The increasing automation of modern systems—across industry, healthcare, mobility, and beyond—has raised the demand for human reasoning and expertise, while alleviating the burden of repetitive tasks. This transformation is driving us toward Automation 5.0, a new paradigm aimed at unleashing human potential. Recently, the development of foundation models (FMs) has reinvigorated its realization, making it both urgent and critical to explore the concept of Automation 5.0 in this new era. In this article, we define Automation 5.0, discuss its significance, and emphasize its new world, thinking, and technology with the goal of achieving knowledge automation. A framework, based on business FMs, human-oriented operating systems, and scenarios engineering, is proposed, where biological, robotic, and digital humans work together in three modes: autonomous, parallel, and expert/emergency modes. Additionally, a diverse range of its scenarios and applications are summarized and discussed, such as Manufacturing 5.0, Healthcare 5.0, and Transportation 5.0. We believe that Automation 5.0 can drive the co-evolution of productivity and production relations across all domains, propelling society toward a “Safety, Security, Sustainability, Sensitivity, Service, Smartness (6S)” future.

In a socially tense environment with rising emotional pressure, understanding the spread patterns of group emotions-particularly negative emotions-is crucial for identifying social risks. Extensive research has explored emotion contagion, often using propagation models where node state transitions rely on preset probabilities. However, these methods introduce randomness, making them less reflective of real-world dynamics by failing to capture individual node behaviors and interactions in emotional networks. To address this, our study introduces a novel approach integrating text-based emotion recognition with propagation models, reconstructing emotion contagion at an individual level. This model enhances traditional nodes with multihop agents driven by text emotion analysis, where agents record and respond to neighbors' emotional states. As a result, emotion spread becomes a deterministic process, with individualized infection rates reflecting node variability. We categorized nodes based on emotional states, creating corresponding agent types to form the dynamic agent-based emotion model (AEmo). Tests on real-world and scale-free networks show this method effectively predicts group negative emotion spread and provides insight into individual emotion evolution, validating the model's effectiveness.

Computational pathology enables the automatic tissue analysis of Whole Slide Images (WSIs), offering unmatched possibilities to capture quantitative tumor microenvironment (TME) characteristics that are essential for patients' prognosis and therapy response. The existing clinical and digital biomarkers do not encompass the morphometric features and spatial interactions between vascular networks and immunological compartment in the TME. To address this challenge, this work presents a high- throughput quantitative framework for automatic segmentation and assessment of aberrant phenotypes of blood vessels and immune cell clusters in hematoxylin & eosin-stained WSIs, to construct the Vascular-Immune Pathomic (VIPath) biomarker. For our study, we utilized three public datasets of Colon Adenocarcinoma (COAD) and Stomach Adenocarcinoma (STAD) from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) projects: TCGA-COAD, TCGA-STAD, and CPTAC-COAD. Additionally, we collected two in-house gastric cancer cohorts of 80 and 51 patients : DBGC-M0 and DBGC-M1. The VIPath biomarker was incorporated in a Cox proportional hazards model trained on the TCGA-COAD. Then, it was validated for predicting Overall Survival (OS) in TCGA-STAD, DBGC-M0, and DBGC-M1, Disease Free Survival in CPTAC-COAD and second-line therapy Progression-free Survival (PFS-2) in DBGC-M1. VIPath encompasses features from both vascular and immunological compartments interacting in the TME. Results proved that VIPath was capable to significantly stratify risk groups for OS TCGA-STAD (p=0.018), OS DBGC-M0 (p=0.029), OS DBGC-M1 (p=0.014), and PFS-2 DBGC-M1 (p$< $0.005). Furthermore, when inserted in a Cox model, it led to an improvement of C-index and R2over all other considered prognostic factors, i.e., p-TNM, ECOG, MSI, HER2.

Access to electricity is essential for socio-economic development, especially in rural areas of the Global South. Portable battery-based systems allow users to rent or recharge batteries at centralized stations powered by renewable sources, offering a viable alternative for off-grid electrification. This paper presents a methodology for designing a multi-hub battery charging system aimed at ensuring cost-effectiveness, efficiency, and scalability. The study addresses two main objectives: 1) determining the optimal number, location, and allocation strategy of households to charging hubs considering geographical and logistic constraints, and 2) estimating the daily power demand profile for each hub. To address the first goal, an integer linear programming problem is defined, while for the latter, an analysis-based Monte Carlo simulation of a discrete-time queue is proposed. The methodology is validated through simulations using data extracted from a real scenario, demonstrating its effectiveness and adaptability.

This paper presents a nonlinear model predictive control (NMPC) strategy for speed tracking in electric vehicles that accounts for the thermal degradation of the power inverter module. The method integrates a real-time damage predictor based on thermal cycling and cycle counting, enabling adaptive weighting in the NMPC cost function. This allows the controller to balance speed tracking and thermal stress, extending component lifetime based on the actual and predicted health of the power module. Simulations results using a high-fidelity vehicle model under WLTC and ARTEMIS cycles show that the damage-aware controller significantly reduces thermal stress and accumulated damage while maintaining accurate speed tracking.

Agro-voltaic (AV) systems enable the simultaneous use of land for both agriculture and solar energy generation, offering a promising solution to land-use conflicts. However, the shadows cast by solar panels present a significant challenge: while they can protect crops from excessive heat and reduce water loss, they may also limit sunlight exposure, reducing crop yield. This paper presents a method for controlling shadow placement on farmland by dynamically adjusting the azimuth and elevation angles of dual-axis solar panels. A geometric formulation is introduced to model the shadow of a rectangular panel on the ground, followed by an optimization framework that restricts the shadow to a predefined zone while ensuring minimum solar radiation within that zone over the course of a day. The proposed approach balances the goals of maximizing solar energy capture and achieving precise shadow control, using an iterative refinement method. The system is evaluated using real-world solar data from a clear day in Bari, Italy. The results demonstrate the system’s practical potential for effectively managing shading in real-world AV applications, thus enabling sustainable integration of energy generation and farming activities.

Photovoltaic (PV) technology is one of the most popular means of renewable generation, whose applications range from commercial and residential buildings to industrial facilities and grid infrastructures. The problem of determining a suitable layout for the PV arrays, on a given deployment region, is generally non-trivial and has a crucial importance in the planning phase of solar plants design and development. In this paper, we provide a mixed integer non-linear programming formulation of the PV arrays’ layout problem. First, we define the astronomical and geometrical models, considering crucial factors such as self-shadowing and irradiance variability, depending on the geographical position of the solar plant and yearly time window. Subsequently, we formalize the mathematical optimization problem, whose constraints’ set is characterized by non-convexities. In order to propose a computationally tractable approach, we provide a tight parametrized convex relaxation. The resulting optimization resolution procedure is tested numerically, using realistic data, and benchmarked against the traditional global resolution approach, showing that the proposed methodology yields near-optimal solutions in lower computational time.Note to Practitioners—The paper is motivated by the need for efficient algorithmic procedures which can yield near-optimal solutions to the PV arrays layout problem. Due to the strong non-convexity of even simple instances, the existing methods heavily rely on global or stochastic solvers, which are computationally demanding, both in terms of resources and run-time. Our approach acts as a baseline, from which practitioners can derive more elaborate instances, by suitably modifying both the objective function and/or the constraints. In fact, we focus on the minimum set of necessary geometrical (e.g., arrays position model), astronomical (e.g., irradiance variation), and operational (e.g., power requirements) constraints which make the overall problem hard. The Appendices provide a guideline for suitably choosing the optimization parameters. All data and simulation code are available on a public repository at: https://github.com/nicomignoni/pvlayout.git.

