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.

Parkinson’s Disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease, significantly impairing motor functions and quality of life. Early and accurate monitoring of PD progression is essential for improving patient outcomes. Among the innovative approaches, vocal signal analysis has gained traction as a non-invasive tool for assessing disease progression and treatment efficacy. PD patients often experience dysarthria, a neurological speech disorder affecting the pneumo-phono-articulatory system responsible for voice and language production. This study leverages machine learning algorithms to predict the motor and total scores of the Unified Parkinson’s Disease Rating Scale (UPDRS), widely used for tracking PD symptoms. Utilizing a dataset of 5,875 samples, various regression models, including Decision Tree, Random Forest, XGBoost, and Extra Tree, were trained and tested. Additionally, an ensemble Stacking Regressor was implemented to enhance prediction accuracy. The analysis of vocal recordings offers an innovative, non-invasive method for monitoring PD progression, reducing reliance on more subjective and invasive traditional approaches. The use of the ensemble model surpassed the performance of individual models, achieving an R2of 98.31% for predicting total UPDRS and 98.21% for motor UPDRS. Furthermore, the ensemble approach mitigates the risk of overfitting, ensuring greater robustness and reliability in predictions.These findings demonstrate the potential of machine learning in providing reliable and objective tools for PD monitoring, overcoming the subjectivity and limitations of traditional methods.

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.

Being able to walk is one of the most important human abilities. With the increase in life expectancy, the disability rate is also rising, and research is extensively focusing on robotic devices to address this issue. These devices are now being applied in various fields for the assistance and rehabilitation of patients with different types of motor impairments. The aim of this article is to develop a lower limb exoskeleton model controlled using standard regulators in Simulink. After analyzing the construction of the model, the results of various simulations will be presented based on different desired response types and compared with the state of the art.

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 addresses the problems of diagnosability verification and enforcement of discrete event systems modeled with unbounded Petri nets. Diagnosability in such systems is critical for ensuring reliability and maintaining operational integrity, yet current methods often struggle with the complexity introduced by unboundedness and potential deadlocks. Given an unbounded labeled Petri net that may reach deadlocks, a quiescent basis coverability graph is established to verify the diagnosability of the considered system. This procedure employs a deterministic finite state automaton, called an extended verifier, derived from the proposed quiescent basis coverability graph. It is shown that an unbounded Petri net is diagnosable if and only if the verifier does not contain a class of cycles, called repetitive F-cycles. This result also provides necessary and sufficient conditions for diagnosability enforcement by developing an online supervisor. Further, the designed supervisor is maximally permissive and also circumvents a plant entering deadlocks by firing non-fault sequences. Examples are presented to demonstrate the proposed method. Note to Practitioners—Fault diagnosis and diagnosability enforcement are critical for the development and operation of highly automated systems covering computer-integrated production processes, intelligent traffic, computer and communication networks, smart gird, etc. This work touches upon this problem from the perspective of discrete event systems that are modeled with unbounded labeled Petri nets. The feasibility and applicability of the reported method stem from the usage of a structurally compact representation of a considered plant such that the computational cost of a real-world system is acceptable. The graphical representation of Petri nets as well as the proposed quiescent basis coverability graph make the method easy to use and manipulate. Moreover the sufficient and necessary conditions of diagnosability enforcement can be readily verified by the supervisory theory, facilitating its adoption by practitioners.

This paper deals with the online fault diagnosis problem of discrete event systems under malicious external attacks. We consider a scenario where an attacker can intercept certain sensor measurements and alter them arbitrarily, potentially causing a diagnoser to malfunction. In the framework of labeled Petri nets, a novel integer linear programming problem is formulated by introducing binary variables to estimate the possible transition sequences of an observation that may have been tampered with by an attacker. The proposed approach makes two main contributions. The first one is that, by specifying two different objective functions to the integer linear programming problem, we can obtain the diagnosis results in the presence of attacks, which classic diagnosers may fail to achieve; the second is computational efficiency. In the absence of attacks, the proposed approach is experimentally verified to have lower computational overhead compared with the existing results that are based on integer linear programming and those using basis markings. Finally, the proposed approach is illustrated through a manufacturing system for assembling brake valves. Note to Practitioners—Fault diagnosis is critical for highly automated systems such as manufacturing systems, power plants and smart grids. Engineers are familiar with various fault diagnosis techniques in their community. However, malicious attacks in a system are not fully considered when performing fault diagnosis. This research elaborates upon a novel approach to fault diagnosis of discrete event systems modeled by labeled Petri nets, which can perform online fault diagnosis both in the presence and absence of attacks. The approach enables faults to be detected even after certain labels are tampered with by an attacker. The proposed diagnostic vehicle is presented in terms of integer linear programming problems, which facilitates its applications to real systems by practitioners.

