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Climate change is increasing the occurrence of the so-called heatwaves with a trend that is expected to worsen in the next years due to global warming. The growing intensity and duration of these extreme weather events are leading to a significant number of power system failures, especially in urban areas. This is drastically affecting the reliability and normal operation of power distribution grids around the world, with high financial costs and huge negative impacts on people's life. Typically, the response to these failure events is approached by post-event analysis, aimed at identifying the grid areas that require resources to increase the resilience of the system and prevent future outages. Nevertheless, understanding the nature of heatwaves and forecasting their impact on power distribution systems can be useful to anticipate them and accelerate a reaction, possibly avoiding negative impacts on power systems and customers. In this study, a structured method to predict distribution grid disruptions caused by heatwaves is defined. The proposed method relies on machine learning to analyze previous failure data and forecast power grid outages using operational and meteorological information. The method is evaluated using real failure data from a large power distribution network located in southern Italy.

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.

This paper focuses on the control of microgrids where both gas and electricity are provided to the final customer, i.e., multi-carrier microgrids. Hence, these microgrids include thermal and electrical loads, renewable energy sources, energy storage systems, heat pumps, and combined heat and power units. The parameters characterizing the multi-carrier microgrid are subject to several disturbances, such as fluctuations in the provision of renewable energy, variability in the electrical and thermal demand, and uncertainties in the electricity and gas pricing. With the aim of accounting for the data uncertainties in the microgrid, we propose a Robust Model Predictive Control (RMPC) approach whose goal is to minimize the total economical cost, while satisfying comfort and energy requests of the final users. In the related literature various RMPC approaches have been proposed, focusing either on electrical or on thermal microgrids. Only a few contributions have addressed the robust control of multi-carrier microgrids. Consequently, we propose an innovative RMPC algorithm that employs on an uncertainty set-based method and that can provide better performance compared with deterministic model predictive controllers applied to multi-carrier microgrids. With the aim of mitigating the conservativeness of the approach, we define suitable robustness factors and we investigate the effects of such factors on the robustness of the solution against variations of the uncertain parameters. We show the effectiveness of the proposed RMPC approach by applying it to a realistic residential multi-carrier microgrid and comparing the obtained results with the ones of a baseline robust method. Postprint accepted for publication in IEEE Transactions on Automation Science and Engineering (T-ASE). How to cite: R. Carli, G. Cavone, T. Pippia, B. De Schutter, and M. Dotoli, ”Robust Optimal Control for Demand Side Management of Multi-Carrier Microgrids,” IEEE Transactions on Automation Science and Engineering, vol. 19, no. 3, pp. 1338-1351, July 2022. DOI: https://doi.org/10.1109/TASE.2022.3148856

In this paper, we address the problem of automating the definition of feasible pallets configurations. This issue is crucial for the competitiveness of logistic companies and is still one of the most difficult problems in internal logistics. In fact, it requires the fast solution of a three-dimensional Bin Packing Problem (3D-BPP) with additional logistic specifications that are fundamental in real applications. To this aim, we propose a matheuristics that, given a set of items, provides feasible pallets configurations that satisfy the practical requirements of items' grouping by logistic features, load bearing, stability, height homogeneity, overhang as well as weight limits, and robotized layer picking. The proposed matheuristics combines a mixed integer linear programming (MILP) formulation of the 3D-Single Bin-Size BPP (3D-SBSBPP) and a layer building heuristics. In particular, the feasible pallets configurations are obtained by sequentially solving two MILP sub-problems: the first, given the set of items to be packed, aims at minimizing the unused space in each layer and thus the number of layers; the latter aims at minimizing the number of shipping bins given the set of layers obtained from the first problem. The approach is extensively tested and compared with existing approaches. For its validation we use both realistic data-sets drawn from the literature and real data-sets, obtained from an Italian logistics leader. The resulting outcomes show the effectiveness of the method in providing high-quality bin configurations in short computational times.

Deep drawing is a metalworking procedure aimed at getting a cold metal sheet plastically deformed in accordance with a pre-defined mould. Although this procedure is well-established in industry, it is still susceptible to several issues affecting the quality of the stamped metal products. In order to reduce defects of workpieces, process control approaches can be performed. Typically, process control employs simple proportional-integral-derivative (PID) regulators that steer the blank holder force (BHF) based on the error on the punch force. However, a single PID can only control single-input single-output systems and cannot handle constraints on the process variables. Differently from the state of the art, in this paper we propose a process control architecture based on Model Predictive Control (MPC), which considers a multi-variable system model. In particular, we represent the deep drawing process with a single-input multiple-output Hammerstein-Wiener model that relates the BHF with the draw-in of n different critical points around the die. This allows the avoidance of workpiece defects that are due to the abnormal sliding of the metal sheet during the forming phase. The effectiveness of the proposed process controller is shown on a real case study in a digital twin framework, where the performance achieved by the MPC-based system is analyzed in detail and compared against the results obtained through an ad-hoc defined multiple PID-based control architecture.

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.

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.

