Fault diagnosis in internet of things systems, where multiple distributed components interact asynchronously through communication buffers, poses significant challenges due to system scalability and communication uncertainties. To address this, this paper studies the problem of diagnosability consistency in a discrete event system modeled using a labeled Petri net composed of several interconnected subnets via buffer places. Due to the state explosion problem, the diagnosability analysis by a centralized approach for large-scale systems is computationally demanding and sometimes even impossible. In this work, we assume that Petri net modules are connected through buffer places according to predefined rules and do not share transitions or resources, offering a complementary and computationally efficient alternative to existing modular approaches for large-scale systems. The diagnosability of subnets is analyzed with a particular automaton, called an unfolded verifier, by determining whether there exists a fundamental path that leads to the violation of the diagnosability. The proposed approach investigates the diagnosability of large systems with modular structures (namely global diagnosability), without constructing a global unfolded verifier, by analyzing the diagnosability of each module only (namely local diagnosability). More precisely, the consistency between the local diagnosability and the global diagnosability is addressed by determining whether all the fundamental paths of subnets survive in the global net due to the composition of subnets. Finally, an algorithm is given to deduce the diagnosability of a monolithic system. Compared with the existing centralized approaches, the complexity is practically mitigated by using the proposed one.

This study presents a Digital Twin (DT)-based system to solve the last-mile delivery problem. The city is divided in zones, each zone is associated to a hub where the trucks arrive and the last-mile deliveries are performed by a set of robots. The DT architecture is composed of an application layer that optimize the robot initial routes, a simulation layer connected with the physical layer to manage unexpected events, such as traffic congestion and road closures. A Mixed Integer Linear Programming (MILP) optimization model is designed to determine the optimal routes of the robots and a simulation model is implemented for the rerouting application. The system is validated through two case studies conducted in the city of Bari, Italy, using the MILP model to reduce travel distances and energy consumption of robots and the Simulation of Urban Mobility tool for reproducing the DT physical layer. The results demonstrate the effectiveness and adaptability of the proposed approach.

This paper addresses the diagnosability analysis problem under malicious attacks of a networked discrete event system modeled by a labeled Petri net. Astealthy replacement attackis considered to alter or corrupt the observation of the system, in which the transition labels are replaced by others or the empty string, and its attack stealthiness requires that the corrupted observations should be contained in the behavior of system. The objective of this work is, from an attacker’s viewpoint, to design such an attack for compromising theattack-induced diagnosabilityof a system. Specifically, a new structure, called an attack verifier, is constructed by integrating the attack behavior to enumerate all the attack paths to be transformed into fundamental ones that violate the attack-induced diagnosability. Then an optimal attack synthesis problem in terms of minimum energy cost is formulated by integer linear programming problems. An example of an automated manufacturing system is provided to show the efficiency of the proposed attack strategy compared with the existing approaches.

This work investigates the use of deep reinforcement learning to address the scheduling problem of identifying minimal control sequences while ensuring deadlock avoidance in transition-timed Petri nets. Traditional control strategies often depend on exhaustive search algorithms or heuristics, both of which tend to be computationally intensive and exhibit poor scalability as system complexity increases. In this study, we formulate the control sequence scheduling task as a Markov decision process and adopt the Deep Q-Network framework to learn control policies through interaction with an integrated timed Petri net simulation environment. The reward function is specifically designed to minimize total execution time, with penalties applied to extended durations, thereby guiding the learning process toward efficient and safe behaviors. In addition, we evaluate the robustness of the learned policy under structural perturbations (i.e., failed transitions) and temporal variations via scaled firing delays. Experimental results indicate that the proposed method consistently identifies control sequences with shorter makespans when compared to conventional approaches, offering significant improvements in both runtime and computational efficiency. Moreover, once the learned policy generalizes effectively, it can be reused to quickly generate feasible control sequences even under moderate perturbations, without requiring retraining. The comparative analysis further highlights these advantages, demonstrating the potential of the proposed reinforcement learning-based framework as a practical and scalable solution for optimizing control strategies in complex timed Petri net models. Two case studies on manufacturing systems are used to illustrated the efficiency of the proposed strategy.