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 focuses on the modeling and analysis of systems with various temporal behaviors in the framework of discrete event systems modeled with time-dependent Petri nets. While Petri nets have been extensively studied, few existing formalisms effectively represent the coordination and synchronization among temporal behaviors. To this end, we first introduce a dual-time Petri net that captures both state-driven and event-driven behaviors by integrating temporal attributes associated with places and transitions, respectively. To describe the dynamic evolution of systems, a structural representation of the state space, abstracted as an extended state class graph, is presented. This representation serves as a foundation for analyzing critical system properties. Moreover, we show the efficiency of the proposed method to address the complexity in temporal dynamics, offering a more powerful modeling capability and a more compact structure compared with the existing time-dependent Petri net models. Finally, a case study on a semiconductor manufacturing system is provided to illustrate the practical application of the established model and reported analysis methods.

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 fault diagnosis problem in discrete event systems consists in detecting the occurrences of faults in a plant, which is essential for ensuring the reliability of the plant. In the literature, this problem has been widely studied by assuming that the communications between a plant and a diagnostic agent, i.e., a diagnoser, are reliable and instantaneous. However, for a networked system, the information generated by a plant is transmitted through a shared communication network such that communication delays and losses are inevitable. This article formulates and studies the fault diagnosis problem in networked discrete event systems (NDESs) modeled by labeled Petri nets, where communication delays and losses are considered. Such a problem is also calledrobust fault diagnosisin this article. An important notion closely related to robust diagnosis, namednetworked diagnosability, is introduced, indicating that any occurred fault in a NDES is definitely determined after a limited number of observations. For a NDES, a tool called anetworked basis diagnoseris excogitated to solve the robust diagnosis problem. A necessary and sufficient condition for verifying the networked diagnosability of a plant is derived by using the developed diagnoser. Finally, a manufacturing system is presented to illustrate the developed approach.

This paper addresses the problem of detecting efficiently the diabetic disease. This disease occurs when the human body is not able to produce enough insulin causing high levels of blood glucose or sugar. It can cause different health issues such as eye issues, hearth, kidney and nerve disease and so on. By using a dataset of 2000 patient records from the Frankfurt Hospital in Germany, we implement a procedure to analyze the outliers and the correlations between dataset features in order to optimize the input data for training and testing phase. In addition, we apply and compare different Machine Learning algorithms, namely XGBoost, Random Forest and Decision Tree, to evaluate their prediction performances based on different standard evaluation metrics such as accuracy, precision, recall and F1-Score. Based on the experimental tests the Random Forest method outperforms the competitors achieving 98% of prediction accuracy.

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.

This research focuses on the forbidden state problem in the framework of labeled Petri nets (LPNs), i.e., to design a supervisor for a plant modeled by an LPN such that the closed-loop system cannot reach a set of predefined forbidden markings and does not contain any deadlock. Different from the traditional control scheme, the supervisor derived by this work can not only observe the observable transitions, but also the quiescence information. First, a new structure named an extended basis reachability graph (EBRG) is introduced to describe the reachability space of an LPN without computing all reachable markings. Based on an EBRG, a basis observer is then excogitated to represent the behavior of an LPN. Some states in the basis observer are defined as bad states and control-induced deadlocks, which relates to the undesirable behavior of the plant. Finally, an algorithm is introduced to compute a supervisor based on the basis observer. The consideration of system quiescence provides extra information on the marking estimation of the closed-loop system such that certain disabled transitions are re-enabled. Consequently, the developed supervisor in this article is generally more permissive than those do not observe the quiescence.

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.

This study reviews the research works published in the last five years on Digital Twin (DT) technology for intelligent transportation systems, focusing on the use of DT in electromobility and autonomous vehicles. The review is carried out systematically, considering specific domains within intelligent transportation in which DT technology is applied in combination with Internet of Thing and 5G technologies. In addition, the paper discusses the current issues in electric vehicle services, such as tracking, monitoring, battery management systems, and connectivity, and how they can be addressed effectively through DT approaches.

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 issue of Interdisciplinary Methods and Approaches for Cybernetics and Systems Modeling includes a collection of extended versions of best presented papers in CoDIT 2020 conference. The aim con...

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.

Industry 4.0 revolution is destined to revolutionize the tasks that must be performed within companies and, in this context, new emerging technologies entail the needs for new professional skills. Therefore, it is necessary to think about how the workforce will be affected by the technological changes and how the skills of the workers will change in the future. Nowadays, one of the most critical issues is the misalignment between the needs of the companies and the actual competences of the workers. To face this problem, in this work an innovative approach based on the Analytic Hierarchy Process (AHP) is developed to derive the professional skills needed for Industry 4.0 technologies. In particular, the case of the Adriatic and Ionian area is analyzed in order to show the application of the proposed methodology. The results of the case study allow obtaining Technological Roadmaps to be used by universities and training organizations, companies and authorities in order to provide a more effective and cutting-edge training. Moreover, an overview of the most requested professional profiles in the Adriatic and Ionian area is also provided.

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 issue of New Methods and Approaches in Decision and Control of Intelligent and Cyber Systems contains a collection of extended versions of best presented papers in CoDIT 2019 conference and ad...

