critical points and computes a single couple of control signals (i.e., the BHF and the punch stroke). In the second case, a multi-MPC control system computes n different couples of control signals, i.e., one for each monitored draw-in. Finally, a case study is presented with the aim to test both the architectures, considering several simulation scenarios (with or without external disturbances on the plant), in order to make a control system architectures comparison in terms of tracking errors and workpiece quality. © 2021 IEEE.
    @CONFERENCE{Bozza2021286,
    author={Bozza, A. and Cavone, G. and Carli, R. and Mazzoccoli, L. and Dotoli, M.},
    title={An MPC-based Approach for the Feedback Control of the Cold Sheet Metal Forming Process},
    journal={IEEE International Conference on Automation Science and Engineering},
    year={2021},
    volume={2021-August},
    pages={286-291},
    doi={10.1109/CASE49439.2021.9551602},
    note={cited By 0},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117041530&doi=10.1109%2fCASE49439.2021.9551602&partnerID=40&md5=8f6b0f6133565ad0c087ea675cf9c3b9},
    affiliation={Department of Electrical and Information Engineering of the Polytechnic of Bari, Italy},
    abstract={In the automotive sector the cold forming of metal sheets is one of the main production activities. However, it is also one of the main source of production wastes. The generally adopted strategy to reduce the number of abnormal stamped parts is the feedback control of the stamping press (i.e., the machine control), while the feedback control of the stamping process is rarely considered. The process control, differently from the press control, can allow the monitoring of the state of the stamped part during the formation phase and the provision of corrective actions in case of abnormal behaviors of the metal sheet, thus ensuring a more precise control of the process. In this context, this paper presents a novel methodology for the cold metal forming process control based on Model Predictive Control (MPC). Firstly, a dynamical model of the system is defined that describes the draw-in of n critical points of the metal sheet as a function of the Blank Holder Force (BHF) and the punch stroke. Then, two different MPC-based real-time controllers are built for two different types of press configuration: the monolithic and the differential one. In the first case, a mono-MPC control system evaluates the draw-in of n critical points and computes a single couple of control signals (i.e., the BHF and the punch stroke). In the second case, a multi-MPC control system computes n different couples of control signals, i.e., one for each monitored draw-in. Finally, a case study is presented with the aim to test both the architectures, considering several simulation scenarios (with or without external disturbances on the plant), in order to make a control system architectures comparison in terms of tracking errors and workpiece quality. © 2021 IEEE.},
    author_keywords={Metal forming; Model Predictive Control; process control; real-time feedback control},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Scarabaggio, P., Carli, R., Cavone, G., Epicoco, N. & Dotoli, M. (2021) Modeling, Estimation, and Optimal Control of Anti-COVID-19 Multi-dose Vaccine Administration IN IEEE International Conference on Automation Science and Engineering., 990-995. doi:10.1109/CASE49439.2021.9551418
    [BibTeX] [Abstract] [Download PDF]
    The recent trends of the COVID-19 research are being devoted to disease transmission modeling in presence of vaccinated individuals, while the emerging needs are being focused on developing effective strategies for the optimal distribution of vaccine between population. In this context, we propose a novel non-linear time-varying model that effectively supports policy-makers in predicting and analyzing the dynamics of COVID-19 when partially and fully immune individuals are included in the population. Differently from the related literature, where the common strategies typically rely on the prioritization of the different classes of individuals, we propose a novel Model Predictive Control approach to optimally control the multi-dose vaccine administration in the case the available number of doses is not sufficient to cover the whole population. Focusing on the minimization of the expected number of deaths, the approach discriminates between the number of first and second doses. We calibrate the model on the Israeli scenario using real data and we estimate the impact of the vaccine administration on the virus dynamics. Lastly, we assess the impact of the first dose of the Pfizer’s vaccine confirming the results of clinical tests. © 2021 IEEE.
    @CONFERENCE{Scarabaggio2021990,
    author={Scarabaggio, P. and Carli, R. and Cavone, G. and Epicoco, N. and Dotoli, M.},
    title={Modeling, Estimation, and Optimal Control of Anti-COVID-19 Multi-dose Vaccine Administration},
    journal={IEEE International Conference on Automation Science and Engineering},
    year={2021},
    volume={2021-August},
    pages={990-995},
    doi={10.1109/CASE49439.2021.9551418},
    note={cited By 0},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117035432&doi=10.1109%2fCASE49439.2021.9551418&partnerID=40&md5=489bcdcd710fb127ad522dbebe163265},
    affiliation={Polytechnic of Bari, Department of Electrical and Information Engineering, Italy; Ctr. of Excellence Dews (Des. Methodologies for Embedded Controllers Wireless Interconnect and Syst.-on-chip), University of l'Aquila, Department of Information Engineering, Computer Science and Mathematics (DISIM), L'Aquila, Italy},
    abstract={The recent trends of the COVID-19 research are being devoted to disease transmission modeling in presence of vaccinated individuals, while the emerging needs are being focused on developing effective strategies for the optimal distribution of vaccine between population. In this context, we propose a novel non-linear time-varying model that effectively supports policy-makers in predicting and analyzing the dynamics of COVID-19 when partially and fully immune individuals are included in the population. Differently from the related literature, where the common strategies typically rely on the prioritization of the different classes of individuals, we propose a novel Model Predictive Control approach to optimally control the multi-dose vaccine administration in the case the available number of doses is not sufficient to cover the whole population. Focusing on the minimization of the expected number of deaths, the approach discriminates between the number of first and second doses. We calibrate the model on the Israeli scenario using real data and we estimate the impact of the vaccine administration on the virus dynamics. Lastly, we assess the impact of the first dose of the Pfizer's vaccine confirming the results of clinical tests. © 2021 IEEE.},
    author_keywords={COVID-19; model predictive control; pandemic modeling; vaccine; vaccine distribution},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Proia, S., Carli, R., Cavone, G. & Dotoli, M. (2021) A Literature Review on Control Techniques for Collaborative Robotics in Industrial Applications IN IEEE International Conference on Automation Science and Engineering., 591-596. doi:10.1109/CASE49439.2021.9551600
    [BibTeX] [Abstract] [Download PDF]
    One of the key enabling technologies that has made Industry 4.0 a concrete reality is without doubt collaborative robotics, which is also evolving as a fundamental pillar of the next revolution, the so-called Industry 5.0. The improvement of safety and employees’ well-being, together with the increment of profitability and productivity, are indeed the main goals of human-robot collaboration (HRC) in the industrial setting. The robotic controller design and the analysis of existing decision and control techniques are crucially needed to develop innovative models and state-of-the-art methodologies for a safe, ergonomic, and efficient HRC. To this aim, this article presents an accurate review of the most recent and relevant papers in the related field, focusing on the control perspective. All the surveyed works are carefully selected and categorized by target (i.e., safety, ergonomics, and efficiency), and then by problem and type of control, in presence or absence of optimization. Finally, the discussion of the achieved results and the analysis of the emerging challenges in this research field are reported, highlighting the identified gaps and promising future developments in the context of the digital evolution. © 2021 IEEE.
    @CONFERENCE{Proia2021591,
    author={Proia, S. and Carli, R. and Cavone, G. and Dotoli, M.},
    title={A Literature Review on Control Techniques for Collaborative Robotics in Industrial Applications},
    journal={IEEE International Conference on Automation Science and Engineering},
    year={2021},
    volume={2021-August},
    pages={591-596},
    doi={10.1109/CASE49439.2021.9551600},
    note={cited By 1},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85116978273&doi=10.1109%2fCASE49439.2021.9551600&partnerID=40&md5=a834d330c2d2bedcecdff62c8eaceae7},
    affiliation={Department of Electrical and Information Engineering of the Polytechnic of Bari, Italy},
    abstract={One of the key enabling technologies that has made Industry 4.0 a concrete reality is without doubt collaborative robotics, which is also evolving as a fundamental pillar of the next revolution, the so-called Industry 5.0. The improvement of safety and employees' well-being, together with the increment of profitability and productivity, are indeed the main goals of human-robot collaboration (HRC) in the industrial setting. The robotic controller design and the analysis of existing decision and control techniques are crucially needed to develop innovative models and state-of-the-art methodologies for a safe, ergonomic, and efficient HRC. To this aim, this article presents an accurate review of the most recent and relevant papers in the related field, focusing on the control perspective. All the surveyed works are carefully selected and categorized by target (i.e., safety, ergonomics, and efficiency), and then by problem and type of control, in presence or absence of optimization. Finally, the discussion of the achieved results and the analysis of the emerging challenges in this research field are reported, highlighting the identified gaps and promising future developments in the context of the digital evolution. © 2021 IEEE.},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Cavone, G., Carli, R., Troccoli, G., Tresca, G. & Dotoli, M. (2021) A MILP approach for the multi-drop container loading problem resolution in logistics 4.0 IN 2021 29th Mediterranean Conference on Control and Automation, MED 2021., 687-692. doi:10.1109/MED51440.2021.9480359
    [BibTeX] [Abstract] [Download PDF]
    This paper addresses the multi-drop container loading problem (CLP), i.e., the problem of packing multiple bins -associated to multiple deliveries to one or more customers- into a finite number of transport units (TUs). Differently from the traditional CLP, the multi-drop CLP has been rarely handled in the literature, while effective algorithms to automatically solve this problem are needed to improve the efficiency and sustainability of internal logistics. To this aim, we propose a novel algorithm that solves a delivery-based mixed integer linear programming formulation of the problem. The algorithm efficiently determines the optimal composition of TUs by minimizing the unused space, while fulfilling a set of geometric and safety constraints, and complying with the delivery allocation. In particular, the proposed algorithm includes two steps: the first aims at clustering bins into groups to be compatibly loaded in various TUs; the latter aims at determining the optimal configuration of each group in the related TU. Finally, the proposed algorithm is applied to several realistic case studies with the aim of testing and analysing its effectiveness in producing stable and compact TU loading configurations in a short computation time, despite the high computational complexity of the multi-drop CLP. © 2021 IEEE.
    @CONFERENCE{Cavone2021687,
    author={Cavone, G. and Carli, R. and Troccoli, G. and Tresca, G. and Dotoli, M.},
    title={A MILP approach for the multi-drop container loading problem resolution in logistics 4.0},
    journal={2021 29th Mediterranean Conference on Control and Automation, MED 2021},
    year={2021},
    pages={687-692},
    doi={10.1109/MED51440.2021.9480359},
    art_number={9480359},
    note={cited By 1},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113699292&doi=10.1109%2fMED51440.2021.9480359&partnerID=40&md5=734929101ce82724d3629ddb1e06f4a4},
    affiliation={Polytechnic of Bari, Department of Electrical and Information Engineering, Italy},
    abstract={This paper addresses the multi-drop container loading problem (CLP), i.e., the problem of packing multiple bins -associated to multiple deliveries to one or more customers- into a finite number of transport units (TUs). Differently from the traditional CLP, the multi-drop CLP has been rarely handled in the literature, while effective algorithms to automatically solve this problem are needed to improve the efficiency and sustainability of internal logistics. To this aim, we propose a novel algorithm that solves a delivery-based mixed integer linear programming formulation of the problem. The algorithm efficiently determines the optimal composition of TUs by minimizing the unused space, while fulfilling a set of geometric and safety constraints, and complying with the delivery allocation. In particular, the proposed algorithm includes two steps: the first aims at clustering bins into groups to be compatibly loaded in various TUs; the latter aims at determining the optimal configuration of each group in the related TU. Finally, the proposed algorithm is applied to several realistic case studies with the aim of testing and analysing its effectiveness in producing stable and compact TU loading configurations in a short computation time, despite the high computational complexity of the multi-drop CLP. © 2021 IEEE.},
    author_keywords={Container loading problem; Logistics; MILP; Multi-drop; Optimization},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Cavone, G., Epicoco, N., Carli, R., Del Zotti, A., Paulo Ribeiro Pereira, J. & Dotoli, M. (2021) Parcel delivery with drones: Multi-criteria analysis of trendy system architectures IN 2021 29th Mediterranean Conference on Control and Automation, MED 2021., 693-698. doi:10.1109/MED51440.2021.9480332
    [BibTeX] [Abstract] [Download PDF]
    New technologies, such as Unmanned Aerial Vehicles (UAVs), are transforming facilities and vehicles into intelligent systems that will significantly modify logistic deliveries in any organization. With the appearance of automated vehicles, drones offer multiple new technological solutions that might trigger different delivery networks or boost new delivery services. Differently from the related works, where a single specific delivery system model is typically addressed, this paper deals with the use of UAVs for logistic deliveries focusing on a multi-criteria analysis of trendy drone-based system architectures. In particular, using the cross-efficiency Data Envelopment Analysis approach, a comparative analysis among three different delivery systems is performed: the classic system based on trucks only, the drone-only system using a fleet of drones, and the hybrid truck and drone system combining trucks and drones. The proposed technique constitutes an effective decision-making tool aimed at helping delivery companies in selecting the optimal delivery system architecture according to their specific needs. The effectiveness of the proposed methodology is shown by a simulation analysis based on a realistic data case study that pertains to the main logistic service providers. © 2021 IEEE.