The recent need of supporting the diffusion of electric mobility around the world to progressively substitute petrol transport means, leads to the development of new hardware and software technologies to make even more convenient the use of electric vehicles (EVs). High purchasing costs and long recharging times are two major factors slowing this transition. In addition, from the end users perspective, using EV in long distance journeys is still not convenient despite the increasing diffusion of fast charging infrastructures. In this context, to facilitate traveling with EVs in long distance trips, this paper proposes a trip planner prototype based on Deep Reinforcement Learning (DRL). The trip planner prototype has the goal to suggest to the drivers the best charge stops to be performed during the trip according to the user needs and preferences. Charging stops are optimized, using the available Charging Points (CPs) along the route from origin to destination, and are shown to the user on a map taking into account important information like the EV State of Charge (SoC), the cruise velocity, and the presence of point of interest (e.g. restaurant, hotel, shops, etc.) close around. The trip plan can be done according to three objectives: minimizing the travel time, minimizing the charging costs, optimizing travel time and cost. The proposed DRL approach is compared against Genetic Algorithm (GA), heuristic, and optimization approaches considering a real-world EV trip.

The integration of Autonomous Vehicles (AV) into existing transportation systems presents significant challenges, including safety, infrastructure adaptation, and public acceptance. To address these issues, a structured risk assessment framework is essential for guiding decision-making processes. This paper studies the application of the Analytic Hierarchy Process (AHP) as a decision-support tool for evaluating the risk associated with AV integration. Focusing on three critical risk categories - hazard, vulnerability, and exposure - this study uses AHP to assess and prioritize risks. A case study based on the IN2CCAM European project is presented to evaluate the potential impact of AVs in both urban and extra-urban traffic environments. Key Performance Indicators are identified, and the relative weights of various risk factors are determined. The results indicate that the hazard category has the greatest impact on the integration of AVs, with the probability of accidents being the most critical factor. However, social acceptance, production costs and charging infrastructure limitations also play an important role in determining the feasibility of AV adoption.

Last-mile delivery remains one of the key challenges in modern logistics, especially given the growing demand for fast and efficient transport solutions. This study evaluates two delivery strategies: autonomous aerial drone delivery with one parcel per trip and autonomous terrestrial robot delivery with the ability to transport multiple parcels per trip. Simulation modeling is used to analyze the economic, environmental, and social aspects of these methods. The study applies Simulation of Urban Mobility software to simulate the performance of autonomous terrestrial robots in an urban environment taking into account traffic, and a mathematical model to evaluate the performance of autonomous aerial drones. Three scenarios are considered: (1) drone-only delivery, (2) robot-only delivery, and (3) mixed fleet approach combining both methods. The results show that autonomous aerial drones provide faster delivery, but autonomous terrestrial robots are a more economical and environmentally sustainable solution. The hybrid approach strikes a balance between efficiency and cost, optimizing last-mile logistics. This study contributes to the development of sustainable urban delivery models and provides practical recommendations to policy makers and logistics companies.

Autonomous vehicles (AVs) continuously generate high-resolution sensor data on road conditions, infrastructure updates, and traffic dynamics. Despite their critical relevance for real-time navigation and urban planning, these datasets remain siloed within manufacturer-specific platforms. Motivated by the necessity to overcome such fragmentation, this paper introduces a novel decentralized, blockchain-based framework whose key innovation is a dynamic voting threshold integrated into a modular smart contract architecture. In our model, AVs can submit and validate road events –such as newly detected closures or construction sites– through a modular smart contract system employing dynamic voting thresholds that adapt acceptance criteria based on different factors. This allows urgent changes to achieve consensus while quickly minimizing malicious or erroneous reporting. Upon reaching a consensus regarding the specific event, the proposer is granted token-based incentives redeemable for operational cost reductions (e.g., charging or parking discounts). The proposed approach is validated via a Hardhat simulation on an Ethereum Virtual Machine compatible test network, demonstrating our design’s feasibility, robustness, and responsiveness under diverse scenarios.

In the age of intelligent transportation systems and smart cities, teleoperated driving aims to bridge the gap between human and fully autonomous driving. However, the reliability of teleoperated driving is heavily dependent on the quality of the cellular networks, a limitation that could be addressed by 6G networks, which aims to enhance ultralow latency and high reliability. This study proposes an integrated simulator for teleoperated driving by utilizing Deep Reinforcement Learning (DRL) in a framework of 6G and Network Digital Twin. The presented simulation framework combines different tools (i.e., SUMO, OMNeT++, and Simu5G) to model realistic traffic and network dynamics. In addition, the Random Forest algorithm is used for the coverage prediction system and maintaining stable connectivity, and a DRL model optimizes vehicle routing by balancing path length and signal coverage. A case study is simulated considering the city of Bari (Italy). The framework demonstrates robust communication between teleoperated vehicles and 6G Digital Twin infrastructure.