Ambient assisted living and smart home technologies are a good way to take care of dependent people whose number will increase in the future. They allow the discovery and the recognition of human's activities of daily living (ADLs) in order to take care of people by keeping them in their home. In order to consider the human behavior nondeterminism, probabilistic approaches are used despite difficulties encountered in model generation and probabilistic indicators computing. In this article, a global method based on probabilistic finite-state automata and the definition of the normalized likelihood and perplexity is proposed to manage ADLs discovery and recognition. In order to reduce the computational complexity, some results about a simplified normalized likelihood computation are proved. A real case study showing the efficiency of the proposed method is discussed. Note to Practitioners-This article is motivated by the problem of the automatic recognition of activities that are daily performed by elderly or disabled people in a smart dwelling. The set of activities to be recognized is defined by a medical staff (e.g., to prepare meal, to do housework, to take leisure, etc.) and correspond to pathologies that have to be monitored by doctors (e.g., loss of memory, loss of mobility, etc.). The proposed method is based on a systematic procedure of offline construction of a model for each activity to be monitored (the activity discovering step). The online recognition of activities actually performed (the activity recognition step) is afterward based on these models of activities. Since the human behavior is nondeterministic, and may even be irrational, probabilistic activity models are built from a learning database. In the same way, probabilistic indicators are used for determining online the most probable activities actually performed. The efficiency of the proposed approach is illustrated through a case study performed in a smart living lab.

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.

Recent years have witnessed the rapid development and deployment of cyber and computer technologies, thus highly influencing the design methodologies of discrete-event and hybrid systems, i.e., systems with discrete and mixed discrete-continuous states/inputs. Their prevalence can be found in almost all areas of human life, such as embedded software, automated manufacturing systems, work-flow management, logic controllers, communication protocols, robotics, transportation and mobility, military, smart buildings, etc. Given the criticality of such applications, such systems ought to be carefully modeled, thoroughly verified, and adequately analyzed.

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.

The Key Performance Indicators (KPIs) are usually adopted to evaluate the progress of the stated objectives in a specific framework. In a context where no suitable data are available, the correct estimation of KPIs values is an open issue. Hence, a new methodology is needed to evaluate the baseline values of KPIs. This paper presents an innovative approach to estimate the baseline values for a set of KPIs, that can be already existing or defined for the first time, in absence of historical data. The proposed approach makes use of data retrieved by different suitable sources, such as surveys, questionnaires, etc., comparing them with existing data in similar contexts to estimate KPIs baseline values. A case study is presented and the proposed methodology is applied to estimate specific KPIs in the electro-mobility sector.

In the present paper, a new Integer Linear Programming (ILP) formulation is proposed for a general Multi-Day Container Drayage Problem (MDCDP) that consists in assigning trucks to container transportation tasks during several days. The model describes real-world problems taking their particular issues into account: different types of tasks, different types of containers, a heterogeneous fleet of trucks, the rest periods of drivers and so on. After a review of the state-of-the-art about the Container Drayage Problem (CDP), the MDCDP is presented and modeled as an integer programming problem. This new formulation is an improvement of a previously published formulation: it considers a more realistic objective function and fixed rest periods of drivers. As a consequence, the number of variables is quite reduced and so the new model can be more easily solved with standard solvers. Finally, computational tests on randomly generated instances are presented in order to illustrate the benefits of the new ILP formulation. In particular, it turns out that computational times for the new model are an order of magnitude lower than the previous formulation.

The challenges brought about by the fourth industrial revolution are the key point of public and private research institutions. An excellent knowledge of industrial changes and enabling technologies in Industry 4.0 context becomes fundamental in the manufacturing sector in order to project an education and training governance model. This paper proposes a procedure based on three techniques synergistically applied in order to analyze the problem for the Adriatic-Ionian areas: i) a desk study to investigate the problem and define the involved phenomena; ii) the application of the Analytic Hierarchy Process to obtain numerical indicators of the importance of the considered phenomena in the fourth industrial revolution; iii) a set of interviews to manufacturing companies based on suitable “interview form” in order to validate the obtained numerical indicators.

The satisfaction of thermal comfort and indoor air quality conditions is one of the main objectives in the design of residential aged care homes such as retirement homes. Furthermore, in this context, the integration of building automation systems can both help the user to interact easier with the building components, and at the same time allows guaranteeing an adequate level of indoor comfort. The aim of this work is to design a Decision Support System able to improve indoor comfort by responding to occupants actions and preferences inside a room unit of a retirement home. In particular, optimized control logics for building automation systems are designed to minimize discomfort conditions within a smart room unit. The indoor environmental conditions and the user thermal-hygrometric comfort are estimated by means of the Fanger's comfort theory, by evaluating the Predicted Mean Vote and the Percentage of Person Dissatisfied indices. With the aim of determining the optimal activation ranges of the Heating, Ventilation, and Air-Conditioning systems that minimize the thermal discomfort conditions, several simulations are conducted by varying the activation temperature set points. The results show how the integration of automation systems may provide significant thermal discomfort reductions by optimizing the air conditioning activation timing.

Serious Games (SGs) represent a new approach to improve learning processes more effectively and economically than traditional methods. This paper aims to present a SG approach for the electro-mobility context, in order to encourage the use of electric light vehicles. The design of the SG is based on the typical elements of the classic “game” with a real gameplay with different purposes. In this work, the proposed SG aims to raise awareness on environmental issues caused by mobility and actively involve users, on improving livability in the city and on real savings using alternative means to traditional vehicles. The objective of the designed tool is to propose elements of fun and entertainment for tourists or users of electric vehicles in the cities, while giving useful information about the benefits of using such vehicles, discovering touristic and interesting places in the city to discover. In this way, the user is stimulated to explore the artistic and historical aspects of the city through an effective learning process: he/she is encouraged to search the origins and the peculiarities of the monuments. A case study in the city of Bari, Italy, shows the application of the proposed SG tool.