    @CONFERENCE{Cavone2021693,
    author={Cavone, G. and Epicoco, N. and Carli, R. and Del Zotti, A. and Paulo Ribeiro Pereira, J. and Dotoli, M.},
    title={Parcel delivery with drones: Multi-criteria analysis of trendy system architectures},
    journal={2021 29th Mediterranean Conference on Control and Automation, MED 2021},
    year={2021},
    pages={693-698},
    doi={10.1109/MED51440.2021.9480332},
    art_number={9480332},
    note={cited By 0},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113688309&doi=10.1109%2fMED51440.2021.9480332&partnerID=40&md5=b1147e916948e156664d7ff61520c1dd},
    affiliation={Polytechnic of Bari, Department of Electrical and Information Engineering, Italy; University of l'Aquila, Center of Excellence DEWS (Design Methodologies for Embedded Controllers, Wireless Interconnect and System-on-Chip), Department of Information Engineering, Computer Science and Mathematics (DISIM), L'Aquila, Italy; University of Lisbon, Centre for Management Studies of Superior Technical Institute, Portugal; Polytechnic Institute of Bragança, School of Technology and Management, Portugal},
    abstract={New technologies, such as Unmanned Aerial Vehicles (UAVs), are transforming facilities and vehicles into intelligent systems that will significantly modify logistic deliveries in any organization. With the appearance of automated vehicles, drones offer multiple new technological solutions that might trigger different delivery networks or boost new delivery services. Differently from the related works, where a single specific delivery system model is typically addressed, this paper deals with the use of UAVs for logistic deliveries focusing on a multi-criteria analysis of trendy drone-based system architectures. In particular, using the cross-efficiency Data Envelopment Analysis approach, a comparative analysis among three different delivery systems is performed: the classic system based on trucks only, the drone-only system using a fleet of drones, and the hybrid truck and drone system combining trucks and drones. The proposed technique constitutes an effective decision-making tool aimed at helping delivery companies in selecting the optimal delivery system architecture according to their specific needs. The effectiveness of the proposed methodology is shown by a simulation analysis based on a realistic data case study that pertains to the main logistic service providers. © 2021 IEEE.},
    author_keywords={Data Envelopment Analysis; Drones; Multi-criteria decision making; Parcel delivery; UAVs},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Scarabaggio, P., Carli, R., Cavone, G., Epicoco, N. & Dotoli, M. (2021) Modeling, estimation, and analysis of COVID-19 secondary waves: The Case of the Italian Country IN 2021 29th Mediterranean Conference on Control and Automation, MED 2021., 794-800. doi:10.1109/MED51440.2021.9480319
    [BibTeX] [Abstract] [Download PDF]
    The recent trends of the COVID-19 research have been devoted to disease transmission modeling, with the aim of investigating the effects of different mitigation strategies mainly through scenario-based simulations. In this context we propose a novel non-linear time-varying model that effectively supports policy-makers in predicting and analyzing the dynamics of COVID-19 secondary waves. Specifically, this paper proposes an accurate SIRUCQTHE epidemiological model to get reliable predictions on the pandemic dynamics. Differently from the related literature, in the fitting phase, we make use of the google mobility reports to identify and predict the evolution of the infection rate. The effectiveness of the presented method is tested on the network of Italian regions. First, we describe the Italian epidemiological scenario in the COVID-19 second wave of contagions, showing the raw data available for the Italian scenario and discussing the main assumptions on the system parameters. Then, we present the different steps of the procedure used for the dynamical fitting of the SIRUCQTHE model. Finally, we compare the estimation results with the real data on the COVID-19 secondary waves in Italy. Provided the availability of reliable data to calibrate the model in heterogeneous scenarios, the proposed approach can be easily extended to cope with other scenarios. © 2021 IEEE.
    @CONFERENCE{Scarabaggio2021794,
    author={Scarabaggio, P. and Carli, R. and Cavone, G. and Epicoco, N. and Dotoli, M.},
    title={Modeling, estimation, and analysis of COVID-19 secondary waves: The Case of the Italian Country},
    journal={2021 29th Mediterranean Conference on Control and Automation, MED 2021},
    year={2021},
    pages={794-800},
    doi={10.1109/MED51440.2021.9480319},
    art_number={9480319},
    note={cited By 0},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85113602900&doi=10.1109%2fMED51440.2021.9480319&partnerID=40&md5=980feaa724975719c46c214ad1dcbfed},
    affiliation={Polytechnic of Bari, Department of Electrical and Information Engineering, Italy; University of l'Aquila, Computer Science and Mathematics (DISIM), Center of Excellence DEWS (Design Methodologies for Embedded Controllers, Wireless Interconnect and System-on-chip), Department of Information Engineering, L'Aquila, Italy},
    abstract={The recent trends of the COVID-19 research have been devoted to disease transmission modeling, with the aim of investigating the effects of different mitigation strategies mainly through scenario-based simulations. In this context we propose a novel non-linear time-varying model that effectively supports policy-makers in predicting and analyzing the dynamics of COVID-19 secondary waves. Specifically, this paper proposes an accurate SIRUCQTHE epidemiological model to get reliable predictions on the pandemic dynamics. Differently from the related literature, in the fitting phase, we make use of the google mobility reports to identify and predict the evolution of the infection rate. The effectiveness of the presented method is tested on the network of Italian regions. First, we describe the Italian epidemiological scenario in the COVID-19 second wave of contagions, showing the raw data available for the Italian scenario and discussing the main assumptions on the system parameters. Then, we present the different steps of the procedure used for the dynamical fitting of the SIRUCQTHE model. Finally, we compare the estimation results with the real data on the COVID-19 secondary waves in Italy. Provided the availability of reliable data to calibrate the model in heterogeneous scenarios, the proposed approach can be easily extended to cope with other scenarios. © 2021 IEEE.},
    author_keywords={COVID-19; Dynamical fitting; Estimation; Identification; Pandemic modeling},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Proia, S., Carli, R., Cavone, G. & Dotoli, M. (2021) Control Techniques for Safe, Ergonomic, and Efficient Human-Robot Collaboration in the Digital Industry: A Survey. IN IEEE Transactions on Automation Science and Engineering, .. doi:10.1109/TASE.2021.3131011
    [BibTeX] [Abstract] [Download PDF]
    The fourth industrial revolution, also known as Industry 4.0, is reshaping the way individuals live and work while providing a substantial influence on the manufacturing scenario. The key enabling technology that has made Industry 4.0 a concrete reality is without doubt collaborative robotics, which is also evolving as a fundamental pillar of the next revolution, the so-called Industry 5.0. The improvement of employees’ safety and well-being, together with the increase of profitability and productivity, are indeed the main goals of human-robot collaboration (HRC) in the industrial setting. The robotic controller design and the analysis of existing decision and control techniques are crucially needed to develop innovative models and state-of-the-art methodologies for a safe, ergonomic, and efficient HRC. To this aim, this paper presents an accurate review of the most recent and relevant contributions to the related literature, focusing on the control perspective. All the surveyed works are carefully selected and categorized by target (i.e., safety, ergonomics, and efficiency), and then by problem and type of control, in presence or absence of optimization. Finally, the discussion of the achieved results and the analysis of the emerging challenges in this research field are reported, highlighting the identified gaps and the promising future developments in the context of the digital evolution. Author
    @ARTICLE{Proia2021,
    author={Proia, S. and Carli, R. and Cavone, G. and Dotoli, M.},
    title={Control Techniques for Safe, Ergonomic, and Efficient Human-Robot Collaboration in the Digital Industry: A Survey},
    journal={IEEE Transactions on Automation Science and Engineering},
    year={2021},
    doi={10.1109/TASE.2021.3131011},
    note={cited By 0},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121365732&doi=10.1109%2fTASE.2021.3131011&partnerID=40&md5=eae23f3b0dbfaf83b934942c9c0736ff},
    affiliation={Department of Electrical and Information Engineering, Polytechnic of Bari, 70126 Bari, Italy.; Department of Electrical and Information Engineering, Polytechnic of Bari, 70126 Bari, Italy (e-mail: ti.ab1651260140ilop@1651260140enova1651260140c.ana1651260140izarg1651260140)},
    abstract={The fourth industrial revolution, also known as Industry 4.0, is reshaping the way individuals live and work while providing a substantial influence on the manufacturing scenario. The key enabling technology that has made Industry 4.0 a concrete reality is without doubt collaborative robotics, which is also evolving as a fundamental pillar of the next revolution, the so-called Industry 5.0. The improvement of employees' safety and well-being, together with the increase of profitability and productivity, are indeed the main goals of human-robot collaboration (HRC) in the industrial setting. The robotic controller design and the analysis of existing decision and control techniques are crucially needed to develop innovative models and state-of-the-art methodologies for a safe, ergonomic, and efficient HRC. To this aim, this paper presents an accurate review of the most recent and relevant contributions to the related literature, focusing on the control perspective. All the surveyed works are carefully selected and categorized by target (i.e., safety, ergonomics, and efficiency), and then by problem and type of control, in presence or absence of optimization. Finally, the discussion of the achieved results and the analysis of the emerging challenges in this research field are reported, highlighting the identified gaps and the promising future developments in the context of the digital evolution. Author},
    author_keywords={cobots; Collaboration; collaborative robotics; efficiency.; Ergonomics; ergonomics; HRC control systems; human-robot collaboration (HRC); industrial automation; Industry 4.0; Manufacturing; Robots; safety; Safety; Service robots; Task analysis},
    document_type={Article},
    source={Scopus},
    }
  • Scarabaggio, P., Carli, R., Cavone, G., Epicoco, N. & Dotoli, M. (2021) Nonpharmaceutical Stochastic Optimal Control Strategies to Mitigate the COVID-19 Spread. IN IEEE Transactions on Automation Science and Engineering, .. doi:10.1109/TASE.2021.3111338
    [BibTeX] [Abstract] [Download PDF]
    This article proposes a stochastic nonlinear model predictive controller to support policymakers in determining robust optimal nonpharmaceutical strategies to tackle the COVID-19 pandemic waves. First, a time-varying SIRCQTHE epidemiological model is defined to get predictions on the pandemic dynamics. A stochastic model predictive control problem is then formulated to select the necessary control actions (i.e., restrictions on the mobility for different socioeconomic categories) to minimize the socioeconomic costs. In particular, considering the uncertainty characterizing this decision-making process, we ensure that the capacity of the healthcare system is not violated in accordance with a chance constraint approach. The effectiveness of the presented method in properly supporting the definition of diversified nonpharmaceutical strategies for tackling the COVID-19 spread is tested on the network of Italian regions using real data. The proposed approach can be easily extended to cope with other countries’ characteristics and different levels of the spatial scale. IEEE
    @ARTICLE{Scarabaggio2021,
    author={Scarabaggio, P. and Carli, R. and Cavone, G. and Epicoco, N. and Dotoli, M.},
    title={Nonpharmaceutical Stochastic Optimal Control Strategies to Mitigate the COVID-19 Spread},
    journal={IEEE Transactions on Automation Science and Engineering},
    year={2021},
    doi={10.1109/TASE.2021.3111338},
    note={cited By 2},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85115686920&doi=10.1109%2fTASE.2021.3111338&partnerID=40&md5=ef02a3b0f8f2eeff0ec2f0e0a861a0b8},
    affiliation={Department of Electrical and Information Engineering, Polytechnic of Bari, 70126 Bari, Italy (e-mail: ti.ab1651260140ilop@1651260140oigga1651260140barac1651260140s.olo1651260140ap1651260140); Department of Electrical and Information Engineering, Polytechnic of Bari, 70126 Bari, Italy.; Department of Information Engineering, Computer Science and Mathematics (DISIM), University of L'Aquila, 67100 L'Aquila, Italy, and also with the Center of Excellence DEWS (Design methodologies for Embedded controllers, Wireless interconnect and System-on-chip), University of L'Aquila, 67100 L'Aquila, Italy.},
    abstract={This article proposes a stochastic nonlinear model predictive controller to support policymakers in determining robust optimal nonpharmaceutical strategies to tackle the COVID-19 pandemic waves. First, a time-varying SIRCQTHE epidemiological model is defined to get predictions on the pandemic dynamics. A stochastic model predictive control problem is then formulated to select the necessary control actions (i.e., restrictions on the mobility for different socioeconomic categories) to minimize the socioeconomic costs. In particular, considering the uncertainty characterizing this decision-making process, we ensure that the capacity of the healthcare system is not violated in accordance with a chance constraint approach. The effectiveness of the presented method in properly supporting the definition of diversified nonpharmaceutical strategies for tackling the COVID-19 spread is tested on the network of Italian regions using real data. The proposed approach can be easily extended to cope with other countries' characteristics and different levels of the spatial scale. IEEE},
    author_keywords={COVID-19; COVID-19; Data models; epidemic control; Medical services; mitigation strategies; pandemic modeling; Pandemics; Predictive models; stochastic model predictive control (MPC).; Stochastic processes; Uncertainty},
    document_type={Article},
    source={Scopus},
    }

2020

  • Cavone, G., Epicoco, N. & Dotoli, M. (2020) Process re-engineering based on colored petri nets: The case of an Italian textile company IN 2020 28th Mediterranean Conference on Control and Automation, MED 2020., 856-861. doi:10.1109/MED48518.2020.9182937
    [BibTeX] [Abstract] [Download PDF]
    Business process re-engineering is crucial for manufacturing companies to improve their productivity and efficiency. The identification of the main criticalities affecting the production processes and the implementation of effective re-engineering solutions can significantly reduce the company losses. However, such actions can be unsuccessful if suitable preliminary investigations on the effectiveness of the solutions are not performed. This paper proposes an integrated process re-engineering technique that allows to: identify workflows via the Unified Modeling Language; model and simulate the business process via Colored Petri Nets (CPNs); detect bottlenecks and waste sources through the Value Stream Mapping tool; rank the impact of the detected criticalities via a mathematical formulation of the Genba-Shikumi lean philosophy; simulate the re-engineering actions and evaluate their effectiveness using the CPN model. The aim is to offer an intuitive tool for strategic decision making, deployable at a managerial level in a digital twin approach. The proposed technique is tested on a textile company located in Southern Italy, showing its effectiveness in removing inefficiencies and ensuring the continuous improvement of the production process. © 2020 IEEE.