The demand for safe and ergonomic workplaces is rapidly growing in modern industrial scenarios, especially for companies that intensely rely on Human-Robot Collaboration (HRC). This work focuses on optimizing the trajectory of the end-effector of a cobot arm in a collaborative industrial environment, ensuring the maximization of the operator’s safety and ergonomics without sacrificing production efficiency requirements. Hence, a multi-objective optimization strategy for trajectory planning in a safe and ergonomic HRC is defined. This approach aims at finding the best trade-off between the total traversal time of the cobot’s end-effector trajectory and ergonomics for the human worker, while respecting in the kinematic constraint of the optimization problem the ISO safety requirements through the well-known Speed and Separation Monitoring (SSM) methodology. Guaranteeing an ergonomic HRC means reducing musculoskeletal disorders linked to risky and highly repetitive activities. The three main phases of the proposed technique are described as follows. First, a manikin designed using a dedicated software is employed to evaluate the Rapid Upper Limb Assessment (RULA) ergonomic index in the working area. Next, a second-order cone programming problem is defined to represent a time-optimal safety compliant trajectory planning problem. Finally, the trajectory that ensures the best compromise between these two opposing goals –minimizing the task’s traversal time and maintaining a high level of ergonomics for the human worker– is computed by defining and solving a multi-objective control problem. The method is tested on an experimental case study in reference to an assembly task and the obtained results are discussed, showing the effectiveness of the proposed approach.Note to Practitioners—Health and safety in workplaces are business imperatives, since they ensure not only a safe collaboration between industrial machinery and human operators, but also an increased productivity and flexibility of the entire industrial process. Hence, investing in health is a real driver for business growth. The key enabling technologies of Industry 4.0, such as collaborative robotics, exoskeletons, virtual and augmented reality, require standardization and indispensable technical safety requirements that cannot ignore physical, sensory, and psychological peculiarities of the human worker and aspects like usability and acceptability of these technologies in performing their activities. Against this ongoing industrial challenge, the aim of this paper is to provide researchers and practitioners with an innovative HRC trajectory planning methodology focused on enhancing production efficiency while respecting the SSM ISO safety requirement and guaranteeing the ergonomic optimal position of the operator during an assembly task. Therefore, the proposed methodology can be a convenient solution to be deployed in industrial companies, since it can support human operators by drastically reducing work-related musculoskeletal disorders and augmenting their performance in the working environment.

The design of the layout of Vertical Lift Module (VLM) warehouses is a non-trivial process that involves selecting dimensions, internal configuration, and allocation of each tray to avoid space loss while satisfying logistic constraints. Our contribution in this context is a two-phase matheuristics –an algorithm that combines exact mathematical methods and heuristics– to simplify the design of VLMs layout. The proposed matheuristics relies on three Mixed-Integer Linear Programming models, addressing the internal configuration of trays and the allocation of trays into columns based on industrial logistic constraints. This approach requires as input parameters the items features, predetermined tray types with different dimensions, matheuristic settings, and a priority rule for tray allocation. The algorithm outputs to the logistics operator types and quantities of trays needed, internal partitioning, item positions in each tray, and tray positions in each column. Extensive testing demonstrates the effectiveness of our approach under realistic scenarios. Additionally, we introduce a comprehensive set of priority rules for allocating trays into columns, providing a comparison to assist logistics operators in selecting the most suitable for specific scenarios. Note to Practitioners—VLMs are closed structures composed of columns that house a variable number of sliding trays and a lift-mounted module that handles the trays. Their operation revolves around the so-called “goods to the man” principle, where goods are automatically brought to the operator using a dedicated access bay. This design ensures that stored items are easily accessible to operators, reducing time for order picking and improving workplace safety and ergonomics. Designing a VLM for logistics companies is a complex and time-consuming task since it requires optimizing a target objective while satisfying a large set of constraints. The current manual approach lacks automated methods and relies on experienced operators and iterative improvement processes. Our two-phase matheuristic algorithm automates VLMs layout configuration, addressing tasks from item placement to tray allocation within columns. The algorithm is versatile, considering various practical and logistic constraints, making it applicable in warehouse design decision support systems or engineering software.

In distribution logistics, the planning of vehicles’ routes and vehicles’ loads are traditionally managed separately, despite these activities are correlated. This often leads to various re-designs to make the routes and load plans compatible and applicable in practice. Moreover, the planned routes, which are static by definition, cannot always cope with unexpected events. Traffic congestion, vehicle failures, adverse meteorological conditions, and further undesired events can make the planned routes inapplicable and requirevehicles’ re-routing. This results in lower service levels, undesired delays, and higher costs for logistics companies. With the aim of overcoming the above limitations, this work proposes a novel approach based on a matheuristic algorithm that jointly solves the problem ofdelivery planninganddynamic vehicle routingto automate the delivery process in a logistics 4.0 perspective. The presented algorithm includes two different phases: the static phase, which is executed offline and in advance with respect to the delivery day, and the dynamic phase, which is executed in real-time to cope with unexpected events during the delivery. For the first phase, a matheuristic approach is defined to efficiently solve the combined vehicle routing and loading problems. Differently, for the second phase, a genetic algorithm is proposed to re-route vehicles in real-time, considering both the redefinition in real-time of the nominal trip and/or of the sequence of the customers to be visited. The algorithm is tested both on a literature benchmark and on a real dataset provided by an Italian logistics company. The obtained results show that, on the one hand, the proposed algorithm can automatically provide feasible solutions that minimise travel costs, total travelled distance, and empty space on the vehicles; on the other hand, it can ensure in real-time effective re-routing solutions in case of unexpected events occurring during delivery.Note to Practitioners—This work is motivated by the need for facilitating the operations of planning and routing deliveries in the external logistics sector. We propose an algorithm that automatically generates feasible routing and loading plans for a set of Transport Units (TUs) (i.e., the static phase), and then updates in real-time the nominal route in case of unexpected events (i.e., the dynamic phase). More specifically, the first phase of the algorithm takes as input the set of different clients, the list of products packed into bins (i.e., standard packing units) to be delivered to each client, and the set of transport units available for the deliveries, and provides as output the number and type of TUs to be used, the composition of the bins in each transport unit, and the corresponding route, while optimising the space occupation in each TU and the travel costs. The second phase, instead, takes as input the nominal routes computed in the first phase and, in case of unexpected events (e.g., accidents, slowdowns, etc.) affecting one or more routes, it re-routes the involved trucks guaranteeing the maximum efficiency in regards to travel cost, travel time, and quality of service. The adoption of this algorithm by logistic companies supports the automation of the delivery process and drastically improves the efficiency of logistic operations, with particular regard to the number of used TUs, costs, safety of goods, and customers’ satisfaction.

The growing global population and the increasing scarcity of arable land highlight the urgent need for reliable and efficient food production systems. With their controlled environments, vertical farms (VFs) offer a promising solution for sustainable food security. Nevertheless, their high energy demands call for innovative approaches to optimize energy consumption while maintaining optimal growing conditions. This paper introduces a novel control-oriented model for VFs, capturing the interactions between crop growth conditions and energy consumption. To address the high energy demand of VFs, the model is integrated into a dynamic energy market characterized by time-varying energy prices and a demand response scheme, which includes a discrete reward to encourage flexible energy consumption. Then, centralized and decentralized receding horizon control approaches are proposed to minimize the energy cost of the VF while ensuring optimal crop growth. Experimental evaluations on real systems of varying scales demonstrate the effectiveness of the proposed approaches in reducing costs and ensuring sustainable agricultural practices.

To this day, tasks like planning and managing warehouses remain complex. Automated storage and retrieval systems have enhanced warehousing efficiency, yet designing them optimally is still challenging, despite their importance for the efficient operation of the warehouse. This article aims to present a novel framework for automating the optimal design of vertical lift modules (VLMs), focusing on tray types, quantities, and item-tray sector assignments, based on a specified inventory list. The approach accounts for VLM’s physical, manufacturing, and ergonomic constraints to ensure a manufacturable system design. To manage computational complexity, the size of the mixed-integer problem is reduced through an exact clustering of items, sectors, and layouts. The proposed framework is tested through numerical simulations using real data from an Italian VLM manufacturer.