Vehicular Ad Hoc Networks (VANETs) play a vital role in enabling Intelligent Transportation Systems (ITS) by allowing communication between vehicles and between vehicles and infrastructure. However, these networks are vulnerable to various attacks that can threaten the integrity and safety of the network. One major attack is the Sybil attack, where malicious actors create multiple fake identities to confuse the network and disrupt normal communication and activities. In this work, we develop a real-time detection framework based on machine learning (ML) that processes data generated in real time from simulations using OMNeT++, Veins, and Simulation Urban Mobility frameworks. Our approach leverages four ML models: Random Forest, Gradient Boosting, XGBoost, and LightGBM, along with a stacking ensemble model to enhance detection accuracy. The proposed models are periodically trained on batches of data collected during the simulation, enabling continuous learning. Adaptive training strategies and a web-based dashboard enable continuous monitoring and effective detection of Sybil attacks. Notably, the simulation successfully replicates realistic Sybil attack scenarios and yields a new labeled dataset, which can support future research in this area. Our results demonstrate that the framework effectively detects Sybil attacks in dynamic vehicle networks, highlighting its potential to enhance security in ITS.

This paper introduces an innovative approach to designing a user-based Heating, Ventilation, and Air-Conditioning (HVAC) system management connected with the District Energy Management System. By classifying the users into dynamic energy consumption classes to reward energy efficiency and penalize excessive use, users can modify their behavior to pass to a less expensive and more virtuous consumption class. To this aim, a blockchain platform determines the rewards and penalties and, by a K-means clustering algorithm, categorizes users into respective groups. Then, a Class Follower Problem is formulated and solved by a Model Predictive Control (MPC) strategy integrated with a Long Short-Term Memory network as a predictive model. If the users follow the suggestions proposed by the controller, i.e., the thermostat set-points and the time intervals in which the HVAC system must be switched off or on, the users can be located in a more virtuous consumption class. A case study conducted within an energy district in Bari (Italy) shows how the proposed architectural framework tuned thermal regulation in intelligent buildings while concurrently achieving energy optimization.Note to Practitioners—This paper addresses the challenge of efficiently managing HVAC systems in smart districts through a novel blockchain-based framework and an optimization strategy solved by an MPC approach. The objective is to incentivize users to optimize their energy consumption by introducing dynamic Consumption Classes that reward energy efficiency and penalize inefficient utilization. For practitioners, this strategy translates to a granular level of energy management that not only adapts to individual behaviors but also aligns with broader sustainability goals. Integrating the blockchain platform ensures a transparent and secure method for managing and recording energy usage. At the same time, adopting MPC with Long Short-Term Memory Networks offers accurate forecasts and adjustments to enhance system responsiveness. Although the study focuses on HVAC systems, the principles may be extended to other energy-intensive applications, providing a comprehensive tool for energy management and user engagement in smart cities. Future research could integrate renewable energy sources and explore the implications of user-driven adjustments on the overall energy distribution and efficiency.

This paper addresses two important smart city logistics problems, i.e., Postal Delivery and Waste Collection, using Electric Vehicle Routing Problems. To this aim two Mixed Integer Linear Programming problems are formulated with the objective of carrying out the collection or delivery activities by minimizing the route length, respecting the working time, and considering the Electric Vehicles (EVs) battery charge constraints. While satisfying the customer needs under the mentioned traveling constraints, the proposed models take into account the implementation of smart charging strategies to minimize the demand peaks on the power grid both at district and charge station levels, that is suitable in large scale problems. To address the complexity of the models, a heuristic algorithm implementing clustering and routing strategies is proposed. Two case studies are implemented to demonstrate the effectiveness of the proposed models for Postal Delivery and Waste Collection activities in large systems.

The rapid advancement and deployment of Autonomous Vehicles (AVs) necessitate innovative solutions for reliable and efficient navigation. In this context, a crucial aspect is the unequivocal identification of intersections. This paper proposes a novel methodology for uniquely identifying intersections by applying a hash algorithm that generates a distinct fingerprint of each intersection, inspired by the operational mechanisms within blockchain platforms, particularly mimicking the generation of Transaction Hashes. The solution’s core is creating a hash tree to unequivocally identify the intersection for the AVs’ navigation. The application to a real complex case study shows the applicability of the proposed approach.