    @CONFERENCE{Cavone2020856,
    author={Cavone, G. and Epicoco, N. and Dotoli, M.},
    title={Process re-engineering based on colored petri nets: The case of an Italian textile company},
    journal={2020 28th Mediterranean Conference on Control and Automation, MED 2020},
    year={2020},
    pages={856-861},
    doi={10.1109/MED48518.2020.9182937},
    art_number={9182937},
    note={cited By 0},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092143469&doi=10.1109%2fMED48518.2020.9182937&partnerID=40&md5=8c365f0bdb732312eac56e75db5e46d8},
    affiliation={Polytechnic of Bari, Dept. of Elettrical and Information Engineering, Bari, Italy; Center of Excellence DEWS, University of l'Aquila, Dept. of Information Engineering, Computer Science and Mathematics, L'Aquila, Italy},
    abstract={Business process re-engineering is crucial for manufacturing companies to improve their productivity and efficiency. The identification of the main criticalities affecting the production processes and the implementation of effective re-engineering solutions can significantly reduce the company losses. However, such actions can be unsuccessful if suitable preliminary investigations on the effectiveness of the solutions are not performed. This paper proposes an integrated process re-engineering technique that allows to: identify workflows via the Unified Modeling Language; model and simulate the business process via Colored Petri Nets (CPNs); detect bottlenecks and waste sources through the Value Stream Mapping tool; rank the impact of the detected criticalities via a mathematical formulation of the Genba-Shikumi lean philosophy; simulate the re-engineering actions and evaluate their effectiveness using the CPN model. The aim is to offer an intuitive tool for strategic decision making, deployable at a managerial level in a digital twin approach. The proposed technique is tested on a textile company located in Southern Italy, showing its effectiveness in removing inefficiencies and ensuring the continuous improvement of the production process. © 2020 IEEE.},
    author_keywords={Colored Petri Nets; Genba-Shikumi; Manufacturing; Process Re-engineering; Unified Modeling Language},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Scarabaggio, P., Carli, R., Cavone, G. & Dotoli, M. (2020) Smart control strategies for primary frequency regulation through electric vehicles: A battery degradation perspective. IN Energies, 13.. doi:10.3390/en13174586
    [BibTeX] [Abstract] [Download PDF]
    Nowadays, due to the decreasing use of traditional generators in favor of renewable energy sources, power grids are facing a reduction of system inertia and primary frequency regulation capability. Such an issue is exacerbated by the continuously increasing number of electric vehicles (EVs), which results in enforcing novel approaches in the grid operations management. However, from being an issue, the increase of EVs may turn to be a solution to several power system challenges. In this context, a crucial role is played by the so-called vehicle-to-grid (V2G) mode of operation, which has the potential to provide ancillary services to the power grid, such as peak clipping, load shifting, and frequency regulation. More in detail, EVs have recently started to be effectively used for one of the most traditional frequency regulation approaches: the so-called frequency droop control (FDC). This is a primary frequency regulation, currently obtained by adjusting the active power of generators in the main grid. Because to the decommissioning of traditional power plants, EVs are thus recognized as particularly valuable solutions since they can respond to frequency deviation signals by charging or discharging their batteries. Against this background, we address frequency regulation of a power grid model including loads, traditional generators, and several EVs. The latter independently participate in the grid optimization process providing the grid with ancillary services, namely the FDC. We propose two novel control strategies for the optimal control of the batteries of EVs during the frequency regulation service. On the one hand, the control strategies ensure re-balancing the power and stabilizing the frequency of the main grid. On the other hand, the approaches are able to satisfy different types of needs of EVs during the charging process. Differently from the related literature, where the EVs perspective is generally oriented to achieve the optimal charge level, the proposed approaches aim at minimizing the degradation of battery devices. Finally, the proposed strategies are compared with other state-of-the-art V2G control approaches. The results of numerical experiments using a realistic power grid model show the effectiveness of the proposed strategies under the actual operating conditions. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
    @ARTICLE{Scarabaggio2020,
    author={Scarabaggio, P. and Carli, R. and Cavone, G. and Dotoli, M.},
    title={Smart control strategies for primary frequency regulation through electric vehicles: A battery degradation perspective},
    journal={Energies},
    year={2020},
    volume={13},
    number={17},
    doi={10.3390/en13174586},
    art_number={4586},
    note={cited By 7},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85090919511&doi=10.3390%2fen13174586&partnerID=40&md5=d7f07f0a819d149b5f1c143b707e731d},
    affiliation={Department of Electrical and Information Engineering, Polytechnic of Bari, Via Orabona 4, Bari, 70125, Italy},
    abstract={Nowadays, due to the decreasing use of traditional generators in favor of renewable energy sources, power grids are facing a reduction of system inertia and primary frequency regulation capability. Such an issue is exacerbated by the continuously increasing number of electric vehicles (EVs), which results in enforcing novel approaches in the grid operations management. However, from being an issue, the increase of EVs may turn to be a solution to several power system challenges. In this context, a crucial role is played by the so-called vehicle-to-grid (V2G) mode of operation, which has the potential to provide ancillary services to the power grid, such as peak clipping, load shifting, and frequency regulation. More in detail, EVs have recently started to be effectively used for one of the most traditional frequency regulation approaches: the so-called frequency droop control (FDC). This is a primary frequency regulation, currently obtained by adjusting the active power of generators in the main grid. Because to the decommissioning of traditional power plants, EVs are thus recognized as particularly valuable solutions since they can respond to frequency deviation signals by charging or discharging their batteries. Against this background, we address frequency regulation of a power grid model including loads, traditional generators, and several EVs. The latter independently participate in the grid optimization process providing the grid with ancillary services, namely the FDC. We propose two novel control strategies for the optimal control of the batteries of EVs during the frequency regulation service. On the one hand, the control strategies ensure re-balancing the power and stabilizing the frequency of the main grid. On the other hand, the approaches are able to satisfy different types of needs of EVs during the charging process. Differently from the related literature, where the EVs perspective is generally oriented to achieve the optimal charge level, the proposed approaches aim at minimizing the degradation of battery devices. Finally, the proposed strategies are compared with other state-of-the-art V2G control approaches. The results of numerical experiments using a realistic power grid model show the effectiveness of the proposed strategies under the actual operating conditions. © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).},
    author_keywords={Electric vehicle batteries (EVBs); Electric vehicles (EVs); Frequency droop control (FDC); Vehicle-to-grid (V2G)},
    document_type={Article},
    source={Scopus},
    }
  • Carli, R., Cavone, G., Pippia, T., Schutter, B. D. & Dotoli, M. (2020) A Robust MPC Energy Scheduling Strategy for Multi-Carrier Microgrids IN IEEE International Conference on Automation Science and Engineering., 152-158. doi:10.1109/CASE48305.2020.9216875
    [BibTeX] [Abstract] [Download PDF]
    We present a Robust Model Predictive Control (RMPC) approach for multi-carrier microgrids, i.e., microgrids based on gas and electricity. The microgrid that we consider includes thermal loads, electrical loads, renewable energy sources, energy storage systems, heat pumps, and combined heat and power plants. Moreover, the system under control is affected by several external disturbances, e.g., uncertainty in renewable energy generation, electrical and thermal demand. The goal of the controller is to minimize the overall economical cost and the energy exchange with the main grid, while guaranteeing comfort. Whereas several RMPC methods have been developed for electrical or thermal microgrids, little or no attention has been devoted to robust control of multi-carrier microgrids. Therefore, we consider a novel RMPC algorithm that can improve the performance with respect to classical deterministic Model Predictive Control (Det-MPC) controllers in the context of multi-carrier microgrids. The RMPC method relies on the box-uncertainty-set robust optimization, where uncertain parameters are assumed to take their values from different intervals independently. The RMPC approach is able to successfully satisfy the constraints even in the presence of the mentioned disturbances. Simulations of a realistic residential case study show the benefits of the proposed approach with respect to Det-MPC controllers. © 2020 IEEE.