This study introduces a novel control framework for human-drone interaction (HDI) in industrial warehouses, targeting pick-and-delivery operations. The goals are to enhance operator safety as well as well-being and, at the same time, to improve efficiency and reduce production costs. To these aims, the speed and separation monitoring (SSM) operation method is employed for the first time in HDI, drawing an analogy to the safety requirements outlined in collaborative robots’ ISO standards. The so-called protective separation distance is used to ensure the safety of operators engaged in collaborative tasks with drones. In addition, we employ the rapid upper limb assessment (RULA) method to evaluate the ergonomic posture of operators during interactions with drones. To validate the proposed approach in a realistic industrial setting, a quadrotor is deployed for pick-and-delivery tasks along a predefined trajectory from the picking bay to the palletizing area, where the interaction between the drone and a moving operator takes place. The drone navigates toward the interaction space while avoiding collisions with shelves and other drones in motion. The control strategy for the drone cruise navigation integrates simultaneously the time-variant artificial potential field (APF) technique for trajectory planning and the iterative linear quadratic regulator (LQR) controller for trajectory tracking. Differently, in the descent phase, the receding horizon LQR algorithm is employed to follow a trajectory planned in accordance with the SSM, which starts from the approach point at the border of the interaction space and ends in the volume with the operator’s minimum RULA. The presented control strategy facilitates drone management by adapting the drone’s position to changes in the operator’s position while satisfying HDI safety requirements. The results of the proposed HDI framework simulations for the case study demonstrate the effectiveness of the method in ensuring a safe and ergonomic HDI within industrial warehouses.

This paper introduces a novel framework for integrating solar-powered electric vehicles (SPEVs) into smart parking infrastructures, primarily focusing on optimizing energy utilization. The proposed framework relies on Model Predictive Control (MPC) to ensure efficient power flow management within smart parking infrastructures. Notably, the paper emphasizes the constraints necessary to ensure the safety and optimal performance of SPEVs and their charging requirements. Results show the effectiveness of the proposed approach, not only in preventing energy management issues but also in substantially reducing reliance on energy procurement from the grid. This integrated system contributes to a more sustainable and cost-effective energy ecosystem, representing a noteworthy advancement in electric mobility infrastructure.

This paper proposes a framework designed to optimise energy consumption in vertical farming. It aims to maximise cost efficiency by balancing between minimising system operations during the electricity price peaks and the ability to trade capacity on the FCR market while also fulfilling constraints on the internal growing process. We consider that the vertical farming system has distributed control with a series of actuators controlled by various spatially distributed PLCs that we refer to as agents, to underline their independence. The paper conducts two experiments for a lO-agent system with a Pareto controller and a lOO-agent system with a Lagrangian approach and shows the balance between more cost-efficient momentary energy consumption control.

Model Predictive Control (MPC) is an optimal control technique that employs a dynamic model of the controlled process and an optimization algorithm to determine the control strategy. Nevertheless, the cost and effort required to create and maintain dynamical models are often high, and solving the resulting optimal control problem can be computationally complex. In recent years, data-driven modeling has become an attractive alternative to approximate the behavior of dynamical systems, with the aim of alleviating these issues. However, using such models for model-based control can be challenging due to their typically nonlinear and nonconvex nature. To address these issues, we propose a recursive multi-step learning-based dynamical modeling framework to capture the temporal behavior of dynamic systems. We take advantage of Input Convex Lipschitz Neural Networks, which are explicitly designed to be convex and continuous with respect to their in-puts. We further show that these mathematical proprieties hold in a multi-step dynamical modeling framework. The proposed approach is evaluated in a real-life MPC experiment conducted in a smart building in the Samso Marina, Denmark. We show that the proposed approach keeps the internal temperature within comfort constraints while minimizing heating/cooling energy consumption.

Cuckoois a popular card game, which originated in France during the 15th century and then spread throughout Europe, where it is currently well-known under distinct names and with different variants. Cuckoo is an imperfect information game-of-chance, which makes the research regarding its optimal strategies determination interesting. The rules are simple: each player receives a covered card from the dealer; starting from the player at the dealer's left, each player looks at its own card and decides whether to exchange it with the player to their left, or keep it; the dealer plays at last and, if it decides to exchange card, it draws a random one from the remaining deck; the player(s) with the lowest valued card lose(s) the round. We formulate the gameplay mathematically and provide an analysis of the optimal decision policies. Different card decks can be used for this game, e.g., the standard 52-card deck or the Italian 40-card deck. We generalize the decision model for an arbitrary number decks' cards, suites, and players. Lastly, through numerical simulations, we compare the determined optimal decision strategy against different benchmarks, showing that the strategy outperforms the random and naive policies and approaches the performance of the ideal oracle.

Driven by the digital transformation required by Logistics 4.0, the use of automation in warehouses is constantly growing. In particular, robotic palletizers offer significant potential for optimizing warehouse operations, thanks to higher flexibility and throughput than traditional palletizing systems. Despite the availability of several solutions in the market, the optimal deployment of a robotic palletizer in warehouses is not straightforward: a design phase is needed to determine the most convenient configuration that ensures automatic palletizing is fully integrated into the warehouse processes. In this paper, we propose a simulation-based versatile tool for modeling and analysis purposes, aimed at supporting the design of a robotic palletizing cell in a bottom-up fashion. As a core methodology, we employ timed colored Petri nets, which allow - once the analysis on packing requirements and constraints is conducted - to rapidly model the system as a composition of basic subsystems, and implement alternative simulations to evaluate the corresponding performance and effectively benchmark the alternative configurations. The proposed approach is applied to a real case study, showing its effectiveness in identifying the solution that achieves a good compromise between the use of resources and the performance of warehouse operations.

Last-mile delivery is one of the most discussed problems of the last decade due to the growing importance of e-commerce and the development of Industry 4.0. In particular, this problem regards the delivery of parcels from the warehouse to the final customers. In order to bring efficiency and innovation, in this paper a hybrid delivery architecture is considered, which takes advantage of the combined use of a drone and a truck to perform a sequence of pick-ups and deliveries, and the problem of optimal control of the drones' missions is addressed. The reference scenario is the smart city where the drone of the hybrid delivery architecture is in charge of three different pick-up and delivery missions: truck to point (i.e., pick-up from the truck and delivery to the customer), point to point (i.e., delivery to a customer and pick-up from the subsequent customer), and point to truck (i.e., reentry from a customer to the truck). From the control point of view, the drone is optimally guided in all the operating modes, i.e., ascent and descent from/to truck mode, free flight mode with/without payload, and descent for pick-up/delivery mode, by a receding horizon linear quadratic regulator (LQR), which is able to manage the drone in the dynamic landing on a movable vehicle and to allow the changing in real time of the landing point on the truck. Simulation results of the truck-drone delivery architecture are presented and discussed in detail, proving the effectiveness of the proposed method.

This paper investigates a novel approach for properly modeling Hydraulic Servo Actuators (HSAs) based on ON-OFF switching valves. HSAs represent very high efficiency and small size-to-power ratio hydraulic actuators. Their functioning is guaranteed by their control system that ensures the desired flow-rate, and consequently, the proper pressure, to be provided to the actuator's chambers. Nevertheless, achieving a good model of such HSAs for control purposes is non-trivial, due to their hybrid nature inherited from the switching between the different operating modes produced by valves. To overcome this limit, we propose an average equivalent discrete-time model of the chambers' pressure dynamics related to a single control input for the digital valves. The proposed model takes inspiration from the analogy existing between hydraulic systems and power electronic converters, and guarantees the same performance as the traditional model, with the advantage of greatly simplifying the control of the servo actuator. Finally, the consistency of the proposed model with respect to its nonlinear hybrid version is proved via numerical examples.

In the last decade, distributed energy generation and storage have significantly contributed to the widespread of energy communities. In this context, we propose an energy community model constituted by prosumers, characterized by their own demand and renewable generation, and service-oriented energy storage providers, able to store energy surplus and release it upon a fee payment. We address the problem of optimally schedule the energy flows in the community, with the final goal of making the prosumers' energy supply more efficient, while creating a sustainable and profitable business model for storage providers. The proposed resolution algorithms are based on decentralized and distributed game theoretical control schemes. These approaches are mathematically formulated and then effectively validated and compared with a centralized method through numerical simulations on realistic scenarios.

This paper presents an application of human-drone interaction (HDI) for inventory management in a ware-house 4.0 that aims at improving the operators' safety and well-being together with increasing efficiency and reducing production costs. In our work, the speed and separation monitoring (SSM) methodology is applied for the first time to HDI, in analogy to the human-robot interaction (HRI) ISO safety requirements as well as the rapid upper limb assessment (RULA), for evaluating the operator's ergonomic posture during the interaction with the drone. With the aim of validating the proposed approach in a realistic scenario, a quadrotor is controlled to perform a pick and place task along a desired trajectory, from the picking bay to the palletizing area where the operator is located, avoiding collisions with the warehouse shelves by implementing the artificial potential field technique (APF) for planning and the linear quadratic regulator (LQR) and iterative LQR (iLQR) algorithms for tracking. The obtained results of the HDI architecture simulations are presented and discussed in detail proving the effectiveness of the proposed method for a safe and ergonomic HDI.

