An integrated manufacturing/remanufacturing system is considered in this paper with the aim of scheduling the operations of the manufacturing plant. The system is partially closed in the sense that the raw materials, necessary for assembling the final products, can be obtained both from an internal remanufacturing plant (which disassembles returned products) and from external suppliers. The manufacturing system is modelled as a flexible flow shop whose stages represent the different assembly phases leading to the final products. In this paper, an original event-based mixed integer programming (MIP) formulation is presented, whose objective consists of minimizing, as primary objective, the weighted number of tardy jobs and, as secondary ones, the fixed and variable purchase costs of raw materials possibly acquired from external suppliers. Due to the complexity of the problem, the MIP formulation can be used to solve only small instances. For this reason, a matheuristics is proposed, which consists of three interoperating mathematical programming models: the first model assigns the jobs to the machines; the second model sequences the jobs on the machines; the third model defines the external supplies, taking into account the component availability constraints. A preliminary computational analysis shows the effectiveness of the proposed algorithm.

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.

Distribution warehouses are considered in this paper with the aim of scheduling the transportation of pallet and roll pallet loads from the storage area to the gates at which the trucks arrive. Transportation activities are carried out by forklift AGVs that can move freely along the warehouse's aisles (guide paths are not considered). In this paper, an optimization procedure is proposed, which is based on three sequential phases. In the first phase, the number of AGVs to be allocated to each arrived truck is determined; in the second phase, an AGV-to-truck assignment problem is solved; in the third phase, the single tasks are assigned to and sequenced on the AGVs. All phases are based on the formalization and solution of a specific mathematical programming problem, and a heuristic procedure is also proposed to solve a part of the third phase.

This paper considers a hybrid remanufacturing and manufacturing system on a closed-loop supply chain. The system manufactures a set of new products characterized by a multi-level structure through multi-stage assembly operations. The required raw or basic parts can be acquired new from suppliers or provided as new by a de-manufacturing facility which performs a remanufacturing process from acquired old products returned by customers. The quality of returned products has impact on the quantity of recovered basic parts which can be assumed as good as new, and on the duration of the remanufacturing process. The considered problem is to determine the production lots for the system machines as well as the quantity of new basic parts and retuned products to be acquired in order to satisfy a deterministic demand in the time buckets of the planning period. The performance criterion to be minimized includes the acquisition costs for the new and returned items, inventory and production costs, recovering and disposal costs, and tardiness costs. A mixed-integer programming model is proposed and its effectiveness is demonstrated by experiments on a case study.

Automated distribution warehouses in which pallet and roll pallet loads are transported by means of forklift AGVs are considered in this work, with the objective of defining a mathematical model which accurately represents the behaviour of AGVs in the system. AGVs can move freely in the warehouse (an open path AGV system is adopted), and their transportation activities can be modelled as a sequence of elementary or basic actions. In the paper, a coloured Petri net (CPN) model is proposed. It allows representing any sequence of elementary actions of AGVs (including pick-up and drop-off activities), and accurately models the interactions among AGVs, in order to guarantee the safety during the execution of activities. The CPN model can be used to analyse and implement deadlock prevention and deadlock recovery strategies, and it has been adopted in the building of a discrete-event simulator which is employed to analyse the system's performance and to evaluate scheduling policies for transportation tasks.

A specific class of production systems is considered in this paper with the aim of minimizing inventory and production costs, over a finite horizon. In such a class of systems, all processes (arrival and departure of parts, production, demand to be satisfied) are modelled as piece-wise constant functions that change their values at asynchronous time instants. The decision variables are the constant rate at which raw parts enter the system and the service rate at the various time instants. In the paper, both the case of completely known demand and that of incomplete demand is taken into account. In both cases, a solution algorithm is provided in order to determine the optimal (or sub-optimal, in case of incomplete demand) solution of the optimization problem.