    @CONFERENCE{Carli2020152,
    author={Carli, R. and Cavone, G. and Pippia, T. and Schutter, B.D. and Dotoli, M.},
    title={A Robust MPC Energy Scheduling Strategy for Multi-Carrier Microgrids},
    journal={IEEE International Conference on Automation Science and Engineering},
    year={2020},
    volume={2020-August},
    pages={152-158},
    doi={10.1109/CASE48305.2020.9216875},
    art_number={9216875},
    note={cited By 4},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85094114652&doi=10.1109%2fCASE48305.2020.9216875&partnerID=40&md5=92ef257518791ef22242636d1c989285},
    affiliation={Polytechnic of Bari, Dept. of Electrical and Information Engineering, Bari, Italy; Delft University of Technology, Delft Center for Systems and Control, Delft, Netherlands},
    abstract={We present a Robust Model Predictive Control (RMPC) approach for multi-carrier microgrids, i.e., microgrids based on gas and electricity. The microgrid that we consider includes thermal loads, electrical loads, renewable energy sources, energy storage systems, heat pumps, and combined heat and power plants. Moreover, the system under control is affected by several external disturbances, e.g., uncertainty in renewable energy generation, electrical and thermal demand. The goal of the controller is to minimize the overall economical cost and the energy exchange with the main grid, while guaranteeing comfort. Whereas several RMPC methods have been developed for electrical or thermal microgrids, little or no attention has been devoted to robust control of multi-carrier microgrids. Therefore, we consider a novel RMPC algorithm that can improve the performance with respect to classical deterministic Model Predictive Control (Det-MPC) controllers in the context of multi-carrier microgrids. The RMPC method relies on the box-uncertainty-set robust optimization, where uncertain parameters are assumed to take their values from different intervals independently. The RMPC approach is able to successfully satisfy the constraints even in the presence of the mentioned disturbances. Simulations of a realistic residential case study show the benefits of the proposed approach with respect to Det-MPC controllers. © 2020 IEEE.},
    author_keywords={Energy and Environment-Aware Automation; Microgrid; Optimization and Optimal Control; Robust Model Predictive Control; Set-based Uncertainty},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Cavone, G., Dotoli, M., Epicoco, N., Morelli, D. & Seatzu, C. (2020) Design of Modern Supply Chain Networks Using Fuzzy Bargaining Game and Data Envelopment Analysis. IN IEEE Transactions on Automation Science and Engineering, 17.1221-1236. doi:10.1109/TASE.2020.2977452
    [BibTeX] [Abstract] [Download PDF]
    This article proposes a novel methodology for multistage, multiproduct, multi-item, and closed-loop Supply Chain Network (SCN) design under uncertainty. The method considers that multiple products are manufactured by the SCN, each composed by multiple items, and that some of the sold products may require repair, refurbishing, or remanufacturing activities. We solve the two main decisions that take place in the medium-/short-term planning horizon, namely partners’ selection and allocation of the received orders among them. The partners’ selection problem is solved by a cross-efficiency fuzzy Data Envelopment Analysis technique, which allows evaluating the efficiency of each SCN member and ranking them against multiple conflicting objectives under uncertain data on their performance. Then, according to the estimated customers’ demand, the order allocation problem is solved by a fuzzy bargaining game problem, where each SCN actor behaves to simultaneously maximize both its own profit and the service level of the overall SCN in terms of efficiency, costs, and lead time. An illustrative example from the literature is finally presented. Note to Practitioners-We present a decision tool to address the optimal design, performance evaluation, and continuous improvement of modern cooperative SCNs. We propose an effective method to jointly solve the members’ selection and the orders’ allocation, considering the complex structure of modern SCNs, the multiobjective nature of the problems, and the uncertainty characterizing economic markets. Competition within SCNs stages and cooperation along the chain are considered, with the aim to improve both financial and environmental sustainability, while ensuring the highest service levels to customers. © 2004-2012 IEEE.
    @ARTICLE{Cavone20201221,
    author={Cavone, G. and Dotoli, M. and Epicoco, N. and Morelli, D. and Seatzu, C.},
    title={Design of Modern Supply Chain Networks Using Fuzzy Bargaining Game and Data Envelopment Analysis},
    journal={IEEE Transactions on Automation Science and Engineering},
    year={2020},
    volume={17},
    number={3},
    pages={1221-1236},
    doi={10.1109/TASE.2020.2977452},
    art_number={9040428},
    note={cited By 5},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85087545403&doi=10.1109%2fTASE.2020.2977452&partnerID=40&md5=36c9b0b7af0bb44437e25bed1ffbd281},
    affiliation={Department of Electrical and Information Engineering, Polytechnic University of Bari, Bari, 70126, Italy; Department of Information Engineering Computer Science and Mathematics, University of l'Aquila, L'Aquila, 67100, Italy; Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, 94720, Italy},
    abstract={This article proposes a novel methodology for multistage, multiproduct, multi-item, and closed-loop Supply Chain Network (SCN) design under uncertainty. The method considers that multiple products are manufactured by the SCN, each composed by multiple items, and that some of the sold products may require repair, refurbishing, or remanufacturing activities. We solve the two main decisions that take place in the medium-/short-term planning horizon, namely partners' selection and allocation of the received orders among them. The partners' selection problem is solved by a cross-efficiency fuzzy Data Envelopment Analysis technique, which allows evaluating the efficiency of each SCN member and ranking them against multiple conflicting objectives under uncertain data on their performance. Then, according to the estimated customers' demand, the order allocation problem is solved by a fuzzy bargaining game problem, where each SCN actor behaves to simultaneously maximize both its own profit and the service level of the overall SCN in terms of efficiency, costs, and lead time. An illustrative example from the literature is finally presented. Note to Practitioners-We present a decision tool to address the optimal design, performance evaluation, and continuous improvement of modern cooperative SCNs. We propose an effective method to jointly solve the members' selection and the orders' allocation, considering the complex structure of modern SCNs, the multiobjective nature of the problems, and the uncertainty characterizing economic markets. Competition within SCNs stages and cooperation along the chain are considered, with the aim to improve both financial and environmental sustainability, while ensuring the highest service levels to customers. © 2004-2012 IEEE.},
    author_keywords={Bargaining game; fuzzy set theory; order allocation; supplier selection; Supply Chain Network Design (SCND)},
    document_type={Article},
    source={Scopus},
    }
  • Cavone, G., Montaruli, V., Van Den Boom, T. J. J. & Dotoli, M. (2020) Demand-Oriented Rescheduling of Railway Traffic in Case of Delays IN 7th International Conference on Control, Decision and Information Technologies, CoDIT 2020., 1040-1045. doi:10.1109/CoDIT49905.2020.9263874
    [BibTeX] [Abstract] [Download PDF]
    The railway sector is currently experiencing a rapid evolution from fully manual towards automatic rail traffic control systems, due to the growth of transport demand and networks complexity. One of the main issues is to automatically and effectively reschedule the railway traffic in case of unexpected events, thus avoiding dramatic drops in the system performance. In the literature, the majority of contributions aims at automatically minimizing the train delays or optimizing the railway system performance (e.g., energy consumption). However, such strategies are not always able to ensure satisfaction of passengers that in many cases experience the side-effects of the rescheduling actions (e.g., cancellation of train runs, cancellation of coincidences, rerouting of trains, etc.). In this paper, we propose a demandoriented train rescheduling automatic technique that minimizes simultaneously the train delays and the discomfort perceived by passengers. When an unexpected event occurs, the rescheduling problem is set, based on the current state and nominal timetable of the system and its passengers flows. Hence, the problem is solved providing the control actions necessary to minimize both the delays and number of passengers subject to severe side-effects. The rescheduling is here formulated as a mixed integer linear programming problem, where the operating rules of the railway network are represented by linear equality and inequality constraints, while the objective is a linear function to be minimized. The possible control actions consist in re-timing the rail traffic and modifying the connections among lines. The proposed technique is preliminarily evaluated on a test case and a discussion is provided on the outcomes. © 2020 IEEE.
    @CONFERENCE{Cavone20201040,
    author={Cavone, G. and Montaruli, V. and Van Den Boom, T.J.J. and Dotoli, M.},
    title={Demand-Oriented Rescheduling of Railway Traffic in Case of Delays},
    journal={7th International Conference on Control, Decision and Information Technologies, CoDIT 2020},
    year={2020},
    pages={1040-1045},
    doi={10.1109/CoDIT49905.2020.9263874},
    art_number={9263874},
    note={cited By 1},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85098278130&doi=10.1109%2fCoDIT49905.2020.9263874&partnerID=40&md5=77e95b60271d0546bb0ca7e53e9fbfc4},
    affiliation={Polytechnic of Bari, Dept. of Elettrical and Information Engineering, Bari, Italy; Delft University of Technology, Delft Canter for Systems and Control, Netherlands},
    abstract={The railway sector is currently experiencing a rapid evolution from fully manual towards automatic rail traffic control systems, due to the growth of transport demand and networks complexity. One of the main issues is to automatically and effectively reschedule the railway traffic in case of unexpected events, thus avoiding dramatic drops in the system performance. In the literature, the majority of contributions aims at automatically minimizing the train delays or optimizing the railway system performance (e.g., energy consumption). However, such strategies are not always able to ensure satisfaction of passengers that in many cases experience the side-effects of the rescheduling actions (e.g., cancellation of train runs, cancellation of coincidences, rerouting of trains, etc.). In this paper, we propose a demandoriented train rescheduling automatic technique that minimizes simultaneously the train delays and the discomfort perceived by passengers. When an unexpected event occurs, the rescheduling problem is set, based on the current state and nominal timetable of the system and its passengers flows. Hence, the problem is solved providing the control actions necessary to minimize both the delays and number of passengers subject to severe side-effects. The rescheduling is here formulated as a mixed integer linear programming problem, where the operating rules of the railway network are represented by linear equality and inequality constraints, while the objective is a linear function to be minimized. The possible control actions consist in re-timing the rail traffic and modifying the connections among lines. The proposed technique is preliminarily evaluated on a test case and a discussion is provided on the outcomes. © 2020 IEEE.},
    author_keywords={demand-oriented rescheduling; passengers satisfaction; Railways},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R., Cavone, G., Othman, S. B. & Dotoli, M. (2020) IoT based architecture for model predictive control of HVAC systems in smart buildings. IN Sensors (Switzerland), 20.. doi:10.3390/s20030781
    [BibTeX] [Abstract] [Download PDF]
    The efficient management of Heating Ventilation and Air Conditioning (HVAC) systems in smart buildings is one of the main applications of the Internet of Things (IoT) paradigm. In this paper we propose an IoT based architecture for the implementation of Model Predictive Control (MPC) of HVAC systems in real environments. The considered MPC algorithm optimizes on line, in a closed-loop control fashion, both the indoor thermal comfort and the related energy consumption for a single zone environment. Thanks to the proposed IoT based architecture, the sensing, control, and actuating subsystems are all connected to the Internet, and a remote interface with the HVAC control system is guaranteed to end-users. In particular, sensors and actuators communicate with a remote database server and a control unit, which provides the control actions to be actuated in the HVAC system; users can set remotely the control mode and related set-points of the system; while comfort and environmental indices are transferred via the Internet and displayed on the end-users’ interface. The proposed IoT based control architecture is implemented and tested in a campus building at the Polytechnic of Bari (Italy) in a proof of concept perspective. The effectiveness of the proposed control algorithm is assessed in the real environment evaluating both the thermal comfort results and the energy savings with respect to a classical thermostat regulation approach. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.