The increasing demand for freight transport requires logistic companies to improve their competitiveness by ensuring high service levels at limited costs. This paper investigates the problem of defining delivery plans with the aim to support logistic companies in reducing planning times and freight delivery costs. In delivery planning, given a set of delivery requests, both the routes and load configurations of Transport Units (TUs) are to be established. In the literature, this problem is defined as Three-dimensional Loading Capacitated Vehicle Routing Problem with Time Windows (3LCVRPTW). However, these problems are generally tackled separately and referred to as the vehicle routing problem and the container loading problem, respectively. Moreover, only a few contributions present solution approaches for real logistic systems, and these methods are mainly based on heuristics. In this work, we define a novel matheuristic algorithm for the integrated solution of the vehicle routing problem and container loading problem. The proposed method is suitable for real logistic applications and combines the advantages of exact solutions with the rapidity of heuristics. The approach aims at minimizing the total travel costs and the clients’ time windows violations in the routes’ definition, while optimizing the configuration of the cargo inside each TU. The developed matheuristic algorithm is tested both on a well-known literature benchmark and on a real dataset provided by the Italian company Elettric80. The obtained results show that the proposed method succeeds in determining in a short computational time both feasible routes and loading plans, minimizing the related costs while fulfilling logistics constraints.

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.Note to Practitioners—This work is motivated by the emerging need for effective energy management approaches in multi-carrier microgrids. The inherent difficulty of scheduling simultaneously the operations of various energy infrastructures (e.g., electricity, natural gas) is exacerbated by the inevitable presence of uncertainties that affect the inter-dependent dynamics of different energy resources and equipment. The proposed robust MPC-based control strategy allows the energy manager to effectively determine an optimal energy scheduling of multi-faceted system components, making a tradeoff between performance and protection against data uncertainty. The presented strategy is comprehensive and generic, as it can be applied to different microgrid frameworks integrating various types of system components and sources of uncertainty, while at the same time being implementable in any energy management system.

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.

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.Note to Practitioners—This work is motivated by the emerging need for the effective implementation of the zero-defect manufacturing paradigm in the Industry 4.0 framework. Especially in the deep drawing process, various quality issues in stamped parts can lead to significant product waste and manufacturing inefficiencies. This turns into considerable economic losses for companies, particularly in the automotive sector, where deep drawing is one of the most used cold sheet metal forming techniques. In most applications, only sample inspections are performed on batches of finished-product, with subsequent losses of time and resources. For the sake of improving the workpiece quality, innovative strategies for real-time process control represent a viable and promising solution. In this context, the proposed MPC-based process control approach allows the correct shaping of the metal sheet that is getting deformed during the forming stroke, thanks to the draw-in monitoring at various locations around the die. The draw-in is indeed one of the most effective forming variables to control in order to provide a correct BHF during the forming stroke. A useful and easy-to-implement non-linear metal sheet deep drawing process model is provided by this paper to perform an innovative process control strategy. A comprehensive methodology is applied in detail to an automotive case study, ranging from process modeling (model identification and validation based on experimental data acquisition) to MPC implementation (controller tuning and testing and software-in-the-loop system validation). The presented method can be easily implemented on any real deep drawing press, providing the multivariable constrained process with a suitable control system able to make the stamped parts well formed.

We are pleased to present this Special Issue of TASE, including 12 extended articles selected from the technical program of the 2020 International Conference on Automation Science and Engineering (CASE2020). CASE2020 was held virtually due to the COVID19 pandemics, August 20–21, 2020, and was originally scheduled in Hong Kong, China. CASE is an offspring of TASE and is the flagship automation conference of the IEEE Robotics and Automation Society, constituting the primary forum for cross-industry and multidisciplinary research in automation. The 2020 CASE theme was Automation Analytics, a global challenge emphasized at the conference by several invited and regular sessions, as well as specific workshops.

Improving the efficiency and sustainability of distribution networks (DNs) is nowadays a challenging objective both for large networks and microgrids connected to the main grid. In this context, a crucial role is played by the so-called network reconfiguration problem, which aims at determining the optimal DN topology. This process is enabled by properly changing the close/open status of all available branch switches to form an admissible graph connecting network buses. The reconfiguration problem is typically modeled as an NP-hard combinatorial problem with a complex search space due to current and voltage constraints. Even though several metaheuristic algorithms have been used to obtain—without guarantees—the global optimal solution, searching for near-optimal solutions in reasonable time is still a research challenge for the DN reconfiguration problem. Facing this issue, this article proposes a novel effective optimization framework for the reconfiguration problem of modern DNs. The objective of reconfiguration is minimizing the overall power losses while ensuring an enhanced DN voltage profile. A multiple-step resolution procedure is then presented, where the recent Harris hawks optimization (HHO) algorithm constitutes the core part. This optimizer is here intelligently accompanied by appropriate preprocessing (i.e., search space preparation and initial feasible population generation) and postprocessing (i.e., solution refinement) phases aimed at improving the search for near-optimal configurations. The effectiveness of the method is validated through numerical experiments on the IEEE 33-bus, the IEEE 85-bus systems, and an artificial 295-bus system under distributed generation and load variation. Finally, the performance of the proposed HHO-based approach is compared with two related metaheuristic techniques, namely the particle swarm optimization algorithm and the Cuckoo search algorithm. The results show that HHO outperforms the other two optimizers in terms of minimized power losses, enhanced voltage profile, and running time.Note to Practitioners—This article is motivated by the emerging need for effective network reconfiguration approaches in modern power distribution systems, including microgrids. The proposed metaheuristic optimization strategy allows the decision maker (i.e., the distribution system operator) to determine in reasonable time the optimal network topology, minimizing the overall power losses and considering the system operational requirements. The proposed optimization framework is generic and flexible, as it can be applied to different architectures both of large distribution networks (DNs) and microgrids, considering various types of system objectives and technical constraints. The presented strategy can be implemented in any decision support system or engineering software for power grids, providing decision makers with an effective information and communication technology tool for the optimal planning of the energy efficiency and environmental sustainability of DNs.

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 doubtcollaborative 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.Note to Practitioners—The design and development of manufacturing systems are experiencing substantial changes towards full automation. This ongoing challenge is being tackled by academia and industrial practitioners with the adoption of collaborative robots, where the skills and peculiarities of humans (e.g., intelligence, creativity, adaptability, etc.) and robots (e.g., flexibility, pinpoint accuracy, tirelessness, etc.) are combined to better perform a variety of tasks. Nevertheless, due to their different characteristics, there is an emerging need for designing suitable decision and control techniques to ensure a safe and ergonomic HRC, while keeping the highest level of productivity. Against this background, the aim of this paper is to provide researchers and practitioners with a reference source in the related field, which can help them designing and developing suitable solutions to control problems in safe, ergonomic, and efficient collaborative robotics.

This special section of the IEEE Transactions on Automation Science and Engineering (T-ASE) focuses on new models, methods, and technologies for energy efficiency and sustainability in transportation and energy systems. In this section, the focus is thus on articles considering sustainable transportation, such as electric vehicles (EVs), integrated with the smart grid requirements. As guest editors, we are very pleased to present the selected 12 papers, whose topics are specifically related to optimal planning of charging stations (CSs), sustainable transportation and mobility, EVs integration in smart grids, reliability, reduction of consumption, demand response and smart grid modeling, optimal scheduling, routing and charging of fleets of EVs, as well as smart parking.

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.Note to Practitioners—This article is motivated by the emerging need for developing effective methods to support policymakers in mitigating the effects of the COVID-19 pandemic. The proposed feedback control strategy—combining a multiregion epidemiological model with a nonlinear stochastic model predictive control approach—allows the robust identification of the most effective restrictive measures considering the corresponding effects on the healthcare and socioeconomic systems. The proposed framework is a general and flexible method that can be applied to various real scenarios, leveraging mobility data, available from the Google mobility service, to recognize patterns and predict future behaviors of individuals.