Urban autonomous driving has the potential to enhance both safety and efficiency of transportation in environments also in complex traffic conditions. However, new services and approaches are necessary to manage Autonomous Vehicles in the real traffic. This paper introduces a novel approach to optimize routing in the urban settings by Deep Reinforcement Learning (DRL) techniques. A modular DRL architecture is proposed to obtain a route able to minimize the length of the paths, minimize the number of turns during the travel and select the dedicated lanes. The proposed DRL is implemented on a case study where the agents are trained in a simulation environment for the city center of Bari, a town of Southern Italy.

The development of Quantum Computing has brought great advantages in terms of computational power that can be seen as an opportunity or as a potential threat to currently implemented systems. The security of a platform can be easily broken if its founding algorithms are not quantum-safe. For this reason, it is crucial to understand how quantum computers work and how much time is needed to switch to quantum-safe platforms. The main contribution of this paper consists of a software architecture for modular Blockchains to let multiple encryption algorithms coexist in order to mine new quantum-safe blocks without discarding old, validated, quantum-broken ones. Old blocks will still be unsafe for post-quantum cryptography, but this is not a threat to the chain integrity.

I n the era of the transition towards electric vehicles (EVs), new services and tools are needed in order to facilitate the use of such vehicles. In this paper, a new tool is designed to optimally plan the long distance trips with an EV. The trip planner tool is realized by using MATLAB software. It implements an algorithm that, based on the EV battery model and on the charging stations information available on the route from departure to destination, determines the best itinerary in term of travel time and cost, minimizing the charge stops. The prototype of the trip planner tool is demonstrated by a real case study.

This work addresses the security problem of protecting secrets in the framework of discrete event systems that are modeled by deterministic finite automata. We characterize a global secret that composes of one or multiple states, in which each state is assigned to a security level. A state is said to be protected if any event sequence from the initial state for reaching it contains the amount of protected events equal to or greater than the required security level. In addition, we assume that the protected event labels must be recovered within a bounded of consecutive protected events (called as$K$-protection). Our objective is to design a$K$-protection event policy such that the protected secret state pieces satisfy a predefined protection threshold. To this end, we first construct a security automaton that integrates the system state information and its current security level, and a$K$-protection automaton that lists all the possible protections of event sequences. Then by using the supervisor control theory technique, the valid protecting policy to enforce the security requirement is obtained. Finally, examples are used to illustrate the proposed protection method.

The increasing volume of traffic worldwide enlightens the problem of ensuring driving safety and preventing collisions at unsignalized intersections. In this regard, with the advent of Connected Autonomous Vehicles (CAV), Collision Avoidance (CA) and Autonomous Intersection Management (AIM) problems have been intensively studied and many cooperative and optimization-based approaches have been proposed. In this paper, we introduce a double-level collision-free control system, performed by a Central Optimizer (CO) and local CAV controllers, that allows CAVs to safely cross the intersection at the same time. The CO collects data from CAVs and imposes waiting times at specific waypoints that are then used by the low-level controllers to regulate the vehicle speed. The main advantage of the presented method is that a less complex optimization problem is formulated by imposing waiting times rather than determining the speed profile. A simulation campaign conducted on Matlab shows the performance of the proposed approach.

A time Petri net is said to be critically observable at a given time instant if the markings consistent with any observation at the time instant are included either in the set of critical markings or non-critical markings. This work studies the verification problem of critical observability for timed discrete event systems modeled by bounded labeled time Petri nets. The proposed method is a two-fold process: a preliminary verification of critical observability for the underlying logic labeled Petri net and a further verification considering the time constraint associated with each transition. The first step is based on the concurrent composition of a reachability graph of the logic net. If the logic net is critically observable, then the time net is also critically observable at any given time instant. Otherwise, the second step is to design an algorithm to compute all pairs of transition-class sequences that violate critical observability at the given time instant, and then a set of linear programming problems is exploited to check critical observability for the corresponding timed system.Note to Practitioners—Timed discrete event systems provide a theoretical model for safety-critical real applications such as air traffic management, smart grid, and industrial control, which are vulnerable to malicious attack and destruction at some particular time instants such that a system may be misled to dangerous states. Critical observability of a timed discrete event system is a property with which the predefined dangerous states can be determined and detected from the observation by an observer at a given time instant. This research aims to offer a systematic approach to check critical observability for timed discrete event systems modeled by bounded labeled time Petri nets, which can also present some new ideas and insights for practitioners in the field of safety-critical systems.