    @ARTICLE{Carli2020,
    author={Carli, R. and Cavone, G. and Othman, S.B. and Dotoli, M.},
    title={IoT based architecture for model predictive control of HVAC systems in smart buildings},
    journal={Sensors (Switzerland)},
    year={2020},
    volume={20},
    number={3},
    doi={10.3390/s20030781},
    art_number={781},
    note={cited By 34},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85079071499&doi=10.3390%2fs20030781&partnerID=40&md5=b68287ad61a3091865fc7546425dce95},
    affiliation={Department of Electrical and Information Engineering, Polytechnic of Bari, Via Orabona 4, Bari, 70125, Italy; CRIStAL Laboratory UML 9189, Ecole-Central of Lille, Villeneuve d’Ascq, 59655, France},
    abstract={The efficient management of Heating Ventilation and Air Conditioning (HVAC) systems in smart buildings is one of the main applications of the Internet of Things (IoT) paradigm. In this paper we propose an IoT based architecture for the implementation of Model Predictive Control (MPC) of HVAC systems in real environments. The considered MPC algorithm optimizes on line, in a closed-loop control fashion, both the indoor thermal comfort and the related energy consumption for a single zone environment. Thanks to the proposed IoT based architecture, the sensing, control, and actuating subsystems are all connected to the Internet, and a remote interface with the HVAC control system is guaranteed to end-users. In particular, sensors and actuators communicate with a remote database server and a control unit, which provides the control actions to be actuated in the HVAC system; users can set remotely the control mode and related set-points of the system; while comfort and environmental indices are transferred via the Internet and displayed on the end-users’ interface. The proposed IoT based control architecture is implemented and tested in a campus building at the Polytechnic of Bari (Italy) in a proof of concept perspective. The effectiveness of the proposed control algorithm is assessed in the real environment evaluating both the thermal comfort results and the energy savings with respect to a classical thermostat regulation approach. © 2020 by the authors. Licensee MDPI, Basel, Switzerland.},
    author_keywords={Heating ventilation and air conditioning system; Internet of things; Model predictive control; Predicted mean vote; Smart buildings},
    document_type={Article},
    source={Scopus},
    }
  • Carli, R., Cavone, G., Epicoco, N., Scarabaggio, P. & Dotoli, M. (2020) Model predictive control to mitigate the COVID-19 outbreak in a multi-region scenario. IN Annual Reviews in Control, 50.373-393. doi:10.1016/j.arcontrol.2020.09.005
    [BibTeX] [Abstract] [Download PDF]
    The COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. In multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible onset of further epidemic waves, while efficiently returning the economic activities to their standard level of intensity. Differently from the related literature, where modeling and controlling the pandemic contagion is typically addressed on a national basis, this paper proposes an optimal control approach that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario. Based on the joint use of a non-linear Model Predictive Control scheme and a modified Susceptible-Infected-Recovered (SIR)-based epidemiological model, the approach is aimed at minimizing the cost of the so-called non-pharmaceutical interventions (that is, mitigation strategies), while ensuring that the capacity of the network of regional healthcare systems is not violated. In addition, the proposed approach supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model, thus both respecting the specific regional health systems characteristics and improving the system-wide performance by avoiding uncoordinated actions of the regions. The methodology is tested on the COVID-19 outbreak data related to the network of Italian regions, showing its effectiveness in properly supporting the definition of effective regional strategies for managing the COVID-19 diffusion. © 2020 Elsevier Ltd
    @ARTICLE{Carli2020373,
    author={Carli, R. and Cavone, G. and Epicoco, N. and Scarabaggio, P. and Dotoli, M.},
    title={Model predictive control to mitigate the COVID-19 outbreak in a multi-region scenario},
    journal={Annual Reviews in Control},
    year={2020},
    volume={50},
    pages={373-393},
    doi={10.1016/j.arcontrol.2020.09.005},
    note={cited By 29},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85097753585&doi=10.1016%2fj.arcontrol.2020.09.005&partnerID=40&md5=adce49e71a999948867e93de3ae2e142},
    affiliation={Dept. of Electrical and Information Engineering, Polytechnic of Bari via Orabona 4, Bari, 70125, Italy; Center of Excellence DEWS, Dept. of Information Engineering, Computer Science and Mathematics, University of L'Aquila via Vetoio (Coppito 1), L'Aquila, 67100, Italy},
    abstract={The COVID-19 outbreak is deeply influencing the global social and economic framework, due to restrictive measures adopted worldwide by governments to counteract the pandemic contagion. In multi-region areas such as Italy, where the contagion peak has been reached, it is crucial to find targeted and coordinated optimal exit and restarting strategies on a regional basis to effectively cope with possible onset of further epidemic waves, while efficiently returning the economic activities to their standard level of intensity. Differently from the related literature, where modeling and controlling the pandemic contagion is typically addressed on a national basis, this paper proposes an optimal control approach that supports governments in defining the most effective strategies to be adopted during post-lockdown mitigation phases in a multi-region scenario. Based on the joint use of a non-linear Model Predictive Control scheme and a modified Susceptible-Infected-Recovered (SIR)-based epidemiological model, the approach is aimed at minimizing the cost of the so-called non-pharmaceutical interventions (that is, mitigation strategies), while ensuring that the capacity of the network of regional healthcare systems is not violated. In addition, the proposed approach supports policy makers in taking targeted intervention decisions on different regions by an integrated and structured model, thus both respecting the specific regional health systems characteristics and improving the system-wide performance by avoiding uncoordinated actions of the regions. The methodology is tested on the COVID-19 outbreak data related to the network of Italian regions, showing its effectiveness in properly supporting the definition of effective regional strategies for managing the COVID-19 diffusion. © 2020 Elsevier Ltd},
    author_keywords={COVID-19; Epidemic control; MPC; Multi-region SIR model; Pandemic modeling; Post-lockdown mitigation strategies; SIR model},
    document_type={Article},
    source={Scopus},
    }
  • Carli, R., Cavone, G., Epicoco, N., Di Ferdinando, M., Scarabaggio, P. & Dotoli, M. (2020) Consensus-Based Algorithms for Controlling Swarms of Unmanned Aerial Vehicles. IN Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12338 LNCS.84-99. doi:10.1007/978-3-030-61746-2_7
    [BibTeX] [Abstract] [Download PDF]
    Multiple Unmanned Aerial Vehicles (multi-UAVs) applications are recently growing in several fields, ranging from military and rescue missions, remote sensing, and environmental surveillance, to meteorology, logistics, and farming. Overcoming the limitations on battery lifespan and on-board processor capabilities, the coordinated use of multi-UAVs is indeed more suitable than employing a single UAV in certain tasks. Hence, the research on swarm of UAVs is receiving increasing attention, including multidisciplinary aspects, such as coordination, aggregation, network communication, path planning, information sensing, and data fusion. The focus of this paper is on defining novel control strategies for the deployment of multi-UAV systems in a distributed time-varying set-up, where UAVs rely on local communication and computation. In particular, modeling the dynamics of each UAV by a discrete-time integrator, we analyze the main swarm intelligence strategies, namely flight formation, swarm tracking, and social foraging. First, we define a distributed control strategy for steering the agents of the swarm towards a collection point. Then, we cope with the formation control, defining a procedure to arrange agents in a family of geometric formations, where the distance between each pair of UAVs is predefined. Subsequently, we focus on swarm tracking, defining a distributed mechanism based on the so-called leader-following consensus to move the entire swarm in accordance with a predefined trajectory. Moreover, we define a social foraging strategy that allows agents to avoid obstacles, by imposing on-line a time-varying formation pattern. Finally, through numerical simulations we show the effectiveness of the proposed algorithms. © 2020, Springer Nature Switzerland AG.
    @ARTICLE{Carli202084,
    author={Carli, R. and Cavone, G. and Epicoco, N. and Di Ferdinando, M. and Scarabaggio, P. and Dotoli, M.},
    title={Consensus-Based Algorithms for Controlling Swarms of Unmanned Aerial Vehicles},
    journal={Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
    year={2020},
    volume={12338 LNCS},
    pages={84-99},
    doi={10.1007/978-3-030-61746-2_7},
    note={cited By 4},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85093852465&doi=10.1007%2f978-3-030-61746-2_7&partnerID=40&md5=1c7da6000e4015880227c1eafe608f20},
    affiliation={Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy; Center of Excellence DEWS, Department of Information Engineering, Computer Science and Mathematics, University of L’Aquila, L’Aquila, Italy},
    abstract={Multiple Unmanned Aerial Vehicles (multi-UAVs) applications are recently growing in several fields, ranging from military and rescue missions, remote sensing, and environmental surveillance, to meteorology, logistics, and farming. Overcoming the limitations on battery lifespan and on-board processor capabilities, the coordinated use of multi-UAVs is indeed more suitable than employing a single UAV in certain tasks. Hence, the research on swarm of UAVs is receiving increasing attention, including multidisciplinary aspects, such as coordination, aggregation, network communication, path planning, information sensing, and data fusion. The focus of this paper is on defining novel control strategies for the deployment of multi-UAV systems in a distributed time-varying set-up, where UAVs rely on local communication and computation. In particular, modeling the dynamics of each UAV by a discrete-time integrator, we analyze the main swarm intelligence strategies, namely flight formation, swarm tracking, and social foraging. First, we define a distributed control strategy for steering the agents of the swarm towards a collection point. Then, we cope with the formation control, defining a procedure to arrange agents in a family of geometric formations, where the distance between each pair of UAVs is predefined. Subsequently, we focus on swarm tracking, defining a distributed mechanism based on the so-called leader-following consensus to move the entire swarm in accordance with a predefined trajectory. Moreover, we define a social foraging strategy that allows agents to avoid obstacles, by imposing on-line a time-varying formation pattern. Finally, through numerical simulations we show the effectiveness of the proposed algorithms. © 2020, Springer Nature Switzerland AG.},
    author_keywords={Swarm intelligence; Trajectory control; Unmanned Aerial Vehicles},
    document_type={Conference Paper},
    source={Scopus},
    }

2019

  • Carli, R., Cavone, G., Dotoli, M., Epicoco, N., Manganiello, C. & Tricarico, L. (2019) ICT-based methodologies for sheet metal forming design: A survey on simulation approaches IN Conference Proceedings – IEEE International Conference on Systems, Man and Cybernetics., 128-133. doi:10.1109/SMC.2019.8914082
    [BibTeX] [Abstract] [Download PDF]
    Sheet metal forming processes are widely adopted in manufacturing industries and in the recent years there has been a growing demand for sheet metal items with different shapes and characteristics. However, the traditional process is unable to meet the modern industrial requirements, mainly due to the high costs of dies and the long manufacturing time cycles. On the contrary, developing products with high speed, low cost, and high quality is a key issue. Therefore, new methods and technologies to speed up the sheet metal forming process while keeping costs limited are needed. In particular, a key issue is the proper design of the forming process, which can benefit from the use of Information and Communications Technology (ICT) simulation techniques. This paper investigates the recent trends on ICT-based methodologies for sheet metal forming to identify the foremost research areas whose advancement will lead meeting the modern market’s needs. © 2019 IEEE.
    @CONFERENCE{Carli2019128,
    author={Carli, R. and Cavone, G. and Dotoli, M. and Epicoco, N. and Manganiello, C. and Tricarico, L.},
    title={ICT-based methodologies for sheet metal forming design: A survey on simulation approaches},
    journal={Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics},
    year={2019},
    volume={2019-October},
    pages={128-133},
    doi={10.1109/SMC.2019.8914082},
    art_number={8914082},
    note={cited By 1},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076787727&doi=10.1109%2fSMC.2019.8914082&partnerID=40&md5=b067223578db03937c9be68febc5f920},
    affiliation={Polytechnic of Bari, Department of Electrical and Information Engineering, Bari, Italy; Polytechnic of Bari, Department of Mechanics, Mathematics and Management, Bari, Italy},
    abstract={Sheet metal forming processes are widely adopted in manufacturing industries and in the recent years there has been a growing demand for sheet metal items with different shapes and characteristics. However, the traditional process is unable to meet the modern industrial requirements, mainly due to the high costs of dies and the long manufacturing time cycles. On the contrary, developing products with high speed, low cost, and high quality is a key issue. Therefore, new methods and technologies to speed up the sheet metal forming process while keeping costs limited are needed. In particular, a key issue is the proper design of the forming process, which can benefit from the use of Information and Communications Technology (ICT) simulation techniques. This paper investigates the recent trends on ICT-based methodologies for sheet metal forming to identify the foremost research areas whose advancement will lead meeting the modern market's needs. © 2019 IEEE.},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Carli, R., Cavone, G., Dotoli, M., Epicoco, N. & Scarabaggio, P. (2019) Model predictive control for thermal comfort optimization in building energy management systems IN Conference Proceedings – IEEE International Conference on Systems, Man and Cybernetics., 2608-2613. doi:10.1109/SMC.2019.8914489
    [BibTeX] [Abstract] [Download PDF]
    Model Predictive Control (MPC) has recently gained special attention to efficiently regulate Heating, Ventilation and Air Conditioning (HVAC) systems of buildings, since it explicitly allows energy savings while maintaining thermal comfort criteria. In this paper we propose a MPC algorithm for the on-line optimization of both the indoor thermal comfort and the related energy consumption of buildings. We use Fanger’s Predicted Mean Vote (PMV) as thermal comfort index, while to predict the energy performance of the building, we adopt a simplified thermal model. This allows computing optimal control actions by defining and solving a tractable non-linear optimization problem that incorporates the PMV index into the MPC cost function in addition to a term accounting for energy saving. The proposed MPC approach is implemented on a building automation system deployed in an office building located at the Polytechnic of Bari (Italy). Several on-field tests are performed to assess the applicability and efficacy of the control algorithm in a real environment against classical thermal comfort control approach based on the use of thermostats. © 2019 IEEE.