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.Note to Practitioners—This work is motivated by the intention of facilitating the transition from Logistics 3.0 to Logistics 4.0 by providing an effective tool to automate bin packing, suitable for automated warehouses. On the one hand, the proposed technique provides stable and compact bin configurations in less than half a minute per bin on average, despite the high computational complexity of the 3D-SBSBPP. On the other hand, the approach allows to consider compatibility constraints for the items (e.g., final customer and category of the items), and the use of robotized layer picking in automated warehouses. In effect, layers composed by only one type of items (i.e., monoitem layers) can be directly picked and placed on the pallet by a robotic arm without the intervention of any operator. Consequently, the adoption of this approach in warehouses could drastically improve the efficiency of the packing process.

This paper focuses on the optimal scheduling of the charging and discharging strategies of a community energy storage (CES) system, which is shared by the prosumers belonging to a grid-connected energy community. The prosumers own renewable energy sources (RESs), while they can buy/sell their energy imbalance directly from/to the power grid. For the sake of increasing the penetration of RESs and reducing the operating cost, prosumers leverage on the shared CES: in particular, each user can only employ a portion of the overall CES charge/discharge profile. Differently from the related literature, where storage devices are individually owned and the battery degradation aspects are typically disregarded, we propose a novel control mechanism based on noncooperative game theory, which allows users to minimize their energy cost as well as concur on the CES resources allocation with minimal-degradation. The effectiveness of the method is validated through numerical experiments on a realistic case study, where a shared CES supplies energy to the local community of residential prosumers. Finally, the comparison with a centralized control approach shows that the proposed framework allows all prosumers to achieve a fair cost-optimal utilization of the shared CES.

Smart microgrids are experiencing an increasing growth due to their economic, social, and environmental benefits. However, the inherent intermittency of renewable energy sources (RESs) and users' behavior lead to significant uncertainty, which implies important challenges on the system design. Facing this issue, this article proposes a novel robust framework for the day-ahead energy scheduling of a residential microgrid comprising interconnected smart users, each owning individual RESs, noncontrollable loads (NCLs), energy- and comfort-based CLs, and individual plug-in electric vehicles (PEVs). Moreover, users share a number of RESs and an energy storage system (ESS). We assume that the microgrid can buy/sell energy from/to the grid subject to quadratic/linear dynamic pricing functions. The objective of scheduling is minimizing the expected energy cost while satisfying device/comfort/contractual constraints, including feasibility constraints on energy transfer between users and the grid under RES generation and users' demand uncertainties. To this aim, first, we formulate a min-max robust problem to obtain the optimal CLs scheduling and charging/discharging strategies of the ESS and PEVs. Then, based on the duality theory for multi-objective optimization, we transform the min-max problem into a mixed-integer quadratic programming problem to solve the equivalent robust counterpart of the scheduling problem effectively. We deal with the conservativeness of the proposed approach for different scenarios and quantify the effects of the budget of uncertainty on the cost saving, the peak-to-average ratio, and the constraints' violation rate. We validate the effectiveness of the method on a simulated case study and we compare the results with a related robust approach. Note to Practitioners-This article is motivated by the emerging need for intelligent demand-side management (DSM) approaches in smart microgrids in the presence of both power generation and demand uncertainties. The proposed robust energy scheduling strategy allows the decision maker (i.e., the energy manager of the microgrid) to make a satisfactory tradeoff between the users' payment and constraints' violation rate considering the energy cost saving, the system technical limitations and the users' comfort by adjusting the values of the budget of uncertainty. The proposed framework is generic and flexible as it can be applied to different structures of microgrids considering various types of uncertainties in energy generation or demand.

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.

A characteristic of social networks is the ability to quickly spread information between a large group of people. The widespread use of online social networks (e.g., Facebook) increases the interest of researchers on how influence propagates through these networks. One of the most important research issues in this field is the so-called influence maximization problem, which essentially consists in selecting the most influential users (i.e., those who are able to maximize the spread of influence through the social network). Due to its practical importance in various applications (e.g., viral marketing), such a problem has been studied in several variants. Nevertheless, the current open challenge in the resolution of the influence maximization problem still concerns achieving a good trade-off between accuracy and computational time. In this context, based on independent cascade modeling of social networks, we propose a novel low-complexity and highly accurate algorithm for selecting an initial group of nodes to maximize the spread of influence in large-scale networks. In particular, the key idea consists in iteratively removing the overlap of influence spread induced by different seed nodes. The application to several numerical experiments based on real datasets proves that the proposed algorithm effectively finds practical near-optimal solutions of the addressed influence maximization problem in a computationally efficient fashion. Finally, the comparison with the state of the art algorithms demonstrates that in large scale scenarios the proposed approach shows higher performance in terms of influence spread and running time.

This paper proposes a novel robust decentralized charging strategy for large-scale EV fleets. The system incorporates multiple EVs as well as inelastic loads connected to the power grid under power flow limits. We aim at minimizing both the overall charging energy payment and the aggregated battery degradation cost of EVs while preserving the robustness of the solution against uncertainties in the price of the electricity purchased from the power grid and the demand of inelastic loads. The proposed approach relies on the so-called uncertainty set-based robust optimization. The resulting charge scheduling problem is formulated as a tractable quadratic programming problem where all the EVs' decisions are coupled via the grid resource-sharing constraints and the robust counterpart supporting constraints. We adopt an extended Jacobi-Proximal Alternating Direction Method of Multipliers algorithm to solve effectively the formulated scheduling problem in a decentralized fashion, thus allowing the method applicability to large scale fleets. Simulations of a realistic case study show that the proposed approach not only reduces the costs of the EV fleet, but also maintains the robustness of the solution against perturbations in different uncertain parameters, which is beneficial for both EVs' users and the power grid.

This article proposes a decision-making procedure that supports the city energy manager in determining the optimal energy retrofit plan of an existing public street lighting system throughout a wide urban area. The proposed decision model aims at simultaneously maximizing the energy consumption reduction and achieving an optimal allocation of the retrofit actions among the street lighting subsystems, while efficiently using the available budget. The resulting optimization problem is formulated as a quadratic knapsack problem. The proposed solution relies on a decentralized control algorithm that combines discrete dynamic programming with additive decomposition and value functions approximation. The optimality and complexity of the presented strategy are investigated, demonstrating that the proposed algorithm constitutes a fully polynomial approximation scheme. Simulation results related to a real street lighting system in the city of Bari (Italy) are presented to show the effectiveness of the approach in the optimal energy management of large-scale street lighting systems. Note to Practitioners-This article addresses the emerging need for decision support tools for the energy management of urban street lighting systems. The proposed decision-making strategy allows city energy managers and local policy makers taking retrofit decisions on an existing public street lighting system throughout a wide urban area. The presented strategy can be implemented in any engineering software, providing decision makers with a low-complexity and scalable Information and Communication Technology (ICT) tool for the optimization of the energy efficiency and environmental sustainability of street lighting systems.

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.

We are very pleased to present this Special Section of these Transactions, including six extended articles selected from the technical program of the 2017 International Conference on Automation Science and Engineering (CASE 2017), held in Xi’an, China, August 20–23, 2017. CASE is an offspring of the Transactions on Automation Science and Engineering and is the flagship automation conference of the IEEE Robotics and Automation Society, constituting the primary forum for cross-industry and multidisciplinary research in automation.

We present a novel method for supply chain network (SCN) design under uncertainty that jointly solves the candidate selection, the order allocation, and the transportation mode selection problems. In the proposed method, four steps are executed in cascade. First, a cross-efficiency fuzzy data envelopment analysis technique ranks the candidates of each SCN stage in a multiobjective perspective and under uncertain data. Second, a fuzzy linear integer programming model determines the supplies required from each actor by those belonging to the subsequent SCN stage. This step determines the best compromise between candidates' efficiencies, estimated costs, and delivery time, considering stock levels and uncertain capacity of actors, while satisfying customers' uncertain demand. The third step evaluates the efficiency of the transportation alternatives under uncertain data to optimally plan the transport chain. Finally, the fourth step measures the performance of the designed SCN. The method provides as a result an integrated, agile, and resource-efficient design of the SCN under uncertainty. Its application to a case study shows it is effective in selecting the SCN partners, assigning the corresponding order quantities, and delivering them to customers. Validation is obtained by comparison with well-known approaches and statistical analysis.

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.

Waste management is one of the major concerns of our times. This paper investigates the main issues in waste management, the classical practices and their limitations, and highlights the recent trends in the field to identify the foremost research areas whose advancement will lead to the achievement of smart waste management systems.