Safety issues related to autonomous vehicles are of great concern both in the academy and industry, identifying the braking system performance as a crucial research field. In this work, an autonomous braking system based on deep reinforcement learning is proposed, employing an intelligent agent trained in city scenarios to manage both pedestrians’ safety and passengers’ comfort. The agent is modelled via the deep deterministic policy gradient algorithm in a software environment and its performance is tested showing good results in maximizing both pedestrians’ safety and passengers’ comfort.

This article presents two methods for solving the electric vehicles (EVs) relocation in EV-sharing system: 1) a centralized method where the decisions are taken by a unique decision-maker by using the complete knowledge of the system and 2) a randomized matheuristic algorithm where decisions are taken by the stations that coordinate for solving the relocation problem. For each methodology, two approaches are proposed for the EV relocation, i.e., the relocation performed by the EV-sharing operators and the relocation involving registered users also with an incentive scheme based on the crowdsourcing concept. In both the methods, two integer linear programming (ILP) problems are formulated to minimize the relocation cost in the two considered approaches. Moreover, in the randomized matheuristic method, a set of smart stations solve local ILP problems to produce a relocation plan. Finally, some instances and a case study are presented to demonstrate the effectiveness of the proposed approaches for the EVs relocation problem.Note to Practitioners—This article is motivated by the need to optimize the relocation process in the electric vehicle (EV)-sharing systems in order to minimize the relocation costs and guarantee the high quality of the service. To this aim, we first propose a centralized optimization that can be applied by the EV-sharing company for incentivizing users to optimally relocate vehicles in the stations. In this context, both the users and the company obtain benefits. Second, the randomized matheuristic optimization allows the stations to reach a decision about the relocation plan by using local information. The presented strategies can be applied in real applications, and in particular, the randomized matheuristic approach appears a promising strategy for large systems by using limited resources with low computational effort. Future research will focus on the EVs relocation problem in free-floating sharing systems.

This article focuses on the issue of checking critical observability for labeled Petri nets. Critical observability is a property related to the safety concern of cyber-physical systems. With the aim of checking this property of a net system, it is required to detect whether a set of markings consistent with any observed word of the net system is a subset of a set of critical states representing undesirable operations or a set of noncritical states. In this work, we prove a necessary and sufficient condition to check critical observability when the critical state set is described by an arbitrary subset of reachable markings. Then, the result is extended to the case when a critical state set is modeled by all the reachable markings that satisfy disjunctions of generalized mutual exclusion constraints. The proposed method is derived from the solutions of integer linear programming problems and is applicable to net systems with liveness and boundness. Several case studies show the performance of the presented methodology for discrete-event systems.

The control of road intersection in presence of priority vehicles is central in terms of performance of the emergency scenarios optimal management. In this paper a study applying Deep Reinforcement Learning to the traffic light control of a road intersection is presented, also considering the presence of three classes of priority vehicles such as ambulances and police. A case study of a road intersection in the city of Bari is presented. The paper focuses on a high-level dynamics of traffic management, not considering low-level issues like communication and data transferring.

This work aims to define a system for measuring the "lift" of the anchor (the final part of the shutter) present inside the injector based on the use of Machine Learning classification algorithms. The measurement method determined is a non-invasive method, which guarantees that the internal organs of the injection system are not damaged to carry out the measurement and that it can be performed after welding the injector to prevent the "lift" from changing later. This measurement method provides for the classification of the currents circulating inside the solenoid, each of which can be associated with a specific value of the "injector lift. This approach is part of predictive maintenance techniques, a type of maintenance that tries to predict incorrect behavior of the system avoiding that critical operating conditions are reached.Finally, an analysis of the possible techniques for measuring the injector "lift" is carried out through the use of Machine Learning algorithms

In the electro-mobility sector there is a rising necessity of providing new infrastructures, services, tools and solutions to support the diffusion of electric vehicles (EVs). In this framework, this paper aims to propose an innovative service that can improve the experience of electric vehicle users by providing customized information to reduce the range anxiety risk before starting the trip. In particular, an Information Technology (IT) service based on cooperative virtual sensors (VSs) is designed to predict the charge demand by an electric vehicle, driven by a specific user, to accomplish a predefined trip. To this goal, three virtual sensors are designed as software components each one implementing an algorithm to perform a specific task. It is shown how the cooperation of the three VSs is necessary to achieve the final service objective that is to provide customized information to help the user in preparing the EV for the trip. In addition, the effectiveness of the proposed service is demonstrated through a use case implemented by the developed IT application prototype.