This paper deals with the routing problem of pick-up and delivery services considering time windows and capacitated vehicles. In order to consider large real problems, this paper proposes a distributed vehicle routing problem (VRP) with time windows, based on cluster first, route second methods. First, by using a graph partitioning solved by local integer linear programing problems, the vehicles autonomously generate a number of clusters equal to the number of available vehicles. Second, each vehicle solves a traveling salesman problem with time windows to compute its route in the assigned cluster. The method can be applied by the vehicles for both planning the workday of the pick-up services and adapting the routing plan to manage the ongoing requests. Some benchmark problems are generated and solved by the distributed algorithm and compared with the solution obtained by the exact approach. Moreover, a real case study involving a courier service shows the efficiency of the solution method.

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.

In the European electromobility framework, the interoperability of platforms and the standardization of services are becoming the main goals of researchers and practitioners. In this context, the objective of this paper is to propose innovative services for electromobility actors and stakeholders based on the definition of Virtual Sensors (VSs). A VS provides new information to the network by aggregating, elaborating and processing of existing and available electromobility data. To this purpose, each VS functioning, outputs and inputs can be described by UML diagrams. In order to show the proposed methodology, a useful VS for electromobility enhancement is presented, i.e., a personal mobility probability VS.

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 aims to present the software functional requirements in the context of electromobility with specific focus on Electric Light Vehicles (EL-Vs). By adopting a consolidated methodology, the Information and Communication Technology (ICT) requirements are defined on the basis of the definition of use cases and their representation with the Unified Modeling Language (UML), through Use Case Diagrams and Activity Diagrams. More specifically, six use cases are described with the main flow of actions. UML use case diagrams are also reported to show the functionalities requested from the ICT service providers, whereas UML activity diagrams show the detailed sequence of actions for each use case. Finally, the case study section reports the results of the application of the proposed methodology and lists the user software requirements for EL-Vs management.

Car sharing (CS) services are promising solutions complementary to the classic public transport forms. In order to make CS effectively competitive, suitable planning and management strategies are required. This paper presents a decision support system (DSS) for handling the user-based vehicle relocation problem by applying economic incentives ruled by a threshold policy. Unlike the existing approaches, a methodology is proposed for determining the optimal threshold, which considers explicitly the stochastic reactions of the customers to the incentives. To this aim, the CS system is described in detail by unified modeling language diagrams and is modeled in a discrete event system framework. Moreover, a closed-loop control strategy is introduced to implement the vehicle relocation policy on the basis of the system state and the best threshold values, evaluated by discrete event simulation and particle swarm optimization. A case study simulation analysis shows that the proposed DSS management strategy can significantly improve the system performance.

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 correspondence paper deals with the sensor placement optimization problem in the context of indoor multiple inhabitants location tracking to solve ambient assisted living problems. Binary sensors, like passive infrared (PIR) sensors, are used to guaranty specific coverage requirements and allow privacy respecting. Moreover, within real home environments, different kinds of obstacles (like walls, high furniture, etc.) can affect the detection capacity of PIR sensors. This paper proposes an integrated framework devoted to optimize the placement of sensors and PIR sensors in smart homes by taking into account physical topologies and coverage precision constraints. An integer linear programming problem is formalized and a case study illustrates the applicability of the proposed approach and the scalability of the optimization method.

Smart home technologies are a promising way to improve health safety of frail people living alone at home. They allow for example on-line recognition of Activities of Daily Living (ADLs) performed by a person, in order to detect dangerous or unusual behaviour. Since human behaviour is not deterministic, probabilistic approaches are often used for ADL recognition, despite difficulties encountered in model building and probabilistic indicators computing. In this paper, it is proposed an approach, based on a Probabilistic Finite State Automata, to detect which activity is being performed. For that a new indicator, called the normalised likelihood, is proposed. The robustness of this indicator to the size of the observed behaviour as well as its computational complexity are also addressed. Finally, the quality of the obtained results are discussed on the basis of an experiment performed in a living lab.

This paper deals with the smart placement of motion sensors in smart homes for Ambient Assisted Living, by considering the sensor technology and cost and respecting specific coverage requirements. The core of the proposed methodology is a decision module that can optimize the sensors placement according to different objectives. More precisely, the main objective is the minimization of costs of the deployed sensors. Moreover, the second objective can be the maximization of the overlapping in order to find a robust solution or the minimization of the overlapping of the detection areas in order to improve the inhabitant localization. A case study demonstrates the effectiveness of the proposed strategy on sensors placement in a domestic environment.

