This article proposes a model for the risk-based scheduling of predictive maintenance activities on a railway line to intervene when a track segment has reached a certain state of degradation, thus preventing faults and possible failures. With the aim of taking into account the stochastic nature of real environments, the rail-track degradation process is represented as a stochastic process, and the failure probability is evaluated as the probability of reaching a degradation threshold. Moreover, a rolling-horizon framework is introduced to manage newly available real-time information and unpredicted faults or maintenance activity delays. Whereas the traditional scheduling models are offline models that cover the long-term horizon but neglect operational disturbances, the presented model allows for dynamic day-to-day planning and adaptation of the maintenance plan to real-time information, thereby responding to the increasing understanding of real-world processes. The optimization problem on maintenance scheduling is formulated as a mixed-integer linear programming problem based on risk minimization, in adherence to ISO 55 000 guidelines. Finally, the application of the approach to a real rail network is reported and discussed, with a focus on the planning of tamping activities at the operational level.

One-way car-sharing systems, which allow customers to return vehicles to the stations where they were picked up, offer greater flexibility compared to conventional car-sharing services. Nevertheless, to guarantee this flexibility, such systems have to face difficult problems of vehicle relocation in order to be ready to satisfy as many trip reservations as possible. This paper proposes a user-based relocation methodology in which the users may accept to leave the car in a different location in exchange for fare discounts. To this aim, a two-stage optimization problem is formulated for optimizing the alternative destinations proposed to users and for maximizing the profit of car-sharing operators. The extensive analysis of results shows that, with the proposed user-based relocation strategy and without the operator-based relocation, the number of rejected reservations can be significantly reduced, even with a relatively small number of vehicles and, at the same time, the operator's profit can be increased.

The problem of reducing congestion within urban areas by means of a traffic-responsive control strategy is addressed in this paper. The model of an urban traffic network is microscopically represented by means of deterministic and stochastic Petri nets, which allow a compact representation of the dynamic traffic network. To properly model traffic congestion, intersections are divided into crossing sections, and roads have limited capacity. Each intersection includes a multiphase traffic signal, whose sequence of phases is given and represented by a timed Petri net. The control strategy proposed in this paper aims at minimizing queue lengths by optimizing the duration of each signal phase. This is accomplished by heuristically solving a stochastic optimization problem within a receding-horizon scheme, to take into account the actual traffic flow entering the network, thus making the proposed approach traffic-responsive. In this framework, the Petri nets play a key role, as the cost function to be minimized is a function of the marking, and the constraints include the marking state evolution. The proposed strategy is applicable to both undersaturated and oversaturated traffic conditions.

The limited economic and spatial resources require a careful planning of investments, so that achieving the most accurate knowledge of the performances of any proposed solution is a problem of worth importance. A possible solution to this problem is provided by sensitivity analysis to evaluate their robustness to uncertainties. Then, in this paper, traffic simulations will be used to evaluate, by means of a proposed sensitivity analysis approach, the robustness of several interventions planned for the real case study consisting of the transportation network around the seaport of Genoa in Italy.

An urban network of signalized intersections can be suitably modeled as a hybrid system, in which the vehicle flow behavior is described by means of a time-driven model and the traffic light dynamics are represented by a discrete event model. In this paper, a model of such a network via hybrid Petri nets is used to state and solve the problem of coordinating several traffic lights with the aim of improving the performance of some classes of special vehicles, i.e., public and emergency vehicles. The proposed model has been validated using real traffic data relevant to the city of Torino, Italy. Some relevant experimental results are reported and discussed.

Petri nets are used to define a hierarchical monitoring structure intended for real-time fault diagnosis and recovery in an intermodal terminal where freight only arrives and departs stored into containers. An intermodal container terminal can be modelled as a discrete event system prone to unforeseeable events resulting in deviations (delays) from the a-priori planned schedule. Whenever a failure, i.e., a delay in some operation, is detected, a diagnosis procedure is triggered accordingly. A real case study relevant to a terminal in the port of Genova, Italy, is introduced to show a practical application of the proposed model.