In recent years, the employment of robots has become a value-added entity in the industries in gaining their competitive advantages. Moreover, thanks to Industry 4.0 paradigm, many production tasks have grown in terms of dimensionality, complexity and higher precision and need to be performed by robots. Among them, the palletizing task is still highly dependent on the particular problem to solve, and its optimization needs to be performed basing on the ground condition. In this paper a palletizing task problem performed by a robotic manipulator is studied. More in detail, some objects have to be transported from a pre-determined storage area to a delivery area. In the storage area the objects are stacked one on the other in columns, while in the delivery area the robotic manipulator poses the objects in horizontal levels, one over another. The process is optimized by minimizing the total distance travelled by the robotic manipulator to transport all the objects from the storage area to the delivery area. An Integer Linear Programming (ILP) problem is formalized and tested by simulations and experimental results.

This paper proposes a two-level strategy to determine the optimal fleet size of electric car (EC) sharing systems (ECSSs) in a networks of a set of stations. At the first level, the system is modeled as a discrete event system in a closed queueing network framework that allows describing the asymptotic system behavior and determining the optimal fleet size that maximizes the network profit. At the second level the ECSS dynamics is modeled by timed Petri net, in order to take into account some particular aspects, such as the user flows in different time periods of the day or the exit of the customers from the stations when they do not find available ECs. A simulation campaign analysis enlightens the effectiveness of the presented complementary design strategy.

In last years the number of journeys on the Italian rail network is constantly increasing. The railway infrastructure is at a high risk of suffering failure and this can generate the unavailability of the railway service. The monitoring of the railway infrastructure becomes crucial to guarantee punctuality, regularity and quality to the railway service. To this aim, the paper proposes an AHP (Analytic Hierarchy Process) based methodology to evaluate: i) the incidence of failure on the railway infrastructure (single track) and consequently, ii) the unavailability of the railway service. The AHP methodology can represent a valid tool for the railway manager to define the priorities for the intervention and the restoration of failures according to the impact of the risk on the unavailability of the transport service.

In last years the number of journeys on the European rail network is constantly increasing with consequent increase in risk for railway operators and passengers. New strategies, like integrating novel methodologies into innovative ICT (Information and Communication Technology) tools, can help all actors involved in train transportation system (managers, operators, passenger, etc.) in achieving improved human safety conditions. This paper aims at proposing a new ICT tool with an internal module for the evaluation of the safety level of the rail transport system. In the ICT module, an Analytic Hierarchy Process (AHP) based methodology defines a safety level of the rail transport system, considering both the railway infrastructure and the train equipment. The methodology outcomes a global index that describes the safety performance level.

In recent years, the rationalization of building energy usage is one of the most virtuous ways to reduce the consumption of fossil fuels and the containment of the environmental impact, associated with the production, distribution and consumption of electrical and thermal energy. In this context, this paper proposes a First Order Hybrid Petri Net (FOHPN) model to simulate and control the energy consumption of the main building electric appliances by a modular approach. The aim of the paper is two-fold: i) helping to recognize how the building electric appliances contribute to peak demand; ii) managing efficiently the building energy consumption. Finally, a case study shows how the FOHPN system works and highlights the advantages of the proposed approach.

Nowadays logistics stakeholders have the increasing need to be better connected with other businesses and authorities than ever before, being in the same time faster and cost efficient in their own business combining information from various sources. For this purpose, an innovative connectivity platform is proposed in order to simplify the visibility between the different actors of a supply chain and simplify collaboration among them. Moreover, the new technological solution performed by the platform is expected to improve the information exchange within the logistics environment. Finally, a case study about the application of the platform is presented to show the advantages of the new technological solution.

This paper proposes innovative services in the electro-mobility framework with the goal of enhancing the electric vehicle charging experience. In this context, the objective is to provide a smart charging service that helps drivers to make the best choice for charging their electric vehicles, according to the vehicle real-time position, battery type and autonomy. Moreover, the drivers are allowed to book the preferred charge option according to availability and cost of the charge points. To this purpose, two virtual sensors are designed and defined that allow to perform the smart charging searching service. In particular, an algorithm and a UML diagram are adopted to describe the virtual sensors operations and cooperation. In addition, the proposed virtual sensors functioning and interactions are described as Discrete Event Systems modeled in a Petri Net framework.