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

This paper proposes a stochastic non-linear model predictive controller to support policy-makers in determining robust optimal non-pharmaceutical 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 socio-economic categories) to minimize the socio-economic 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 non-pharmaceutical 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. Postprint accepted for pubblication in IEEE Transactions on Automation Science and Engineering (T-ASE) How to cite: P. Scarabaggio, R. Carli, G. Cavone, N. Epicoco and M. Dotoli, (2021) "Non-Pharmaceutical Stochastic Optimal Control Strategies to Mitigate the COVID-19 Spread," in IEEE Transactions on Automation Science and Engineering. DOI: http://doi.org/10.1109/TASE.2021.3111338 © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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.

Introduzione: La morte endouterina fetale rappresenta un problema rilevante per la mortalità perinatale colpendo 2,6 milioni di famiglie ogni anno. Tale evento influenza anche il benessere psicofisico degli operatori sanitari ponendoli a rischio di sviluppare una Sindrome da Burnout Obiettivo: Valutazione dell’influenza del percorso assistenziale in caso di morte endouterina fetale sulla salute emotiva degli operatori sanitari. Materiali e Metodi: Indagine osservazionale multicentrica effettuata con campionamento di convenienza. Il campione risulta formato da 110 professionisti quali Ostetriche, Medici e medici in formazione. Si sono utilizzati il questionario di Ravaldi, l’Impact of Event Scale e la MaslachBurn-out Inventory. Risultati: Più della metà degli intervistati (60.9%) affermano di non aver ricevuto nessun tipo di preparazione se non quella post base acquisita autonomamente (54,5%). Dall’analisi dell’IES risulta che l’ostetrica sia la figura maggiormente colpita da sintomi da stress post traumatico in seguito ad assistenza in caso di MEF. Dall’analisi del MBI si nota come possa esserci una stretta correlazione tra MEF e Burnout in mancanza di una adeguata preparazione, in particolare nei medici in formazione. Discussione e conclusione Una formazione specifica nell’assistenza alle famiglie colpite da MEF può aiutare gli operatori sanitari nella gestione dell’evento.

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.

For high-density metro traffic, nowadays the time-variant passenger flow is the main cause of train delays and stranded passengers. Typically, the main objective of automatic metro traffic regulation methods is to minimize the delay time of trains while passengers' satisfaction is not considered. Instead, 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. In particular, the PFM is devoted to the optimization of the headway time in case of platform overcrowding, so as to reduce the passengers waiting time at platforms and increase the load rate of trains; while the TOM is devoted to the minimization of trains' delays. 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., Comment: This work has been submitted to the 2021 IEEE International Intelligent Transportation Systems Conference (ITSC)

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. Specifically, this paper proposes an accurate SIRUCQHE epidemiological model, with eight compartments (namely, Susceptible, Infected, Removed, Unsusceptible, Contagious, Quarantined, Hospitalized, and Extinct). 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, thus considering also the possibility that some individuals may receive only one injection if the resulting expected fatalities are low. To show the effectiveness of the resulting strategies, we first calibrate the model on the Israeli scenario using real data to get reliable predictions on the pandemic dynamics. Lastly, we estimate the impact of the vaccine administration on the virus dynamics and, in particular, based on validated model, we assess the impact of the first dose of the Pfitzer's vaccine confirming the results of clinical tests. Extended version of the paper published in Proceedings of the IEEE 16th International Conference on Automation Science and Engineering (CASE) How to cite: Scarabaggio, P., Carli, R., Cavone, G., Epicoco, N., & Dotoli, M. "Modeling, Estimation, and Optimal Control of Anti-COVID-19 Multi-dose Vaccine Administration." In 2021 IEEE 17th International Conference on Automation Science and Engineering (CASE) (pp. 990-995). IEEE. DOI: https://doi.org/10.1109/CASE49439.2021.9551418 © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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.

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.

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. info:eu-repo/semantics/publishedVersion

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.

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.

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 demand-oriented 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.

Nowadays, developing coordinated optimal charging strategies for large-scale electric vehicle (EV) fleets is crucial to ensure the reliability and efficiency of power grids. This paper presents a novel fully distributed control strategy for the optimal charging of large-scale EV fleets aiming at the minimization of the aggregated charging cost and battery degradation, while satisfying the EVs' individual load requirements and the overall grid congestion limits. We formulate the optimization problem as a convex quadratic programming problem where all the EVs' decisions are coupled both via the objective function and some grid resource sharing constraints. Based on the distributed waterfilling approach, the proposed resolution algorithm requires a minimal shared information between EVs that communicate only with their neighbors without relying on a central aggregator, thus guaranteeing the EV users' privacy. The performance of the proposed approach is evaluated through numerical experiments to validate its effectiveness in achieving a global optimum while respecting the grid constraints with a favorable computational efficiency.

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&rsquo, 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.

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.

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.

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.

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.

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.

We present an automatic rescheduling algorithm for the real-time control of large-scale railway networks in case of disturbances and disruptions. The algorithm consists of four main steps, one for the offline configuration and the remaining three for the real-time control of the railway traffic. The on-line control is performed using a Model Predictive Control strategy combined with a distributed optimization algorithm and a bilevel heuristics based on MILP modelling. The method is preliminarily tested on the national Dutch railway traffic network.

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.

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.

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.

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.

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. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

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. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

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.

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. (C) 2017 Elsevier Inc. All rights reserved.

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. (C) 2017 Elsevier Ltd. All rights reserved.

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.

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.

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.

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.

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).