    @CONFERENCE{Carli20192608,
    author={Carli, R. and Cavone, G. and Dotoli, M. and Epicoco, N. and Scarabaggio, P.},
    title={Model predictive control for thermal comfort optimization in building energy management systems},
    journal={Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics},
    year={2019},
    volume={2019-October},
    pages={2608-2613},
    doi={10.1109/SMC.2019.8914489},
    art_number={8914489},
    note={cited By 9},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076778873&doi=10.1109%2fSMC.2019.8914489&partnerID=40&md5=3c982fb93adbcfb5202b48b60ad0f22d},
    affiliation={Information Engineering of the Polytechnic of Bari, Department of Electrical, Bari, 70125, Italy},
    abstract={Model Predictive Control (MPC) has recently gained special attention to efficiently regulate Heating, Ventilation and Air Conditioning (HVAC) systems of buildings, since it explicitly allows energy savings while maintaining thermal comfort criteria. In this paper we propose a MPC algorithm for the on-line optimization of both the indoor thermal comfort and the related energy consumption of buildings. We use Fanger's Predicted Mean Vote (PMV) as thermal comfort index, while to predict the energy performance of the building, we adopt a simplified thermal model. This allows computing optimal control actions by defining and solving a tractable non-linear optimization problem that incorporates the PMV index into the MPC cost function in addition to a term accounting for energy saving. The proposed MPC approach is implemented on a building automation system deployed in an office building located at the Polytechnic of Bari (Italy). Several on-field tests are performed to assess the applicability and efficacy of the control algorithm in a real environment against classical thermal comfort control approach based on the use of thermostats. © 2019 IEEE.},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Cavone, G., Blenkers, L., Van Den Boom, T., Dotoli, M., Seatzu, C. & De Schutter, B. (2019) Railway disruption: A bi-level rescheduling algorithm IN 2019 6th International Conference on Control, Decision and Information Technologies, CoDIT 2019., 54-59. doi:10.1109/CoDIT.2019.8820380
    [BibTeX] [Abstract] [Download PDF]
    The real-time rescheduling of railway traffic in case of unexpected events is a challenging task. This is mainly due to the complexity of the railway service, which has to ensure safety, punctuality, and efficiency to customers by respecting timetable, framework, and resources constraints. Most of the available researches focus on short delays (i.e., disturbances). Approaches typically rely on simplified macroscopic models for large-scale systems or detailed microscopic models for one or a few lines, due to the long computation time required for solving the rescheduling problem. Only a small number of works consider rescheduling in case of long delays (i.e., disruptions) and all of them are also based on either a macroscopic or a microscopic model. This research focuses on disruptions and aims at filling the gap between macroscopic and microscopic modelling by proposing an innovative bi-level rescheduling algorithm based on a mesoscopic Mixed Integer Linear Programming (MILP) model. The technique allows obtaining a feasible rescheduled timetable in a short computation time respecting not only timetable and safety constraints (typical of macroscopic models) but also capacity and ordering constraints for the disrupted stations (typical of microscopic models). The bi-level algorithm first solves the macroscopic MILP rescheduling problem and then, considering the cancellation and non-admissible platform assignments results, it solves a mesoscopic MILP rescheduling problem. This allows to significantly reduce the search space and consequently the computation time. The method is tested for the rescheduling of the Dutch railway traffic in case of a full blockade between two consecutive stations. © 2019 IEEE.
    @CONFERENCE{Cavone201954,
    author={Cavone, G. and Blenkers, L. and Van Den Boom, T. and Dotoli, M. and Seatzu, C. and De Schutter, B.},
    title={Railway disruption: A bi-level rescheduling algorithm},
    journal={2019 6th International Conference on Control, Decision and Information Technologies, CoDIT 2019},
    year={2019},
    pages={54-59},
    doi={10.1109/CoDIT.2019.8820380},
    art_number={8820380},
    note={cited By 5},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85072825857&doi=10.1109%2fCoDIT.2019.8820380&partnerID=40&md5=1402c9b7c47196a37e50ebaba061fdef},
    affiliation={Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy; Delft Center for Systems and Control, Technology University of Delft, Delft, Netherlands; Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, Italy},
    abstract={The real-time rescheduling of railway traffic in case of unexpected events is a challenging task. This is mainly due to the complexity of the railway service, which has to ensure safety, punctuality, and efficiency to customers by respecting timetable, framework, and resources constraints. Most of the available researches focus on short delays (i.e., disturbances). Approaches typically rely on simplified macroscopic models for large-scale systems or detailed microscopic models for one or a few lines, due to the long computation time required for solving the rescheduling problem. Only a small number of works consider rescheduling in case of long delays (i.e., disruptions) and all of them are also based on either a macroscopic or a microscopic model. This research focuses on disruptions and aims at filling the gap between macroscopic and microscopic modelling by proposing an innovative bi-level rescheduling algorithm based on a mesoscopic Mixed Integer Linear Programming (MILP) model. The technique allows obtaining a feasible rescheduled timetable in a short computation time respecting not only timetable and safety constraints (typical of macroscopic models) but also capacity and ordering constraints for the disrupted stations (typical of microscopic models). The bi-level algorithm first solves the macroscopic MILP rescheduling problem and then, considering the cancellation and non-admissible platform assignments results, it solves a mesoscopic MILP rescheduling problem. This allows to significantly reduce the search space and consequently the computation time. The method is tested for the rescheduling of the Dutch railway traffic in case of a full blockade between two consecutive stations. © 2019 IEEE.},
    document_type={Conference Paper},
    source={Scopus},
    }

2018

  • Cavone, G., Dotoli, M., Epicoco, N., Morelli, D. & Seatzu, C. (2018) A Game-theoretical Design Technique for Multi-stage Supply Chains under Uncertainty IN IEEE International Conference on Automation Science and Engineering., 528-533. doi:10.1109/COASE.2018.8560501
    [BibTeX] [Abstract] [Download PDF]
    We present a design approach for multi-stage Supply Chains (SCs) that allows selecting candidates and assigning them orders under uncertainty. A bargaining game model in its extensive form (i.e., with a time sequencing of moves) and in a fuzzy setting is proposed. The product quantities that each actor requires from the previous SC stage are determined modelling the real behavior of SC stakeholders, which on the one hand act to maximize their own profit, on the other hand cooperate to maximize the overall efficiency of the SC and minimize production costs and lead times. Assignments are determined taking into account stock levels, uncertain production or warehouse capacities, and customers’ demand. Thus, the method supports the decision making process providing an agile, cooperative, and resource-efficient design of multi-stage SCs under uncertain parameters. A literature SC is used as a test case to evaluate the effectiveness of the technique. © 2018 IEEE.
    @CONFERENCE{Cavone2018528,
    author={Cavone, G. and Dotoli, M. and Epicoco, N. and Morelli, D. and Seatzu, C.},
    title={A Game-theoretical Design Technique for Multi-stage Supply Chains under Uncertainty},
    journal={IEEE International Conference on Automation Science and Engineering},
    year={2018},
    volume={2018-August},
    pages={528-533},
    doi={10.1109/COASE.2018.8560501},
    art_number={8560501},
    note={cited By 4},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85059976158&doi=10.1109%2fCOASE.2018.8560501&partnerID=40&md5=029376a062b0c55a7cccfc170988ada1},
    affiliation={Polytechnic of Bari, Department of Electrical and Information Engineering, Bari, Italy; Department of Electrical and Electronic Engineering, University of Cagliari, Caglinri, Italy},
    abstract={We present a design approach for multi-stage Supply Chains (SCs) that allows selecting candidates and assigning them orders under uncertainty. A bargaining game model in its extensive form (i.e., with a time sequencing of moves) and in a fuzzy setting is proposed. The product quantities that each actor requires from the previous SC stage are determined modelling the real behavior of SC stakeholders, which on the one hand act to maximize their own profit, on the other hand cooperate to maximize the overall efficiency of the SC and minimize production costs and lead times. Assignments are determined taking into account stock levels, uncertain production or warehouse capacities, and customers' demand. Thus, the method supports the decision making process providing an agile, cooperative, and resource-efficient design of multi-stage SCs under uncertain parameters. A literature SC is used as a test case to evaluate the effectiveness of the technique. © 2018 IEEE.},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Cavone, G., Dotoli, M. & Seatzu, C. (2018) A Survey on Petri Net Models for Freight Logistics and Transportation Systems. IN IEEE Transactions on Intelligent Transportation Systems, 19.1795-1813. doi:10.1109/TITS.2017.2737788
    [BibTeX] [Abstract] [Download PDF]
    The benefits of logistics and transportation systems to citizens, economy, and society can strongly increase when considering a smart, safe, and environmentally friendly management. This results in the implementation of intelligent transportation systems that combine innovative technologies and transportation frameworks at the aim of finding proper solutions to the related decision problems. To achieve such a goal, the intrinsic discrete event dynamics of these systems should be considered when deriving a model to be used for simulation, analysis, optimization, and control. Among the different discrete event models, Petri Nets (PNs) are particularly effective due to a series of relevant features. In addition, several high-level PN models (e.g., colored, continuous, or hybrid) allow the solution of complex and large-dimension problems that typically arise from real-life applications in the area of freight logistics and transportation systems. This paper presents a survey on contributions in this area. Papers are classified according to the addressed problem, namely, strategic/tactical or operational decision-making-level problem, and the adopted PN formalism. We also debate the approaches’ viability, discussing contributions and limitations, and identify future research directions to enhance the successful application of PNs in freight logistics and transportation systems. © 2000-2011 IEEE.