This paper proposes a novel decentralized on-line fault diagnosis approach based on the solution of some integer linear programming problems for discrete event systems in a Petri net framework. The decentralized architecture consists of a set of local sites communicating with a coordinator that decides whether the system behavior is normal or subject to some possible faults. To this aim, some results allow defining the rules applied by the coordinator and the local sites to provide the global diagnosis results. Moreover, two protocols for the detection and diagnosis of faults are proposed: they differ for the information exchanged between local sites and coordinator and the diagnostic capability. In addition, a sufficient and necessary condition under which the second presented protocol can successfully diagnose a fault in the decentralized architecture is proved. Finally, some examples are presented to show the efficiency of the proposed approach.

This paper presents a software tool for the simulation of a decentralized control strategy to assign tasks to Autonomous Guided Vehicles (AGV) and coordinate their paths to avoid deadlock and collisions. We consider a zone-controlled guidepath network where a set of intelligent vehicles (agents) has to autonomously reach a consensus about the distribution of a set of tasks, i.e., a set of zones to be reached. To this aim, first the agents apply a discrete consensus algorithm in order to locally minimize the global cost for reaching the destination zone, then they move according to a decentralized coordination protocol that is based on a zone-controlled approach with the aim of avoiding deadlock and collisions. The software tool allows the user to define the guidepath network, then randomly generates the positions of AGVs and destinations and runs the two algorithms visually showing their behavior.

This paper presents a building automation strategy for natural ventilation control and reducing building energy consumption. An on-off control is proposed in order to manage the windows opening and realize a natural ventilation flow guaranteeing indoor thermal comfort. The control logic is based on activation thresholds that are optimized to reduce the discomfort for overheating and undercooling. In particular, the temperature comfort range dynamically varies according to the adaptive thermal comfort theory. To this aim, a co-simulation architecture is proposed: the thermal building behavior and ventilation dynamics are simulated by TRNFLOW within the TRNSYS software and a Particle Swarm Optimization algorithm is employed to optimize the thresholds of windows opening. A case study focusing on a residential building situated in the Mediterranean climatic context is presented: the thermal comfort analysis shows that the optimized control logic significantly reduces the overheating discomfort.

This paper presents an urban-decision support system (U-DSS) devoted to manage, in a unified framework, the logistic services of the smart cities, such as postal delivery (PD) and waste collection (WC) services. The U-DSS architecture is proposed by describing its main components. In particular, this paper focuses on the core of the U-DSS, i.e., the model component that provides the solutions of a general vehicle assignment and routing optimization problem with the aim of minimizing the length of the routes and satisfying time and capacity constraints. In order to solve the vehicle routing problems in reasonable time, a two-phase heuristic algorithm is proposed based on a clustering strategy and a farthest insertion heuristic for the solution of a traveling salesman problem. The applicability of the proposed U-DSS is enlightened by comparing the proposed heuristic algorithm solutions with the mixed integer linear programming problem solutions of the PD and WC services. Moreover, the discussion of the real case studies of the city of Bari (Italy) assesses the proposed approach.

This paper proposes a Petri net (PN) representation of the activities performed by the key human operators for unloading/loading containers in an intermodal maritime container terminal (CT) with a low level of automation. These processes are the core of the export, import, and transshipment cycles executed in the terminal. The aim of this paper is to consider both the human component and the material handling resources, e.g., cranes and transporters, by defining an accurate model, which describes how to coordinate humans and use the system resources necessary for serving mother or feeder ships. The developed generalized stochastic PN-based model is of limited complexity and represents a complete, unambiguous, and readable model of the target process before coding it in the target simulation tool. The modular integrated model is tested and validated by the simulation of typical and perturbed scenarios of the Taranto CT, a real terminal that is taken as a case study for its complexity and similarity to CTs with multiple transport modes.

This paper presents results on the convergence time of a previously proposed algorithm to solve the distributed task assignment problem on a network of agents. The considered algorithm consists in iterative local integer linear optimizations among the agents to cooperatively assign a set of tasks. Local optimizations are performed among a subset of randomly chosen neigh boring agents. This paper discusses a simulation study on the number of iterations and time required by the algorithm to find the best task assignment given the limited information available at each iteration. Simulations on large and small networks with local optimizations that involve a varying number of agents have been performed.

