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.

Containers delivery management is a problem widely studied. Typically, it concerns the container movement on a truck from ships to factories or wholesalers and vice-versa. As there is an increasing interest in shipping goods by container, and that delivery points can be far from railways in various areas of interest, it is important to evaluate techniques for managing container transport that involves several days. The time horizon considered is a whole working week, rather than a single day as in classical drayage problems. Truck fleet management companies are typically interested in such optimization, as they plan how to match their truck to the incoming transportation order. This planning is a relevant both for strategical consideration and operational ones, as prices of transportation orders strictly depends on how they are fulfilled. It is worth noting that, from a mathematical point of view, this is an NP-Hard problem. In this paper, a Decision Support System for managing the tasks to be assigned to each truck of a fleet is presented, in order to optimize the number of transportation order fulfilled in a week. The proposed system implements a hybrid optimization algorithm capable of improving the performances typically presented in literature. The proposed heuristic implements an hybrid genetic algorithm that generate chains of consecutive orders that can be executed by a truck. Moreover, it uses an assignment algorithm based to evaluate the optimal solution on the selected order chains.

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.

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 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.

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 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.

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 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.

Abstract A network design problem which arises in the distribution of a public utility provided by several competitive suppliers is studied. The problem addressed is that of determining minimum‐cost (generalized) arc capacities in order to accommodate any demand between given source–sink pairs of nodes, where demands are assumed to fall within predetermined ranges. Feasible flows are initially considered as simply bounded by the usual arc capacity constraints. Then, more general linear constraints are introduced which may limit the weighted sum of the flows on some subsets of arcs. An exact cutting plane algorithm is presented for solving both of the above cases and some computational results are reported. © 2004 Wiley Periodicals, Inc.

A class of production-distribution problems with unknown-but-bounded uncertain demand is considered. At each time, the demand is unknown, but each of its components is assumed to belong to an assigned interval. Furthermore, the system has production, transportation, and storage capacity constraints. The paper extends previous results to the case in which transportation delays are present. We show that the problem of finding a strategy which keeps bounded the storage levels reduces to that of finding a strategy for the associated instantaneous network, which is the network obtained by setting all the delays to zero in the original system. The state variables of the associated instantaneous network are the "inventory positions," given by the goods actually present in the warehouses plus the goods already ordered and leading to them.

We consider multiinventory production systems with control and state constraints dealing with unknown demand or supply levels. Unlike most contributions in the literature concerning this class of systems, we cope with uncertainties in an "unknown-but-bounded" fashion, in the sense that each unknown quantity may take any value in an assigned interval. For these situations, we perform a worst-case analysis. We show that a "smallest worst-case inventory level" exists, and it is associated to a steady-state control strategy. Then we consider the problem of driving the inventory levels to their smallest worst-case values. For this problem, we first give necessary and sufficient conditions, then we show that convergence occurs in a finite number of steps, and we give an upper bound for such a number.

We consider a dynamic network flow model for the control problem of a production-distribution system with periodic demand in the presence of storage and transportation capacity constraints. Unlike most papers dealing with control problems of dynamic networks, we derive a control strategy in feedback form. It is optimal in the sense that it involves, for any initial time, the set of all the initial states for which there exists a control strategy allowing the network variables (flows, storage levels) to remain in their constraint domain for all future times. The evaluation of such a strategy requires the previous computation of these maximal sets. We show that, due to the particular structure of the system, they are submodular polyhedra; in particular, they are finitely represented and the representation complexity is a priori known. Moreover, the evaluation of this sequence, even for the infinite horizon problem, can be performed in a finite number of steps that has a known upper bound depending on the dimension of the problem only. As a consequence of these results, the optimal strategy requires, at each step, to solve a submodular flow problem. The finite horizon problem is solved by the proposed method as a particular case. © 1996 John Wiley & Sons, Inc.

A mathematical model is proposed for scheduling activities of periodic type. First a model is proposed for scheduling periodic events with particular time constraints. This problem, which could be considered the extension to periodic phenomena of ordinary scheduling with precedence constraints, is shown to be NP-complete. An algorithm for it of implicit enumeration type is designed based on network flow results, and its average complexity is discussed. Some extensions of the model are considered. The results of this first part serve as a basis in modelling periodic activities using resources. Several cases are considered. Finally some applications are presented for which the proposed model can be a useful tool.