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.

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.

This paper presents a real-time demand side management framework based on robust model predictive control (RMPC) for residential smart grids. The system incorporates a number of interconnected smart homes, each equipped with controllable and non-controllable loads, as well as a shared energy storage system (ESS). We aim at minimizing the users' energy payment and limiting the peak-to-average ratio (PAR) of the energy consumption while taking into account all device/comfort/contractual constraints, specifically the feasibility constraints on energy transferred between users and the power grid in presence of load demand uncertainty. We consider a quadratic cost function for energy bought from the grid. Firstly, the energy price and related constraints of the system are modeled. Then, a min-max robust problem is established to optimally schedule energy under an interval-based uncertainty set. We finally adopt model predictive control (MPC) to solve the resulting robust optimization problem iteratively over a finite-horizon time window based on the receding horizon concept. Moreover, the robustness of the proposed real-time approach against the level of conservativeness of the solution is addressed. The effectiveness of the method is validated through a simulated case study.

This paper proposes a real-time strategy based on Model Predictive Control (MPC) for the energy scheduling of a grid-connected smart residential user equipped with deferrable and non-deferrable electrical appliances, a renewable energy source (RES), and an electrical energy storage system (EESS). The proposed control scheme relies on an iterative finite horizon on-line optimization, implementing a quadratic cost function to minimize the electricity bill of the user's load demand and to limit the peak-to-average ratio (PAR) of the energy consumption profile whilst considering operational constraints. At each time step, the optimization problem is solved providing the cost-optimal energy consumption profile for the user's deferrable loads and the optimal charging/discharging profile for the EESS, taking into account forecast uncertainties by using the most updated predicted values of local RES generation and non-deferrable loads consumption. The performance and effectiveness of the proposed framework are evaluated for a case study where the dynamics of the considered residential energy system is simulated under uncertainties both in the forecast of the RES generation and the non-deferrable loads energy consumption.

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 hierarchical decision-making strategy for the energy management of a smart city. The proposed decision process supports the city energy manager and local policy makers in taking energy retrofit decisions on different urban sectors by an integrated, structured, and transparent management. To this aim, in the proposed decision strategy, a bilevel programming model integrates several local decision-making units, each focusing on the energy retrofit optimization of a specific urban subsystem, and a central decision unit. We solve the hierarchical decision problem by a game theoretic distributed algorithm. We apply the developed decision model to the case study of the city of Bari (Italy), where a smart city program has recently been launched.

As innovative energy-related technologies are rapidly developing, new elements are populating the electrical power grid, such as unpredictable renewable energies, plug-in electric vehicles (EVs), smart electrical home appliances, energy efficient homes/buildings, and distributed energy storage. All these elements induce a paradigm shift in power grids, which are evolving from producer-controlled structures to large, distributed, and customer-interactive ones, enhancing the coupling between the physical and the information layer. In order to be effectively employed on a large scale, all these elements must rely on automation, as well as on optimization methods, for the efficient management and consumption of energy resources under varying stochastic conditions. Driven by these changes, power grids have become complex and large-scale platforms with growing needs for automation and optimal energy management services, a concept that can be referred to as smart grids.

We present an advanced traveler information system (ATIS) for public and private transportation, including vehicle sharing and pooling services. The ATIS uses an agent-based architecture and multi-objective optimization to answer trip planning requests from multiple users in a co-modal setting, considering vehicle preferences and conflicting criteria. At each set of users' requests, the transportation network is represented by a co-modal graph that allows decomposing the trip planning problem into smaller tasks: the shortest routes between the network nodes are determined and then combined to obtain possible itineraries. Using multi-objective optimization, the set of user-vehicle-route combinations according to the users' preferences is determined, ranking all possible route agents' coalitions. The ATIS is tested for the real case study of the Lille metropolitan area (Nord Pas de Calais, France).

This paper focuses on applying multicriteria decision making tools to determine an optimal energy retrofit plan for a portfolio of buildings. We present a two-step decision making technique employing a multiobjective optimization algorithm followed by a multiattribute ranking procedure. The method aims at deciding, in an integrated way, the optimal energy retrofit plan for a whole stock of buildings, optimizing efficiency, sustainability, and comfort, while effectively allocating the available financial resources to the buildings. The proposed methodology is applied to a real stock of public buildings in Bari, Italy. The obtained results demonstrate that the approach effectively supports the city governance in making decisions for the optimal management of the buildings' energy efficiency.

In this paper, we present a decision support tool to optimize two of the most critical activities in intermodal railroad container terminals, in an iterative and integrated framework devoted to the terminal profit improvement. First, the model allows optimizing the freight trains composition, maximizing the company profit, while respecting physical and economic constraints, and placing in the train head/tail containers prosecuting to subsequent destinations. Hence, based on the resulting train composition, the decision support system allows optimizing the containers allocation in the terminal storage yard, in order to maximize the filling level while respecting physical constraints. The model is successfully tested on a real case study, the inland railroad terminal of a leading Italian intermodal logistics company.

This paper investigates the problem of optimally distributing the energy produced by a shared renewable energy source among users, without relying on a centralized decision maker. We assume that each user is only allowed to communicate with his neighbors and buys energy from a producer under non-linear pricing. We formulate a quadratic programming problem aimed at ensuring a social welfare-optimal allocation of the shared resource. We propose a low-complexity distributed algorithm that relies on average consensus. We show the convergence of the proposed algorithm to the unique optimal solution of the resource allocation problem. We also provide numerical simulations demonstrating that the approach allows exploiting the potential of renewable energy sources' sharing to reduce users' energy consumption costs.

This paper focuses on optimizing one of the most critical activities in door-to-door intermodal transportation, i.e., the containers' drayage by road. We present a technique to solve in an exact and optimal way the pick-up and delivery problem under the typical assumptions of intermodal transportation: full truck load, split delivery, clustered backhauls, and time windows. The method allows limiting the distance traveled by road, enabling to match a delivery with a pick-up request, while respecting customers' service time windows, vehicles availability, and rental needs. Thus, intermodal companies can manage vehicle routing and scheduling problems in an integrated way. The technique effectiveness is shown by a real case study.

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.

The paper addresses the emerging need for tools devoted to the energy governance of smart cities. We propose a hierarchical decision process that supports the energy manager in governing the smart city while addressing different urban sectors with an integrated, structured, and transparent planning. Starting from the urban control center proposed in a previous contribution for the urban energy management, a hierarchical strategic decision structure is proposed. More in detail, a two-level decentralized programming model integrates several decision making units (decision panels), each focusing on the energy optimization of a specific urban subsystem. We focus on the presentation of the street lighting decision panel and on its application to the energy management of the public lighting of the city of Bari (Italy), where a smart city program has recently been launched.

This paper focuses on the supplier selection problem and the subsequent order allocation, extending an approach originally proposed by some of the authors for supplier ranking under uncertainty. The novel method integrates the cross-efficiency Data Envelopment Analysis and the fuzzy set theory to obtain a ranking of suppliers under nondeterministic evaluation criteria. Subsequently, a fuzzy integer linear programming model allows determining the quantities to require from each supplier as a compromise between the suppliers' efficiency, procurement costs, and time required to fulfill the order, while respecting the suppliers' capacity and satisfying the customers' demand. The case study of an SME manufacturer shows the technique effectiveness.

This paper presents a train load planning technique for intermodal rail-road terminals. The proposed method aims at maximizing the train commercial value while respecting priority, physical, financial, and prosecution constraints (i.e., taking into account containers that prosecute their trip after the first destination). The approach consists of two phases: 1) modifying a previous approach by some of the authors, a linear integer programming problem is solved to maximize the train commercial value, keeping into account urgencies and priorities; 2) hence, a heuristics is used to take into account prosecuting containers and reduce the number of wagons to be re-handled. The technique is tested on a real case study and compared with the previous strategy proposed by some of the authors to show its effectiveness and ease of application.

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).

The paper addresses the emerging need of providing urban managers with tools for energy governance of smart cities. We present the architecture of a decision support system called Urban Control Center (UCC). The UCC measures the city energy performance and supports the decision maker in determining the optimal action plan for implementing smartness strategies in the city energy governance. To this aim, the UCC relies on a two-level decentralized programming model that integrates several decision making units (decision panels), each focusing on the energy optimization of a specific urban subsystem.