This paper specifies a cloud-based cooperative decision support system (DSS) that aims at integrating logistics management and decision support strategies for intermodal transportation systems. The proposed DSS is dedicated to synchronize different transportation means by using the modern information and communications technology tools and by taking into account environmental aspects. This paper describes the DSS cloud-based architecture and presents the procedure to be followed in order to design a DSS able to support decision makers in different logistic decision fields. The advantages of the proposed DSS are enlightened by specifying three main decision modules: cargo transport optimization, intelligent truck parking, and CO2monitoring. Moreover, the applicability of the proposed DSS is described by specifying a DSS for the case study of the logistic network of the Trieste port (Italy), including the port, the inland terminal, and the highway connecting them. Some simulation campaigns are employed both to set the decision modules and evaluate the DSS application benefits.

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.

The aim of the paper is to provide an optimal placement of sensors for inhabitant location tracking in smart homes, by using only motion detectors. In particular, motion detectors are binary sensors largely used in ambient assisted living applications because they are low cost, non-intrusive and privacy sensors. An approach to optimize the placement of motion detectors in a real home environment by adopting a two-dimensional grid is presented. In this context, the real coverage area of a sensor is computed by considering the obstacles and respecting the specified coverage performance requirements. The optimization problem is formalized and solved as an Integer Linear Programming problem and a case study is presented to show the efficacy of the proposed approach.

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 a model of Satellite Base Station (SBS) networks subject to malicious attacks and defense actions. The SBS structure and the attack-defense actions are modelled as a two-player stochastic game and a Nash Equilibrium is computed to obtain the stationary strategy guaranteeing the best behaviour. On the basis of this result, the SBS dynamics are modelled as a Discrete Event System in a Timed Petri Net framework that allows obtaining the evolution of the SBS under the Nash Equilibrium and the stochastic game rules. A case study describing the cyber security of an SBS is outlined and the details of the model are illustrated.

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 presents an integrated system (IS) devoted to solve the complex scheduling problems in steel-making and casting (SMC) plants. The presented IS is composed of four modules: data base, optimization, simulation modules, and the user interface. In particular, we develop the two main components of the IS: the optimization and simulation modules. The optimization module is based on a mixed-integer linear programming formulation with the objective of minimizing the makespan, i.e., the completion time of the last job. Moreover, a discrete-event simulation module is used to validate and assess the proposed schedules. By designing the IS for a real case study, we show how it can be applied off-line to schedule the daily operations of the SMC, as well as online in order to face unpredictable events such as failures and blocks of the machines. Note to Practitioners-This paper is motivated by the necessity of solving the complex scheduling problems in steel-making and casting (SMC) plants. To this aim, the paper presents an integrated system (IS) that is devoted to help decision makers in selecting the optimal schedules of the operations also in the cases in which unpredictable events may occur. The main modules of the presented IS are an optimization module, based on a mixed-integer linear programming model, and a simulation module, able to verify and validate the proposed schedules by what if analyses and stochastic input parameters. Because of the complexity of the problem, some approximations referring to the transportation system planning are considered. However, the results show the efficiency of the proposed scheduling approach for real industrial applications. Future research aims at investigating about more efficient mathematical programming models in order to solve larger problems involving the planning of one or more weeks and a more detailed description of the plant.

This paper deals with the novel and complex problem of scheduling the internal operations in post-distribution cross docking systems (PDCDSs), i.e., a kind of cross docking terminal where the operations of good allocation are performed. More precisely, internal operations of the PDCDS consist in deconsolidating inbound cases, sorting products according to customer requests, and consolidating outbound cases. The problem is determining the optimal schedule of the internal operations with the objective of minimizing the makespan. The contribution of this paper is twofold. First, the PDCDS scheduling problem is characterized and the corresponding NP-hardness is proved. Second, in order to address the complexity of the problem, two mixed integer linear programming (MILP) models and a heuristic algorithm are presented. In particular, the mathematical programming formulations are solved by using symmetry breaking constraints and objective function perturbations. Moreover, the lower bounds of the start times of operations are evaluated in order to strengthen the formulations. A set of test results compare the formulation performances and a case study shows the effectiveness of the proposed heuristic algorithm to schedule the operations of an half work day of an Italian company of cloth retail stores.

The logistics systems that sustain today’s global supply chains and transportation systems must provide resource-efficient, sustainable, safe, equitable, and timely delivery of goods and services for the benefit of the economy and society. The increasing availability of fast, cheap sensing technologies that can capture real-time data on the location of items and the states of the systems in which they are operating, as well as the rapid and often unpredictable changes in environmental and market conditions, provides opportunities for innovative arbitrage of information, speed, and flexibility. Automation Science and Engineering (ASE) has the potential to enhance the performance of logistics systems by providing novel, integrated hardware, and software solutions that alter the economics of different segments of the logistics chain, by improving throughput and reducing resource requirements and environmental impact. At the same time, the development of novel technologies, such as regenerative medicine and renewable energy are placing totally new demands on logistics systems, requiring the movement of living section in the former case and energy in the latter. The increasingly complex networks of service providers and supply chain partners that logistics systems must serve create complex systems of systems where the decisions of any individual agent may have significant unforeseen consequences for the larger network, requiring a broader perspective on these sections than that of individual stages in the logistics network.