This paper presents a district energy management strategy devoted to monitor and control the district power consumption in a twofold human-centered perspective: the respect of user's comfort preferences and the minimization of the power consumption and costs. The presented district energy management system forwards the power profile determined the day ahead to each building energy management system that, in turn, minimizes its real-time power consumption and costs (based on rewards and penalties), respecting the comfort preferences. Successively, the power is redistributed among the district buildings in order to minimize the penalties by applying two approaches: a centralized approach for public buildings and a distributed methodology for private buildings. Such optimization problems are formalized by defining some linear programming problems: two case studies are solved to show the applicability of the proposed management strategies.

This paper addresses the problem of determining the optimal fleet size of electric car sharing systems. We model the system as a Discrete Event System in a closed queueing network framework considering the specific requirements of the electric vehicle utilization. Hence, we describe the asymptotic behavior of the vehicles and develop an optimization problem for maximizing the system revenue by determining the optimal fleet size. The large-scale of real-world systems results in computational difficulties in obtaining the exact solution, and so an approximate formulation is provided. Some numerical results illustrate and validate the solution method.

The paper presents a model for freeway traffic performance evaluation and control in a First-Order Hybrid Petri Net (FOHPN) framework. Such a hybrid Petri net formalism includes continuous places holding fluid, discrete places containing a non-negative integer number of tokens and transitions, which are either discrete or continuous. In order to suitably describe the dynamics of the freeway traffic flow, we allow updating the transition firing speed as a function of the markings modeling the freeway traffic, as described by the stationary flow-density relationship. Moreover, we propose an online optimal control coordination of speed limits with the objective of maximizing the flow density. The use of FOHPNs offers several significant advantages with respect to the model existing in the related literature: the graphical feature enables an easy modular modeling approach and the mathematical aspects efficiently allow simulating and optimizing the system. The effectiveness of the FOHPN formalism is shown by applying the proposed modeling and control technique to a stretch of a freeway in the North-East of Italy, where a solution of an accident situation is considered.

In this paper we deal with the problem of modeling and simulating the Steelmaking and Continuous Casting (SM-CC) processes. The SM-CC production processes are very complex and exhibit production cycles that have to be managed with suitable sequencing and scheduling procedures in order to minimize blocking and bottleneck conditions. The paper describes in details the SM-CC process by a modular approach in a Timed Petri Net framework. In order to show that the modeling procedure is suitable to describe large systems, we model and simulate a real case study involving a SM-CC plant located in the North of Italy.

This paper improves a previous result on the multi-agent assignment problem, in which a group of agents has to reach a consensus on an optimal distribution of tasks, under communication and assignment constraints. However, the drawback of the proposed distributed algorithm was that the initial feasible assignment state is given. In this paper we develop a start-up algorithm to find an initial feasible assignment state based on synchronous communications among agents. Moreover, the agents exchange the messages and update autonomously and iteratively the task assignment. Some simulation results prove that the proposed consensus algorithm not only is able to reach a feasible solution but such a solution is close to the optimal one.

This paper addresses the problem of online fault detection and diagnosis in discrete event systems modeled by labeled Petri nets and using Integer Linear Programming Problem (ILPP) solutions. In particular, unobservable (silent) transitions model faults and both observable and unobservable transitions model the nominal system behavior. Furthermore, observable transitions exhibit a kind of non determinism since several different transitions may share the same event label. This paper proposes two diagnosers that work in two different system settings. The first one is a centralized fault detection strategy: the diagnoser waits for an observable event and an algorithm defines and solves some ILPPs to decide whether the system behavior is normal or may exhibit some faults. In the second setting, the system consists of a set of interacting PN modules and each module is monitored by a diagnoser that has local information on the module structure. Moreover, each diagnoser observes and detects the faults of the module it is attached to and shares information in some of its places that are shared with other modules of the system. Some case studies show the two different approaches and point out the peculiarities of the proposed strategies.

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.

The paper addresses the fault detection problem for large discrete event systems that can be modeled by a set of interacting Petri Net (PN) modules. Each system module is monitored by a PN diagnoser that has local information on the module structure and shares information by some places that are coupled with the other modules of the system. Each diagnoser works on-line: it waits for the firing of an observable transition and employs an algorithm based on the definition of some integer linear programming problems to decide whether the system behaviour is normal or exhibits some possible faults.

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.

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.

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.

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.

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