This paper addresses a key objective of the supply chain strategic design, i.e., the optimal selection of suppliers under uncertainty. A methodology integrating the cross-efficiency Data Envelopment Analysis and the Monte Carlo approach is proposed. Their combination allows overcoming the deterministic feature of the classical cross-efficiency DEA. Moreover, we define an indicator of the robustness of the determined supplier ranking. The resulting technique allows managing the supplier selection problem while considering nondeterministic input and output data, a significant circumstance for assessing potential suppliers, with which there are no previous commercial relationships. The approach helps buyers in choosing the right partners under uncertainty and ranking them upon a multiple sourcing strategy.

The real-time traffic management allow to solve unexpected disturbances that occur along a railway line during the normal developement of the traffic. The original timetable is restored through the rescheduling process. Despite the increase of real-time decision support tools for trains dispatchers that enable a better use of rail infrastructure, real-time traffic management received a limited scientific attention. In this paper, we deal with the real time traffic management for regional railway networks, mainly single tracks, in which a centralized traffic control system is installed. The rescheduling problem is presented as a Mixed Integer Linear Programming Model which resolution allows to carry out the rescheduling process in a very short computational time.

In this paper we present a decision support scheme to help managing and optimizing two critical activities in intermodal terminals, namely the containers allocation in the terminal yard and the freight trains composition. In particular, the focus of this paper is on the first problem and the goal is that of maximizing the utilization of the available space while keeping into account several constraints. The approach was successfully tested on a real case study, the rail-road terminal of a leading intermodal logistics company.

The paper provides general criteria and evidences for the design phase of an interoperable enterprise system. The analysis of interoperability is introduced, to characterize features and criticalities for the subsequent design actions to be undertaken. An axiomatic approach is proposed to this aim, providing general principles to be followed. A simple case study is discussed.

This paper focuses on reengineering of production warehouses with lean manufacturing. Using as a case study an Italian interior design producer, we present an integrated approach for lean warehousing. Firstly a detailed description of the warehouse logistics is provided by the Unified Modeling Language (UML), hence Value Stream Mapping (VSM) allows identifying non-value adding activities, and the Gemba Shikumi technique helps to rank such anomalies. The reapplication of VSM produces an overall picture of the optimized warehouse, and using UML we detail the reengineered warehouse processes. The approach represents a useful tool to systematically improve production warehouse management.

Due to the continuous increase of the world population living in cities, it is crucial to identify strategic plans and perform associated actions to make cities smarter, i.e., more operationally efficient, socially friendly, and environmentally sustainable, in a cost effective manner. To achieve these goals, emerging smart cities need to be optimally and intelligently measured, monitored, and managed. In this context the paper proposes the development of a framework for classifying performance indicators of a smart city. It is based on two dimensions: the degree of objectivity of observed variables and the level of technological advancement for data collection. The paper shows an application of the presented framework to the case of the Bari municipality (Italy).

We address the problem of healthcare systems performance evaluation under uncertainty by a cross-efficiency fuzzy Data Envelopment Analysis (DEA) technique. Triangular fuzzy numbers are employed to deal with uncertain data. More precisely, a fuzzy triangular efficiency is associated to each hospital/ward through a cross-evaluation by a compromise between objectives. Results are defuzzified to obtain the ranking. The method is applied to evaluate hospitals in a region of Southern Italy and estimate the temporal evolution of the performance of one of them, showing the ease of application and usefulness in validating and planning healthcare reforms.

Despite the emerging positive trend of rail freight transport, especially in intermodal contexts, the optimization of intermodal terminals is addressed only by few studies and mainly for seaport terminals. This paper fills this gap presenting a train load1 planning optimization model for intermodal rail-road terminals. The proposed model maximizes the train commercial value while respecting priority, physical, financial, and prosecution constraints (i.e., taking into account containers that prosecute to a subsequent terminal after the train destination). The presented method has been successfully tested on a real case study - the rail-road terminal of an Italian intermodal logistics company that is a leader in the European market - showing its effectiveness and ease of application.

The efficient management of hospital departments (HDs) has recently become an important issue. Indeed, the increased demand and design for hospital services have saturated the capacity of HD that requires suitable tools for the efficient use of resources and flow of patients, staff, and drugs. This paper proposes a model based on a three-level strategy to design at the tactical level in a concise and effective way the structure, the resources, and the dynamics of a critically congested HD. The design strategy is composed of three basic elements: the modeling module, the optimization module, and the simulation and decision module. The first module employs a Unified Modeling Language tool and a timed Petri net (PN) model to effectively capture the detailed flow and dynamics of patients, starting from their arrival to the HD until their discharge. The optimization module employs the fluid relaxation to concisely approximate in a continuous PN framework the HD model and optimize suitable performance indices. The simulation module verifies that the optimized parameters allow an effective workflow organization while maximizing the patient flow. In case of inconsistencies due to the fluid approximation between the continuous model used in the design phase by the optimization module and the discrete one used in the subsequent verification phase by the simulation module, the latter module revises the values of some HD model parameters. A real case study on the Emergency Cardiology Department of the General Hospital of Bari (Italy) shows the efficiency and accuracy of the proposed method.

In this paper we deal with the problem of designing an observer for Petri nets under the assumption that all transitions may be observed but there exist some uncertainties in the initial marking. In particular, the information on the initial marking is given in terms of fuzzy markings by associating a discrete membership function with each place. Some ideas on how to extend the proposed approach to the case of unobservable transitions are also discussed.

We present a novel cross efficiency fuzzy Data Envelopment Analysis (DEA) technique for supplier selection under uncertainty. In order to deal with uncertain input and output suppliers data, triangular fuzzy numbers are employed. A fuzzy triangular efficiency is associated to each supplier through a cross evaluation by a compromise between objectives. The results are defuzzified and a supplier ranking is determined. The method is applied to the evaluation of a set of candidate suppliers of an Italian SME, showing the ease of application and discriminative power among suppliers.

We present a novel lean manufacturing procedure relying on the Value Stream Mapping (VSM) tool and the Analytic Hierarchy Process (AHP) technique. The procedure is iterative and hierarchical. Starting from a detailed description of the manufacturing process by the Unified Modeling Language (UML), the VSM graphical approach allows the identification of non-value adding activities, and the AHP technique leads to a ranking of such system anomalies. The further application of the VSM tool produces an overall picture of the desired manufacturing system, and the UML framework allows to describe in detail the updated system activities. An application of the procedure to a real case study shows its effectiveness.

The paper specifies an Integrated System (IS) devoted to the management of Intermodal Transportation Networks (ITNs) to take both tactical decisions, i.e., in an offline mode, and operational decisions, i.e., in real-time. Both the resulting IS structures rely on a closed-loop approach that is able to tune the choices with the current system conditions. In either case, the core of the presented IS are a reference model and a simulation module. In particular, the reference model uses information from the real system, obtained by modern Information and Communication Technologies (ICTs) and the simulation module evaluates the impact of the management decisions. In order to obtain a systematic model suitable to describe a generic ITN, the paper proposes a metamodeling approach that describes in a thorough and detailed way the structure and the behavior of ITNs. Moreover, the metamodeling procedure is a top-down technique based on the well-known Unified Modeling Language (UML), a graphic and textual formalism able to describe systems from structural and behavioral viewpoints. In order to show the IS application at the tactical decision level, the paper specifies the IS for an ITN case study that is constituted by the port of Trieste (Italy) and the inland terminal of Gorizia (Italy). The results show how the IS can improve the performance of the ITN by applying ICT tools and information-based services.

The paper deals with a key objective of the strategic purchasing function in supply chain management, namely vendor evaluation and selection. We propose a novel formulation of the so-called Data Envelopment Analysis (DEA) technique that overcomes some known drawbacks of DEA in the application to supplier selection in the enterprise integration, New constraints are introduced in the DEA method in order to better mimic the buyer behavior. We call the resulting approach DEA-P (DEA Percentage) because it allows the decision maker to compare the different supplier evaluation criteria by assigning a percentage index expressing the importance of each criterion. A simulated case study demonstrates the effectiveness of the novel method for supplier selection optimization. This work was supported by the Cassa di Risparmio di Puglia Foundation.