The deployment of the Electric Vehicles (EVs) in the fleets of Car Sharing (CS) systems represents a concrete opportunity to overcome the initial drawbacks associated to these new mobility solutions and so stimulate their diffusion. However, in order to make such a solution efficient and competitive with the traditional private transport, a detailed planning and an accurate management are required. With the aim of obtaining a tool able to support the management in facing these concerns, this paper presents the basic structure of a model based Decision Support System (DSS) to manage an electric-CS service. In particular, such a complex system is described as a Discrete Event model: the Unified Modeling Language formalism is used in order to represent the structure and the dynamics of a generic CS system. Moreover, a discrete-event simulator is developed and applied to an important CS management problem: the vehicle relocation problem. In particular, the performances of different vehicle relocation strategies in different operative scenarios are evaluated.

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.

The management and control of vehicles transporting hazardous materials (hazmat) on congested highways has attracted growing attention from researchers in recent years. This paper proposes a decision support system (DSS) for monitoring hazmat vehicles, aimed at assessing two problems: evaluating the social risk induced by hazmat vehicles traveling in highways and selecting restoration procedures after an accident involving heavy vehicles. The proposed DSS can estimate in real time and offline the risk of hazmat transportation, by taking into account the type of transported hazmat, the traffic, and the density of populations living close to the highway. Two main modules of the DSS are specified: the risk assessment module and the simulation module (SM) that allows forecasting risk in different contexts and scenarios. In particular, the SM is realized by modeling the highway network in a colored Petri net (CPN) framework. In order to show the effectiveness and the applicability of the DSS, a prototype is described and applied to a highway in the North-east of Italy.

The rapidly growing of the world's population makes efficient Healthcare System (HS) management more and more important to all stakeholders, from patients and healthcare providers, to insurance companies and governments. The use of advanced information and networking technologies to advance health care management has long been recognized as the most effective way. Hence, healthcare organizations are often compared with manufacturing systems, and techniques and models from production systems are adapted to solve healthcare management problems. This paper presents a review about the relevant approaches in the related literature to model and simulate HSs with particular focus on discrete event system approaches. In particular, some works are recalled with more detail to show the integrated use of different formalisms in order to model and manage conventional HSs (e.g., hospital departments) as well as novel healthcare paradigms, like health at home and electronic health.

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 12 papers in this special section can be divided into two sets. The first eight papers deal with general DES control problems, while the second set of four papers addresses other particular DES problems such as diagnosability analysis, state estimation, deadlock avoidance and testing.

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 deals with the scheduling of operations in a Steelmaking and Continuous Casting (SM-CC) plant. In particular, we consider a real case study of an integrated plant located in the North of Italy. First, we describe in details the SM-CC process of the considered system by using the Unified Modeling Language. Second, we focus on the schedule of casts on the continuous casting machine that is subject to different types of setup operations. With the objective of minimizing the maximum completion time, the schedule of the continuous casting machine is modeled by a Mixed Integer Linear Programming formulation. An example enlightens how a proper scheduling of the casts is of basic importance to obtain good system performances.

The Car Sharing (CS) systems are promising services that are complementary to the traditional public transport forms and ensure a good level of flexibility and comfort. However, in order to make such systems efficient and competitive, suitable planning and management strategies are required. In this paper we deal with a crucial problem of the CS system management: the vehicle relocation problem. In order to obtain an as accurate as possible representation of the behavioral aspects of the CS service, a modeling approach based on the Unified Modeling Language has been used. Moreover, the proposed management strategies are analyzed and assessed by a discrete event simulation that considers a real case study in different scenarios.

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 eight papers in this special section focus on health-care management and optimization, the use of advanced networking and information technologies in health care services, and the provision of high-quality health care to improve medical services.

The transportation of hazardous materials (HAZMAT) on congested motorways is an area of increasing concern for public safety and environmental awareness. This paper models and analyzes the commercial transportation of HAZMAT on motorway in a Colored Timed Petri Net framework. The proposed model allows estimating in real time the risk of HAZMAT transportation, by taking into account the type of transported hazardous material, the traffic and the density of population living close to the motorway. In order to show the potentialities of the model, a real case study involving a stretch of a motorway in the North-East of Italy is simulated and different scenarios are analyzed. The results illustrate that the model is able to provide a support for the risk analysis and the rescue decisions after an accident.

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 studies and assesses the application of an innovative solution based on Information and Communication Technology (ICT) tools in managing security and customs clearance operations. In order to improve these procedures the paper analyzes the main anomalies and bottlenecks related to the customs clearance procedures and proposes novel ICT based solutions. In order to analyze the impact of the new procedures on the overall logistic system, a real case study is modelled and simulated.

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.

The paper focuses on the application of Information and Communication Technology (ICT) tools to the real-time transport monitoring in order to trace and automate specific procedures as payments and customs clearance operations. In particular, a case study, that represents an example of intermodal freight transportation (IFT) system, is analyzed and simulated. The flow of goods and information involved in this case study is described in order to highlight the improvements reached by using ICT solutions. A simulation model for this system is proposed by the UML formalism and the discrete event simulation results point out the huge impact of ICT on real-time management and operations of IFT systems.