Describes a hierarchical two level modeling and control framework for real-time manufacturing processes, based on hybrid Petri nets, and capable of integrating monitoring and fault detection techniques along with performance optimization procedures. At the higher level, first-order hybrid Petri nets are used to generate the asynchronous concurrent events characterizing the nominal optimal system behavior, also providing the machine production rates. At the lower level, a real time scheduler and a discrete Petri net communicate with the field-bus devices to force the machine to produce at the rates provided by the upper level, and monitor the occurring events by keeping track of part movements. Whenever a process failure is detected, the diagnosis procedure is triggered, and a recovery sequence is imposed.

The optimal control of a single machine processing a certain number of jobs and modelled as a discrete-event dynamic system is considered. The sequence of jobs is fixed, whereas their timing and sizes represent the control variables of the system. The objective function to be optimised is a weighted sum of the inventory cost and of the quadratic deviations of job lot-sizes and completion times from those specified by the production demand. The problem is formalised as a multistage optimal control problem and solved by a procedure making use of dynamic programming techniques; in this way, a proper real time control scheme is obtained.

This paper formalizes a unique decisional framework to state different problems relevant to automated manufacturing systems, such as production planning, material requirements planning, and online management. Since the manufacturing systems are modelled as discrete-event systems, the (timed) Petri net formalism can be adopted to represent their dynamics. Moreover, in order to make the decisional problems addressed computationally tractable, it is assumed that the manufacturing systems can be represented by means of a particular class of timed Petri nets-the timed event graphs. Two particular optimization problems are discussed in the paper: the first one is relevant to the minimization of production and inventory costs, while satisfying a pre-specified production demand; and the second one refers to an online management problem in which the deviations between the actual system behaviour and a pre-defined nominal behaviour are sought to be minimized.

A model of manufacturing systems is considered in which several classes of products are repeatedly manufactured, according to the requirements specified by customer orders. The problem considered is the development of control strategies capable of reacting to deviations of the system behaviour from a nominal behaviour defined solving a suitable problem at the scheduling level. The degrees of freedom allowed for the control problem are: the execution times of some elementary operations, the lot-sizes, and the variables expressing the timing of the production process. It is shown that, under suitable hypotheses about the nominal behaviour, the system can be represented by means of a particular class of timed Petri nets. Moreover, the structure of the control problem is that of a continuous variable problem with quadratic cost and linear (or sometimes quadratic) constraints.

A novel hybrid model for transportation systems is proposed, in which the functioning is represented via a continuous-time model whenever no changes in the traffic flow occur, whereas the changes in the system behaviour are modelled through a discrete event system coupled with the continuous-time system. Via the discrete-event part of the hybrid model, its continuous-time part is adapted according to the variation occurred in the system behaviour. The hybrid modelling methodology proposed is based on the use of a special class of Petri nets, the hybrid stochastic Petri nets (HSPN). These nets are composed of some discrete parts, and some continuous parts, so that a hybrid Petri net can be viewed as a combination of an usual (discrete) Petri net and a continuous Petri net, the 'fluid' version of usual timed Petri nets. The discrete part of the HSPN 'regulates' the continuous PN, which models the functioning of the transportation system. This results in the model reacting to any change to the current system behaviour by adapting the traffic parameters to the new state.

A model of a cyclic manufacturing system representable as a deterministic timed event graph is considered in this paper. It is possible to apply analytical techniques for performance evaluation of such systems. On this basis, a two-level optimization problem is considered. The higher level refers to the maximization of the system productivity with respect to the assignment of operations to machines, the lot sizes, and the service sequences at the various machines, assuming that unlimited work-in-progress and buffers of finite dimension are available. The lower level deals with the minimization of an aggregate cost taking into account the work-in-progress and the buffer dimensions.