    @ARTICLE{Cavone20181795,
    author={Cavone, G. and Dotoli, M. and Seatzu, C.},
    title={A Survey on Petri Net Models for Freight Logistics and Transportation Systems},
    journal={IEEE Transactions on Intelligent Transportation Systems},
    year={2018},
    volume={19},
    number={6},
    pages={1795-1813},
    doi={10.1109/TITS.2017.2737788},
    note={cited By 35},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029185229&doi=10.1109%2fTITS.2017.2737788&partnerID=40&md5=b13acc8aca9a368189e296cec63c8a37},
    affiliation={Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, 09123, Italy; Department of Electrical and Information Engineering, Politecnico di Bari, Bari, 70125, Italy},
    abstract={The benefits of logistics and transportation systems to citizens, economy, and society can strongly increase when considering a smart, safe, and environmentally friendly management. This results in the implementation of intelligent transportation systems that combine innovative technologies and transportation frameworks at the aim of finding proper solutions to the related decision problems. To achieve such a goal, the intrinsic discrete event dynamics of these systems should be considered when deriving a model to be used for simulation, analysis, optimization, and control. Among the different discrete event models, Petri Nets (PNs) are particularly effective due to a series of relevant features. In addition, several high-level PN models (e.g., colored, continuous, or hybrid) allow the solution of complex and large-dimension problems that typically arise from real-life applications in the area of freight logistics and transportation systems. This paper presents a survey on contributions in this area. Papers are classified according to the addressed problem, namely, strategic/tactical or operational decision-making-level problem, and the adopted PN formalism. We also debate the approaches' viability, discussing contributions and limitations, and identify future research directions to enhance the successful application of PNs in freight logistics and transportation systems. © 2000-2011 IEEE.},
    author_keywords={control; Freight transportation; management; modeling and simulation; optimization; Petri nets},
    document_type={Article},
    source={Scopus},
    }
  • Cavone, G., Dotoli, M., Epicoco, N. & Seatzu, C. (2018) Efficient Resource Planning of Intermodal Terminals under Uncertainty , 398-403. doi:10.1016/j.ifacol.2018.07.065
    [BibTeX] [Abstract] [Download PDF]
    This paper presents a decision support tool for the efficient resource planning and management of intermodal terminals under uncertainty, allowing to address the planning issue under imprecise or uncertain data (e.g., estimates on flows, resource utilization, operating conditions). The procedure consists of three steps: 1) the definition of a Timed Petri Net model of the terminal; 2) the computation of suitable performance indices to evaluate whether the current configuration is able to cope with a foreseen increase in the freight flows; 3) in the case of not satisfactory values of the indices at the previous step, the simulation of alternative planning solutions and the detection of the most efficient one via a cross-efficiency fuzzy Data Envelopment Analysis technique. In order to test its effectiveness, the procedure is applied to a real case study. © 2018
    @CONFERENCE{Cavone2018398,
    author={Cavone, G. and Dotoli, M. and Epicoco, N. and Seatzu, C.},
    title={Efficient Resource Planning of Intermodal Terminals under Uncertainty},
    year={2018},
    volume={51},
    number={9},
    pages={398-403},
    doi={10.1016/j.ifacol.2018.07.065},
    note={cited By 2},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050140566&doi=10.1016%2fj.ifacol.2018.07.065&partnerID=40&md5=edaa20f412201b8f7e7839c4fc246aad},
    affiliation={Department of Electrical and Information Engineering, Politecnico di Bari, Bari, Italy; Department of Electrical and Electronic Engineering, Università degli Studi di Cagliari, Cagliari, Italy},
    abstract={This paper presents a decision support tool for the efficient resource planning and management of intermodal terminals under uncertainty, allowing to address the planning issue under imprecise or uncertain data (e.g., estimates on flows, resource utilization, operating conditions). The procedure consists of three steps: 1) the definition of a Timed Petri Net model of the terminal; 2) the computation of suitable performance indices to evaluate whether the current configuration is able to cope with a foreseen increase in the freight flows; 3) in the case of not satisfactory values of the indices at the previous step, the simulation of alternative planning solutions and the detection of the most efficient one via a cross-efficiency fuzzy Data Envelopment Analysis technique. In order to test its effectiveness, the procedure is applied to a real case study. © 2018},
    author_keywords={Data Envelopment Analysis; efficiency; fuzzy theory; intermodal terminals; performance evaluation; Petri Nets; resource planning; uncertainty},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Cavone, G., Dotoli, M., Epicoco, N., Franceschelli, M. & Seatzu, C. (2018) Hybrid Petri Nets to Re-design Low-Automated Production Processes: the Case Study of a Sardinian Bakery , 265-270. doi:10.1016/j.ifacol.2018.06.311
    [BibTeX] [Abstract] [Download PDF]
    This paper shows the practical relevance of first-order hybrid Petri nets in the re-design process of low-automated production systems. In particular, we analyze the case study of a bakery producing “pane Carasau” a typical Sardinian bread, whose traditional production plant currently has difficulties in coping with the constant increase in market demand. Through first-order hybrid Petri nets, the current functioning and the operating features and dynamics of the case study are modelled, waste sources and bottlenecks are detected, and alternative re-designed scenarios are implemented and evaluated to identify the most suitable reengineering actions to be developed. © 2018
    @CONFERENCE{Cavone2018265,
    author={Cavone, G. and Dotoli, M. and Epicoco, N. and Franceschelli, M. and Seatzu, C.},
    title={Hybrid Petri Nets to Re-design Low-Automated Production Processes: the Case Study of a Sardinian Bakery},
    year={2018},
    volume={51},
    number={7},
    pages={265-270},
    doi={10.1016/j.ifacol.2018.06.311},
    note={cited By 14},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85050128384&doi=10.1016%2fj.ifacol.2018.06.311&partnerID=40&md5=832573ffac1f56c0f884bc55c52b23d1},
    affiliation={Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, 70125, Italy; Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, 09123, Italy},
    abstract={This paper shows the practical relevance of first-order hybrid Petri nets in the re-design process of low-automated production systems. In particular, we analyze the case study of a bakery producing “pane Carasau” a typical Sardinian bread, whose traditional production plant currently has difficulties in coping with the constant increase in market demand. Through first-order hybrid Petri nets, the current functioning and the operating features and dynamics of the case study are modelled, waste sources and bottlenecks are detected, and alternative re-designed scenarios are implemented and evaluated to identify the most suitable reengineering actions to be developed. © 2018},
    author_keywords={First-Order Hybrid Petri nets; Production processes; Re-design; Reengineering},
    document_type={Conference Paper},
    source={Scopus},
    }

2017

  • Cavone, G., Dotoli, M., Epicoco, N. & Seatzu, C. (2017) A decision making procedure for robust train rescheduling based on mixed integer linear programming and Data Envelopment Analysis. IN Applied Mathematical Modelling, 52.255-273. doi:10.1016/j.apm.2017.07.030
    [BibTeX] [Abstract] [Download PDF]
    This paper presents a self-learning decision making procedure for robust real-time train rescheduling in case of disturbances. The procedure is applicable to aperiodic timetables of mixed-tracked networks and it consists of three steps. The first two are executed in real-time and provide the rescheduled timetable, while the third one is executed offline and guarantees the self-learning part of the method. In particular, in the first step, a robust timetable is determined, which is valid for a finite time horizon. This robust timetable is obtained solving a mixed integer linear programming problem aimed at finding the optimal compromise between two objectives: the minimization of the delays of the trains and the maximization of the robustness of the timetable. In the second step, a merging procedure is first used to join the obtained timetable with the nominal one. Then, a heuristics is applied to identify and solve all conflicts eventually arising after the merging procedure. Finally, in the third step an offline cross-efficiency fuzzy Data Envelopment Analysis technique is applied to evaluate the efficiency of the rescheduled timetable in terms of delays minimization and robustness maximization when different relevance weights (defining the compromise between the two optimization objectives) are used in the first step. The procedure is thus able to determine appropriate relevance weights to employ when disturbances of the same type affect again the network. The railway service provider can take advantage of this procedure to automate, optimize, and expedite the rescheduling process. Moreover, thanks to the self-learning capability of the procedure, the quality of the rescheduling is improved at each reapplication of the method. The technique is applied to a real data set related to a regional railway network in Southern Italy to test its effectiveness. © 2017 Elsevier Inc.
    @ARTICLE{Cavone2017255,
    author={Cavone, G. and Dotoli, M. and Epicoco, N. and Seatzu, C.},
    title={A decision making procedure for robust train rescheduling based on mixed integer linear programming and Data Envelopment Analysis},
    journal={Applied Mathematical Modelling},
    year={2017},
    volume={52},
    pages={255-273},
    doi={10.1016/j.apm.2017.07.030},
    note={cited By 23},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032351265&doi=10.1016%2fj.apm.2017.07.030&partnerID=40&md5=9878b46aea5aa585ef08d87ee28e4b38},
    affiliation={Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, 09123, Italy; Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, 70125, Italy},
    abstract={This paper presents a self-learning decision making procedure for robust real-time train rescheduling in case of disturbances. The procedure is applicable to aperiodic timetables of mixed-tracked networks and it consists of three steps. The first two are executed in real-time and provide the rescheduled timetable, while the third one is executed offline and guarantees the self-learning part of the method. In particular, in the first step, a robust timetable is determined, which is valid for a finite time horizon. This robust timetable is obtained solving a mixed integer linear programming problem aimed at finding the optimal compromise between two objectives: the minimization of the delays of the trains and the maximization of the robustness of the timetable. In the second step, a merging procedure is first used to join the obtained timetable with the nominal one. Then, a heuristics is applied to identify and solve all conflicts eventually arising after the merging procedure. Finally, in the third step an offline cross-efficiency fuzzy Data Envelopment Analysis technique is applied to evaluate the efficiency of the rescheduled timetable in terms of delays minimization and robustness maximization when different relevance weights (defining the compromise between the two optimization objectives) are used in the first step. The procedure is thus able to determine appropriate relevance weights to employ when disturbances of the same type affect again the network. The railway service provider can take advantage of this procedure to automate, optimize, and expedite the rescheduling process. Moreover, thanks to the self-learning capability of the procedure, the quality of the rescheduling is improved at each reapplication of the method. The technique is applied to a real data set related to a regional railway network in Southern Italy to test its effectiveness. © 2017 Elsevier Inc.},
    author_keywords={Data Envelopment Analysis; Decision making; Railways; Real-time; Rescheduling; Robustness},
    document_type={Article},
    source={Scopus},
    }
  • Cavone, G., Dotoli, M., Epicoco, N. & Seatzu, C. (2017) Intermodal terminal planning by Petri Nets and Data Envelopment Analysis. IN Control Engineering Practice, 69.9-22. doi:10.1016/j.conengprac.2017.08.007
    [BibTeX] [Abstract] [Download PDF]
    A procedure for planning and resources’ management in intermodal terminals is presented. It integrates Timed Petri Nets (TPNs) and Data Envelopment Analysis (DEA) and consists of three steps: the terminal modeling via TPNs to model the regular behavior; the evaluation of whether the current configuration may cope with increased freight flows; if not, the analysis by cross-efficiency DEA of alternative planning solutions. The procedure provides the decision maker with number, capacity, and schedule of resources to tackle the flows increase. The method is evaluated by a real case study, showing that integrating TPNs and DEA allows taking planning decisions under conflicting requirements. © 2017
    @ARTICLE{Cavone20179,
    author={Cavone, G. and Dotoli, M. and Epicoco, N. and Seatzu, C.},
    title={Intermodal terminal planning by Petri Nets and Data Envelopment Analysis},
    journal={Control Engineering Practice},
    year={2017},
    volume={69},
    pages={9-22},
    doi={10.1016/j.conengprac.2017.08.007},
    note={cited By 14},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85028873437&doi=10.1016%2fj.conengprac.2017.08.007&partnerID=40&md5=f1dac97345f97f58a4fee381c92037d4},
    affiliation={Department of Electrical and Electronic Engineering, Università degli Studi di Cagliari, Via Marengo, Cagliari, Italy; Department of Electrical and Information Engineering, Politecnico di Bari, Via Re David 200, Bari, 70125, Italy},
    abstract={A procedure for planning and resources’ management in intermodal terminals is presented. It integrates Timed Petri Nets (TPNs) and Data Envelopment Analysis (DEA) and consists of three steps: the terminal modeling via TPNs to model the regular behavior; the evaluation of whether the current configuration may cope with increased freight flows; if not, the analysis by cross-efficiency DEA of alternative planning solutions. The procedure provides the decision maker with number, capacity, and schedule of resources to tackle the flows increase. The method is evaluated by a real case study, showing that integrating TPNs and DEA allows taking planning decisions under conflicting requirements. © 2017},
    author_keywords={Data Envelopment Analysis; Freight transportation; Intermodal terminals; Performance evaluation; Petri Nets; Resource planning},
    document_type={Article},
    source={Scopus},
    }

2016

  • Cavone, G., Dotoli, M. & Seatzu, C. (2016) Resource planning of intermodal terminals using timed Petri nets IN 2016 13th International Workshop on Discrete Event Systems, WODES 2016., 44-50. doi:10.1109/WODES.2016.7497824
    [BibTeX] [Abstract] [Download PDF]
    In this paper we show how timed Petri nets can be efficiently used to solve problems related to resource planning in intermodal freight transport terminals. In particular, the tackled issues regard the strategic planning of the number of facilities used to transfer the intermodal transport units and the capacity/frequency of the transportation means. A real case study is considered, namely a rail-road terminal located in southern Italy. Monte Carlo simulations based on the timed Petri net model of the terminal are carried out considering various scenarios, including both the regular behavior based on real data, and situations of potential congestion resulting from increase in the commercial flows. © 2016 IEEE.
    @CONFERENCE{Cavone201644,
    author={Cavone, G. and Dotoli, M. and Seatzu, C.},
    title={Resource planning of intermodal terminals using timed Petri nets},
    journal={2016 13th International Workshop on Discrete Event Systems, WODES 2016},
    year={2016},
    pages={44-50},
    doi={10.1109/WODES.2016.7497824},
    art_number={7497824},
    note={cited By 6},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84981302932&doi=10.1109%2fWODES.2016.7497824&partnerID=40&md5=6e380dff1ab12bf5ebd7b38ef41fe0ac},
    affiliation={Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, 09123, Italy; Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, 70125, Italy},
    abstract={In this paper we show how timed Petri nets can be efficiently used to solve problems related to resource planning in intermodal freight transport terminals. In particular, the tackled issues regard the strategic planning of the number of facilities used to transfer the intermodal transport units and the capacity/frequency of the transportation means. A real case study is considered, namely a rail-road terminal located in southern Italy. Monte Carlo simulations based on the timed Petri net model of the terminal are carried out considering various scenarios, including both the regular behavior based on real data, and situations of potential congestion resulting from increase in the commercial flows. © 2016 IEEE.},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Dotoli, M., Epicoco, N., Falagario, M. & Cavone, G. (2016) A Timed Petri Nets Model for Performance Evaluation of Intermodal Freight Transport Terminals. IN IEEE Transactions on Automation Science and Engineering, 13.842-857. doi:10.1109/TASE.2015.2404438
    [BibTeX] [Abstract] [Download PDF]
    This paper presents a general modeling framework for Intermodal Freight Transport Terminals (IFTTs). The model allows simulating and evaluating the performance of such key elements of the intermodal transportation chain. Hence, it may be used by the decision maker to identify the IFTT bottlenecks, as well as to test different solutions to improve the IFTT dynamics. The proposed modeling framework is modular and based on timed Petri Nets (PNs), where places represent resources and capacities or conditions, transitions model inputs, flows, and activities into the terminal and tokens are intermodal transport units or the means on which they are transported. The model is able to represent the different types of existing IFTTs. Its effectiveness is tested first on an example from the literature and then on a real case study, the railroad inland terminal of a leading Italian intermodal logistics company, showing its ease of application. In the real case study, using the proposed formalism we test the as-is IFTT performance and evaluate alternative possible to-be improvements in order to identify and eliminate emerging criticalities in the terminal dynamics. © 2015 IEEE.