The paper specifies an Integrated System (IS) devoted to efficient management and control of Intermodal Transportation Networks (ITN). The IS is designed to take both tactical decisions, in an off-line mode, and operational decisions, in real time. Both the resulting IS structures rely on a closed loop approach tuning decisions with the current system conditions. In either case, the IS core is a reference model using information from the real system, obtained by modern Information and Communication Technologies (ICT) tools, for ITN efficient planning and management purposes. To obtain a systematic model suitable to describing generic ITN, the reference model relies on a metamodeling approach that allows the thorough and detailed description of the ITN structure and behavior. The proposed metamodeling procedure is a top-down approach based on the Unified Modeling Language, a graphic and textual formalism for representing systems structure and behavior.

A supply chain (SC) is a network of independent manufacturing and logistics companies that perform the critical functions in the order fulfillment process. This paper proposes an effective and modular model to describe material, financial and information flow of SCs at the operational level based on first-order hybrid Petri nets (PNs), i.e., PNs that make use of first-order fluid approximation. The proposed formalism enables the SC designer to choose suitable production rates of facilities in order to optimize the chosen objective function. The optimal mode of operation is performed based on the state knowledge of the obtained linear discrete-time, time-varying state variable model in order to react to unpredictable events such as the blocking of a supply or an accident in a transportation facility. A case study is modeled in the proposed framework and is simulated under three different closed-loop control strategies.

The paper proposes a model for real time control of urban traffic networks. A modular framework based on first order hybrid Petri nets models the vehicle flows by a first order fluid approximation. Moreover, the lane interruptions and the signal timing plan controlling the area are described by the discrete event dynamics using timed Petri nets. The proposed model is applied to a real intersection located in Bari, Italy. Simulation of different scenarios shows the technique efficiency: validation is performed by comparison with a previously proposed alternative approach employing colored Petri nets.

This paper presents a supply chain (SC) model at the operational level based on first order hybrid Petri nets (PNs), i.e., PNs that make use of first order fluid approximation. The model addresses the issue of the management strategies that control the material flow and the inventory stocks in the SC. In particular, we apply the standard make-to-stock and make-to-order policies to a SC case study. Suitable inventory control rules manage the logistics, while optimal production rates are chosen according to a given objective function.

This paper focuses on real-time control of automated storage and retrieval systems (AS/RSs) serviced by a rail-guided vehicle system, a widely used solution for material handling in warehouses. The generic multiproduct AS/RS is modeled as a timed discrete event dynamical system, whose state provides the information on the current interactions between users and resources. Moreover, we address the real-time controller that governs resource allocations and scheduling choices by enabling and inhibiting the system events in order to avoid collisions and deadlocks. To this aim, we characterize deadlock in AS/RSs and define two deadlock resolution strategies: a deadlock avoidance and a deadlock detection/recovery policy. The proposed deadlock formulation and characterization have a general validity and can be applied to single unit resource allocation systems where a subset of users may be regarded as resources of other users. We compare the proposed control policies for a large-scale AS/RS presented in the related literature by several discrete event simulation tests.

The paper proposes an online identification strategy for discrete event systems (DES). The identifier stores a sequence of events and the corresponding output symbols. Moreover, by solving an integer linear programming problem, an identification procedure synthesizes an interpreted Petri net (IPN) modeling the DES. More precisely, we assume that the fixed numbers of places are given and that a finite sequence of transitions and the corresponding markings are completely or partially known. Moreover, the identification algorithm working in real-time identifies the IPN assuming the DES dynamics deterministic, i.e., the event occurrence from a given state yields only one new state.

An agile and environmentally conscious manufacturing paradigm refers to the ability to reconfigure a flexible system quickly, economically, and environmentally responsibly. In modern manufacturing enterprises, e-supply chains integrate Internet and web-based electronic market and are promising systems to achieve agility. A key issue in the strategic logistic planning of integrated e-supply chains (IESCs) is the configuration of the partner network. This paper proposes a single- and multiobjective optimization model to configure the network of IESCs. Considering an Internet-based distributed manufacturing system composed of different stages connected by material and information links, a procedure is presented to select the appropriate links. A set of performance indices is associated with the network links. Single-criterion and multicriteria optimization models are presented under structural constraint definitions. The integer linear programming (ILP) problem solution provides different network structures that allow to improve supply chain (SC) flexibility, agility, and environmental performance in the design process. The proposed optimization strategy is applied to two case studies describing two networks for desktop computer production.

Global competition has given rise to logistic and production systems (LPSs), that are distributed manufacturing systems integrating international logistics and information technologies with production. This paper builds upon an LPS network design model previously proposed by some of the authors. The recalled technique formulates and solves a multi-criteria optimization problem to select the partners in the different stages of the production chain and the links connecting them. In this paper, in order to rank the equally optimal Pareto solutions of such a problem, we propose to employ fuzzy multi-criteria optimization. Two fuzzification techniques and two different multi-criteria methods are considered. In addition, the methodology is illustrated by way of a case study. Moreover, a discussion on the different advantages and limitations of the proposed techniques is provided

This paper validates a colored timed Petri net (CTPN) model proposed to describe urban traffic networks. In particular, a CTPN models the dynamics of signalized traffic networks and timed Petri nets describe the traffic lights controlling the area. To this aim, the modeling framework is applied to a real intersection located in Bari, Italy. Discrete event simulations of the controlled intersection test the signal timing plan under different traffic scenarios and give a confirmation of the model capability to correctly predict the traffic performance

The industrial manufacturing environment is nowadays characterized by fierce global competition, rapid market changes and short product life cycles. Such a complex scenario originated a vast demand for sophisticated techniques guaranteeing adequate planning and control of warehouses. A widely used solution is to adopt Automated Storage and Retrieval Systems (AS/RSs). A typical AS/RS comprises a number of parallel aisles with storage racks, serviced by automated stacker cranes and rail guided vehicles. This paper compares several management strategies addressing the system operational control, i.e., dealing with the AS/RS real time behaviour. A common coloured timed Petri net models the system and the controlled AS/RS operation is highlighted by way of several discrete event simulations carried out in the Matlab-Stateflow software environment. The proposed control policies are compared and discussed on the basis of appropriate performance indices.

This paper investigates the issue of urban traffic signal control using a real time optimization model for signalized areas proposed in the related literature. The adopted model is modified to take into account the traffic scenarios, the different types of vehicles in the area, as well as pedestrians. The technique is applied to a real case study, consisting of two coordinated intersections located in the urban area of Bari (Italy). On the basis of traffic observations, optimal selection of the phases in the semaphoric cycle is performed under different congestion scenarios. Results show the ability of the strategy to minimize the vehicle queue lengths in the area.

The design of a supply chain network provides the main structure for supply chain operations, since the network is a key element in the competitiveness and investments of an extended production system. The configuration of the network is essential for business to pursue a competitive advantage. We adopt a methodology based on three layers. In the first layer, the performance of the entities candidate to join the network is evaluated and efficient elements are individuated. The second layer develops a model to configure the network. Finally, the third layer is devoted to evaluate and validate the solution proposed in the first two levels. The overall decision process is the result of the interaction of the modules dedicated to each decision layer.

We investigate the functionality of fuzzy inference connectives for control. In particular, we employ selected conjunction, implication and aggregation operators, extensively used in the specialized literature, for speed control of a DC drive. The problem is approached from a practical perspective: we analyze the effect of the different connectives on the rise time performance index of the speed response obtained both in simulation and laboratory tests. Rather than the mere optimization of the DC drive, the object of the paper is the analysis of the key fuzzy controller (FC) features, in order to carry out an in-depth study of the FC operation and take full advantage of its potential.

Swinging up an inverted pendulum is a common benchmark task for the investigation of automatic control techniques. In this paper we introduce a new Fuzzy Sliding Mode (FSM) technique for swinging-up an inverted pendulum and controlling the connected cart, while minimizing the swing-up time, the cart travel and the required control action. The FSM technique adopted is based on a piecewise linear sliding manifold that is bent towards the far off zones of the pole phase plane, thus enabling the reduction of the control action. Further, in order to limit the cart overshoot two variable gains were inserted in the cart controller. We tested our technique both on a nonlinear model, including friction, and on a lab equipment: we report both on the upwards stabilization and swing-up. The pendulum upwards equilibrium point was made globally stable.