The use of computers in education has greatly increased during the last two decades. At the same time, technology advances have opened new spaces and possibilities for the field of computer-based edutainment-education in the form of entertainment - where learners can achieve their learning goals while having fun. Games on mobile phones have become a significant part of the contemporary culture experienced by young people. Research indicates the potential of mobile games to encourage learning in young adults. The 3-year EC-supported project mGBL (mobile game-based learning) had the objective to prototype a platform for the development and deployment of mobile learning and guidance games, able to support the learning process and the support of decision making in critical situations not only in a cognitive but also in an emotional way. This paper describes key issues emerged in development phase of the Mogabal game engine within mGBL framework in both technical and pedagogical aspects, showing technologies, strategies and methodology adopted. A number of game prototypes based on such engine were devised and some were tested during the project user-trials. These games prototypes demonstrate the capabilities of the devised engine to cover a wide range of different games types and educational contents.

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 presents a modeling technique for analyzing supply chains that represent a process oriented approach to producing and delivering products. The system is modeled by using a new extension to ordinary Petri nets (PNs) named complex-valued token Petri nets. Such a high-level of Petri nets employs complex-valued tokens to increase their descriptive abilities. A two stage supply chain is analyzed and a pull JIT/Kanban methodology is used to synchronize the successive completion of the products. The simulation of the stochastic complex token PN modeling the supply chain evaluates the system performance under different values of Kanbans and transporters.

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

This paper presents a new extension to ordinary Petri nets (PNs) that uses complex-valued tokens. By allowing two kinds of tokens, "real" and "imaginary," each place marking contains both quantity and type information. Complex-valued token PNs were designed to integrate seamlessly with other popular Petri net extensions such as timed nets, stochastic nets, and colored nets. This simple and intuitive application of complex numbers and complex arithmetic to PNs provides a unique modeling tool. Some examples show the capabilities of this proposed class of PNs. Note to Practitioners-Discrete-event systems are often man-made systems such as transportation systems, computer communication networks, distributed software, and manufacturing systems. They typically involve the flow of information and physical goods through a network. The flow itself evolves in continuous time but the initiation or completion of the event happens at a discrete point in time. Analyzing the system's performance is key to their successful operation. This paper presents a new approach to performance analysis with application to supply-chain management.

Automated manufacturing systems (AMSs) can process different parts according to operation sequences sharing a finite number of resources. In these systems, deadlock situations can occur so that the flow of parts is permanently inhibited, and the processing of jobs is partially or completely blocked. Hence, one of the tasks of the control system is ruling resource allocation to prevent such situations from occurring. A large part of the existing literature focused on systems in which every operation is performed by only one resource. This paper proposes a deadlock strategy to avoid deadlock conditions in more complex systems where multiple resource acquisitions are allowed to complete a working operation conjunctive resource service system (CRSS). The AMS structure and dynamics is described by a colored timed Petri net model, suitable for following resource changes and working procedure updating. Moreover, digraphs characterize the complex interactions between resources and jobs so that the conditions for the deadlock occurrence are derived. Finally, an event-based controller is defined to avoid deadlock in CRSSs on the basis of the system state knowledge and of the given priority law ruling the concurrent job selection.

As more and more producers move to use flexible and agile manufacturing as a way to keep them with a competitive edge, the investigations on deadlock resolution in automated manufacturing have received significant attention for a decade. Deadlock and related blocking phenomena often lead to catastrophic results in automated manufacturing systems. Their efficient handling becomes a necessary condition for a system to gain high productivity. This paper intends to present a tutorial survey of state-of-the art modeling and deadlock control methods for discrete manufacturing systems. It presents the updated results in the areas of deadlock prevention, detection and recovery, and avoidance. It focuses on three modeling methods: digraphs, automata, and Petri nets. Moreover, for each approach, the main and relevant contributions are selected enlightening pros and cons. The paper concludes with the future research needs in this important area in order to bridge the gap between the academic research and industrial needs.

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.

Automated Manufacturing Systems (AMSs) can process different parts according to operation sequences sharing a finite number of resources. In these systems deadlock situations can occur so that the flow of parts is permanently inhibited and the processing of jobs is partially or completely blocked. This paper proposes a control strategy to manage resources in complex systems where multiple resource acquisitions are allowed to complete a working operation (Conjunctive Resource Service System-CRSS). The AMS structure and dynamics is described by a Colored Timed Petri net model, suitable for following resource changes and working procedure updating. Moreover, on the basis of the deadlock characterization obtained by digraph tools, an event-based controller is defined to avoid deadlock in CRSSs.

This paper will present a new extension to ordinary Petri nets that uses complex-valued tokens to increase their descriptive abilities while retaining their ease of analysis often lost in high-level Petri nets. By allowing two kinds of tokens, "real" and "imaginary", each place marking contains both quantity and type information. Complex Token Petri nets were designed to integrate seamlessly with other popular Petri net extensions such as timed nets, stochastic nets, and continuous nets. This simple and intuitive application of complex numbers to Petri nets provides a unique modeling tool.

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.

a generalized mutual exclusion constraint (GMEC) is a linear constraint that limits the weighted sum of tokens in a subset of places of a place/transition net system. The corresponding controller takes the simple form of a monitor place that can be added to the net to obtain the closed-loop system. In this paper we extend this approach to the case of colored Petri nets, showing that a colored GMEC can express a set of linear constraints and can be enforced by a colored monitor place. We also develop a matrix representation of multisets that is useful for the design of the monitor place.