    @ARTICLE{Dotoli2016842,
    author={Dotoli, M. and Epicoco, N. and Falagario, M. and Cavone, G.},
    title={A Timed Petri Nets Model for Performance Evaluation of Intermodal Freight Transport Terminals},
    journal={IEEE Transactions on Automation Science and Engineering},
    year={2016},
    volume={13},
    number={2},
    pages={842-857},
    doi={10.1109/TASE.2015.2404438},
    art_number={7057695},
    note={cited By 39},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929017877&doi=10.1109%2fTASE.2015.2404438&partnerID=40&md5=04520d65c7c1306a86a7b871f538bcae},
    affiliation={Department of Electrical and Information Engineering, Politecnico di Bari, Bari, 70125, Italy; Department of Mathematics, Mechanics and Management Engineering, Politecnico di Bari, Bari, 70126, Italy; Department of Electric and Electronical Engineering, Università degli Studi di Cagliari, Cagliari, 09123, Italy},
    abstract={This paper presents a general modeling framework for Intermodal Freight Transport Terminals (IFTTs). The model allows simulating and evaluating the performance of such key elements of the intermodal transportation chain. Hence, it may be used by the decision maker to identify the IFTT bottlenecks, as well as to test different solutions to improve the IFTT dynamics. The proposed modeling framework is modular and based on timed Petri Nets (PNs), where places represent resources and capacities or conditions, transitions model inputs, flows, and activities into the terminal and tokens are intermodal transport units or the means on which they are transported. The model is able to represent the different types of existing IFTTs. Its effectiveness is tested first on an example from the literature and then on a real case study, the railroad inland terminal of a leading Italian intermodal logistics company, showing its ease of application. In the real case study, using the proposed formalism we test the as-is IFTT performance and evaluate alternative possible to-be improvements in order to identify and eliminate emerging criticalities in the terminal dynamics. © 2015 IEEE.},
    author_keywords={Discrete-event systems; intermodal freight transport; modeling; performance evaluation; simulation; timed Petri nets},
    document_type={Article},
    source={Scopus},
    }
  • Cavone, G., Dotoli, M. & Seatzu, C. (2016) Management of Intermodal Freight Terminals by First-Order Hybrid Petri Nets. IN IEEE Robotics and Automation Letters, 1.2-9. doi:10.1109/LRA.2015.2502905
    [BibTeX] [Abstract] [Download PDF]
    In this paper, we show how first-order hybrid Petri nets can be efficiently used to model and manage intermodal freight transport terminals. The proposed formalism enables the terminal decision maker to choose the speeds associated with continuous transitions in order to optimize the terminal performance by two alternative control policies: the container flows maximization and the minimization of the residual containers in the storage area. The approach may be used either offline, to take decisions on the terminal resources, or online, to solve congestions/malfunctions. A real case study is modeled and managed by the proposed optimal control policies. © 2016 IEEE.
    @ARTICLE{Cavone20162,
    author={Cavone, G. and Dotoli, M. and Seatzu, C.},
    title={Management of Intermodal Freight Terminals by First-Order Hybrid Petri Nets},
    journal={IEEE Robotics and Automation Letters},
    year={2016},
    volume={1},
    number={1},
    pages={2-9},
    doi={10.1109/LRA.2015.2502905},
    art_number={7339445},
    note={cited By 12},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-85058585244&doi=10.1109%2fLRA.2015.2502905&partnerID=40&md5=5a498586aaee188bb2339dd6fff680ce},
    affiliation={Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari, 09123, Italy; Department of Electrical and Information Engineering, Polytechnic of Bari, Bari, 70125, Italy},
    abstract={In this paper, we show how first-order hybrid Petri nets can be efficiently used to model and manage intermodal freight transport terminals. The proposed formalism enables the terminal decision maker to choose the speeds associated with continuous transitions in order to optimize the terminal performance by two alternative control policies: the container flows maximization and the minimization of the residual containers in the storage area. The approach may be used either offline, to take decisions on the terminal resources, or online, to solve congestions/malfunctions. A real case study is modeled and managed by the proposed optimal control policies. © 2016 IEEE.},
    author_keywords={Discrete Event Dynamic Automation Systems; Logistics; Petri Nets for Automation Control},
    document_type={Article},
    source={Scopus},
    }

2014

  • Dotoli, M., Epicoco, N., Cavone, G., Turchiano, B. & Falagario, M. (2014) Simulation and performance evaluation of an Intermodal terminal using Petri Nets IN Proceedings – 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014., 327-332. doi:10.1109/CoDIT.2014.6996915
    [BibTeX] [Abstract] [Download PDF]
    This paper focuses on modelling and performance evaluation of an Intermodal Freight Transport Terminal (IFTT), the rail-road inland terminal of a leading Italian intermodal logistics company. The IFTT is regarded as a discrete event system and is modelled in a timed Petri net framework. By means of suitable performance indices, we simulate the Petri net model and evaluate the operational performance of the transport system. This allows assessing the efficiency level of the terminal and identifying its criticalities and bottlenecks. Further, the model allows evaluating different solutions to the recognized criticalities under alternative scenarios (e.g., when inflow traffic increases and congestions may occur). © 2014 IEEE.
    @CONFERENCE{Dotoli2014327,
    author={Dotoli, M. and Epicoco, N. and Cavone, G. and Turchiano, B. and Falagario, M.},
    title={Simulation and performance evaluation of an Intermodal terminal using Petri Nets},
    journal={Proceedings - 2014 International Conference on Control, Decision and Information Technologies, CoDIT 2014},
    year={2014},
    pages={327-332},
    doi={10.1109/CoDIT.2014.6996915},
    art_number={6996915},
    note={cited By 3},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84921395897&doi=10.1109%2fCoDIT.2014.6996915&partnerID=40&md5=fd846395d6e7004b94407b51c1ec4da3},
    affiliation={Dept. of Electrical and Information Engineering, Politecnico di Bari, Bari, Italy; Dept. of Mathematics, Mechanics and Management, Politecnico di Bari, Bari, Italy},
    abstract={This paper focuses on modelling and performance evaluation of an Intermodal Freight Transport Terminal (IFTT), the rail-road inland terminal of a leading Italian intermodal logistics company. The IFTT is regarded as a discrete event system and is modelled in a timed Petri net framework. By means of suitable performance indices, we simulate the Petri net model and evaluate the operational performance of the transport system. This allows assessing the efficiency level of the terminal and identifying its criticalities and bottlenecks. Further, the model allows evaluating different solutions to the recognized criticalities under alternative scenarios (e.g., when inflow traffic increases and congestions may occur). © 2014 IEEE.},
    author_keywords={Discrete event systems; Intermodal transport terminal; Modelling; Performance evaluation; Simulation; Timed Petri nets},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Dotoli, M., Epicoco, N., Falagario, M., Turchiano, B., Cavone, G. & Convertini, A. (2014) A Decision Support System for real-time rescheduling of railways IN 2014 European Control Conference, ECC 2014., 696-701. doi:10.1109/ECC.2014.6862177
    [BibTeX] [Abstract] [Download PDF]
    We present a Decision Support System (DSS) for real-time management of railway networks. The DSS employs a mathematical programming approach addressing traffic rescheduling under unexpected disturbances in a mixed-(single- and double-) tracked network. The DSS simulates the network behavior with the mathematical programming model based on the railway topology and constraints, rescheduling the timetable in real time, detecting and solving conflicts in the network. The DSS is applied to a real data set related to a large portion of a regional network in Southern Italy. © 2014 EUCA.
    @CONFERENCE{Dotoli2014696,
    author={Dotoli, M. and Epicoco, N. and Falagario, M. and Turchiano, B. and Cavone, G. and Convertini, A.},
    title={A Decision Support System for real-time rescheduling of railways},
    journal={2014 European Control Conference, ECC 2014},
    year={2014},
    pages={696-701},
    doi={10.1109/ECC.2014.6862177},
    art_number={6862177},
    note={cited By 10},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84911480266&doi=10.1109%2fECC.2014.6862177&partnerID=40&md5=1a944040060d3fed8ad9f768f452b52a},
    affiliation={Department of Electrical and Information Engineering of the Polytechnic of Bari, Italy; Department of Mechanical Engineering, Mathematics and Management of the Polytechnic of Bari, Italy},
    abstract={We present a Decision Support System (DSS) for real-time management of railway networks. The DSS employs a mathematical programming approach addressing traffic rescheduling under unexpected disturbances in a mixed-(single- and double-) tracked network. The DSS simulates the network behavior with the mathematical programming model based on the railway topology and constraints, rescheduling the timetable in real time, detecting and solving conflicts in the network. The DSS is applied to a real data set related to a large portion of a regional network in Southern Italy. © 2014 EUCA.},
    document_type={Conference Paper},
    source={Scopus},
    }
  • Dotoli, M., Epicoco, N., Falagario, M. & Cavone, G. (2014) A timed Petri nets model for intermodal freight transport terminals IN IFAC Proceedings Volumes (IFAC-PapersOnline)., 176-181. doi:10.3182/20140514-3-FR-4046.00038
    [BibTeX] [Abstract] [Download PDF]
    This paper presents a general modelling framework for intermodal freight transport terminals. The model allows evaluating the operational performance of such transportation systems, assessing the efficiency level of the terminal and identifying its bottlenecks by suitable performance indices. Moreover, it allows evaluating different solutions to the identified criticalities. The proposed framework is modular and based on timed Petri nets: places represent resources and capacities or conditions, transitions model inputs, flows and activities into the terminal, and tokens are intermodal transport units or the means on which they are transported. A simulation of a case study shows the model effectiveness. © IFAC.
    @CONFERENCE{Dotoli2014176,
    author={Dotoli, M. and Epicoco, N. and Falagario, M. and Cavone, G.},
    title={A timed Petri nets model for intermodal freight transport terminals},
    journal={IFAC Proceedings Volumes (IFAC-PapersOnline)},
    year={2014},
    volume={9},
    number={3},
    pages={176-181},
    doi={10.3182/20140514-3-FR-4046.00038},
    note={cited By 10},
    url={https://www.scopus.com/inward/record.uri?eid=2-s2.0-84945963822&doi=10.3182%2f20140514-3-FR-4046.00038&partnerID=40&md5=12d63035451f5fdd5d932509425ee237},
    affiliation={Dept. of Electrical and Information Engineering, Politecnico di Bari, Bari, Italy; Dept. of Mathematics, Mechanics and Management Engineering, Politecnico di Bari, Bari, Italy},
    abstract={This paper presents a general modelling framework for intermodal freight transport terminals. The model allows evaluating the operational performance of such transportation systems, assessing the efficiency level of the terminal and identifying its bottlenecks by suitable performance indices. Moreover, it allows evaluating different solutions to the identified criticalities. The proposed framework is modular and based on timed Petri nets: places represent resources and capacities or conditions, transitions model inputs, flows and activities into the terminal, and tokens are intermodal transport units or the means on which they are transported. A simulation of a case study shows the model effectiveness. © IFAC.},
    author_keywords={Discrete event systems; Intermodal transport; Modelling; Simulation; Timed Petri nets},
    document_type={Conference Paper},
    source={Scopus},
    }