The real-time traffic control of railway networks authorizes movements of the trains and imposes safety constraints. The paper deals with the real time traffic control focusing on deadlock prevention problem. Colored Petri nets are used to model the dynamics of the railway network system: places represent tracks and stations, tokens are trains. The prevention policy is expressed by a set of linear inequality constraints, called colored Generalized Mutual Exclusion Constraints that are enforced by adding appropriate monitor places. Using digraph tools, deadlock situations are characterized and a strategy is established to define off-line a set of Generalized Mutual Exclusion Constraints that prevent deadlock. An example shows in detail the design of the proposed control logic.

The use of shared resources by multiple part types in automated manufacturing systems (AMSs) can cause deadlock, i.e., a situation in which the flow of parts is permanently inhibited and the processing of jobs is partially or completely blocked. Most of the work existing in literature focuses on systems in which every operation is performed by only one resource. The paper analyzes deadlock conditions for systems in which multiple resource acquisitions are allowed to complete a working operation (conjunctive resource service, CRS). Extending a digraph representation already used for systems with one-resource operation to the case of CRS allows us a formal characterization of deadlock. This leads to an easy solving approach, consisting of a detection/recovery policy. The paper also shows that some results on the safe states of systems with one-resource operation cannot be extended to CRS. As a consequence, some maximally permissive policies for deadlock avoidance cannot be applied to this kind of system.

Flexible manufacturing systems (FMSs) are modern production facilities with easy adaptability to variable production plans and goals. These systems may exhibit deadlock situations occurring when a circular wait arises because each piece in a set requires a resource currently held by another job in the same set. Several authors have proposed different policies to control resource allocation in order to avoid deadlock problems. These approaches are mainly based on some formal models of manufacturing systems, such as Petri nets (PNs), directed graphs, etc. Since they describe various peculiarities of the FMS operation in a modular and systematic way, PNs are the most extensively used tool to model such systems. On the other hand, digraphs are more synthetic than PNs because their vertices are just the system resources. So, digraphs describe the interactions between jobs and resources only, while neglecting other details on the system operation. The aim of this paper is to show the tight connections between the two approaches to the deadlock problem, by proposing a unitary framework that links graph-theoretic and PN models and results. In this context, we establish a direct correspondence between the structural elements of the PN (empty siphons) and those of the digraphs (maximal-weight zero-outdegree strong components) characterizing a deadlock occurrence. The paper also shows that the avoidance policies derived from digraphs can be implemented by controlled PNs.

We develop control policies to avoid deadlocks in cellular manufacturing systems (CMSs). We take advantage of the structure of a CMS to develop a distributed control system in which each cell is controlled locally and independently. The controller of each cell is a local agent that adopts his own control law to improve flexibility and performance measures.

Modern production facilities (i.e. flexible manufacturing systems) exhibit a high degree of resource sharing, a situation in which deadlocks (circular waits) can arise. Using digraph theoretic concepts we derive necessary and sufficient conditions for a deadlock occurrence and rigorously characterize highly undesirable situations (second level deadlocks), which inevitably evolve to circular waits in the next future. We assume that the system dynamics is described by a discrete event dynamical model, whose state provides the information on the current interactions job-resources. This theoretic material allows us to introduce some control laws (named restriction policies) which use the state knowledge to avoid deadlocks by inhibiting or by enabling some transitions. The restriction policies involve small on-line computation costs, so they are suitable for real-time implementation. For a meaningful class of systems one of these policies is the least restrictive deadlock-free policy one can find, namely it inhibits resource allocation only if leads directly to a deadlock. Finally, the paper discusses the computational complexity of all the proposed restriction policies and shows some examples to compare their performances.

Potentially, flexible manufacturing systems (FMSs) possess the ability to attain efficiency and versatility in small batches, variable mix production. However, the FMS potentiality is not yet fully used, because of the high cost of specific control software. Hence, a generic software, usable in an arbitrary FMS, producing an arbitrary part mix, appears as an important means to minimize programming and re-programming effort. In this context, modeling and control problem formalization emerge as a main issue. In this paper the authors refer to Zeigler's system-theoretic formalism for modeling the dynamics of a generic FMS at the job release and job how levels. The authors' discrete event dynamic system (DEDS) model establishes an abstract comprehensive framework for developing a generic control software at such levels. The model is able to represent both hardware/software components of the FMS and processing activity plans with a common "language". A modular structure underlies the description of both the shop-floor dynamics and control rules. So, the software architecture emerges as a direct consequence of the proposed approach and consists of three main parts: the system state knowledge base, the job release manager and the job flow manager.

Two different approaches for routing optimization are proposed and compared. The first method is open-loop and selects the route so that the job percentage on each alternative path approximates an optimal offline predetermined percentage. In the second approach decision-making is based on a merit index taking into account the current work in progress and the consequent machine availability, i.e., the current system's state. The decision is taken by comparing the index values for each one of the alternative paths allows for the job entering the system. Online simulation is used to estimate the near future system's state trajectory and to compute the merit index. Some case studies are presented comparing the method in terms of efficiency and implementation effort.>