In this paper, supply chain nodes are considered, with the aim of optimizing the production of parts belonging to different classes (the case of two classes is here taken into account). The production node is modelled by a discrete-event model, being the system state, consisting of the continuous level of input and output inventories, affected by the asynchronous processes relevant to part arrivals and product departures. Moreover, also the resource capacity of the node is represented by a continuous variable (to be shared among the two classes). In the paper, two optimization problems are proposed: the former is only stated due to its intrinsic complexity, whereas the latter (a simplified version of the former) is stated and solved. In the latter problem, an optimization is performed each time an event occurs, in order to determine which the next event is, when the next event will occur, how many raw parts/products will arrive/depart, and which portions of production capacity are assigned to the two classes.

Timed Petri nets are considered in this paper, with the aim of providing an efficient tool for the modelling and the analysis of timed models, such as automated manufacturing systems (AMS) and intelligent transportation systems (ITS). In the proposed model, timings are associated with transitions, and firing times are deterministic values. Such a model, the deterministic-timed Petri nets (DTPN), is characterized by a certain number of assumptions. In a DTPN the system state is represented by the joint information consisting of the marking of places and of the residual firing times of timed transitions, and, under the assumptions which characterize the proposed model, it is possible to describe the evolution of the system state in a relatively simple way. In the paper, three subclasses of DTPN are also introduced, and two examples relevant to the application of the proposed model to AMS e ITS are discussed.

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.

In this paper, a novel methodology aiming at the management and control of traffic flows within urban areas, and specifically at the reduction of traffic congestion, is presented. The propose methodology is based on a multi-level system architecture integrating two decision levels, namely the dynamic route guidance level and the traffic signaling control level. The former level implements an approach for the determination of the optimal turning rates of vehicles traveling within the considered urban area. Basing on these values, in the latter level, a mathematical programming-based methodology determines the optimal duration of the phases of various traffic signaling systems in the considered area. A third level, namely the data collection and state estimation level, is also included in the proposed architecture and discussed in the paper.

In this paper, supply chain networks are considered in order to solve decision problems at the tactical level. The proposed MRP multi-site model is characterized by the presence of several production sites, several raw material suppliers, and by non-negligible transportation times and costs. Decision problems, as the choice of a particular supplier (competing with the others) or the determination of the site over which to perform the next operation, are based on the formulation of a mathematical programming problem that minimizes all costs involved in the network.

In this paper, the formalization of performance optimization problems for flexible manufacturing systems is considered. In the adopted model, production resources are multipurpose, in the sense that they are able to perform more than one kind of operation; on the other hand, an operation can in general be carried out by more than one resource. This flexibility leads to the problem of optimally managing the part routing (i.e., the assignment of an operation to a resource) and the local scheduling (i.e., the choice of the next operation in a resource operation sequence). Thus, the performance optimization of such systems requires the solution of combinatorial optimization-problems. In this connection, the introduction of maximum gap policies makes it possible to reduce the number of combinatorial degrees of freedom, by writing constraints,. deriving from the structure of such policies, on the dynamic behaviour of the system. The formalization of the optimization problem is carried out by using a system representation built via the use of deterministically transition-timed Petri net.

The problem of managing and controlling automatic guided vehicles (AGV) in manufacturing shop floor systems is addressed. In such systems, resources are connected through a network of paths that are limited-capacity shared resources as, in general, two or more AGVs cannot use the same section at the same time. The proposed approach makes use of the integration between a multi-agent system and Petri nets. The behaviour and the interactions among AGVs, path sections, and all other resources in the shop floor system are represented by means of Petri nets, whereas agents handle decisional activities. Two architectures are proposed: in the first, decisional agents are associated with path sections (path agents) whereas in the second scheme decisional agents are AGVs themselves (AGV agents).

We provide a formal approach to obtain a discrete event system (DES) representation of a flexible manufacturing system (FMS). The complexity of real FMSs typically yields formidable problems in modeling such systems and in their representation by means of any predefined formalism, for instance Petri nets (PNs). A technique which allows one to obtain the PN from a description of a system is called a synthesis procedure. A synthesis procedure is presented which is based on a modular conception: a generic FMS can be easily divided, from a physical point of view, into a set of connected resources totally independent one from the other. Once such resources have been identified, we represent them by means of a set of independent subnets, the Petri net modules (PNMs), and then we can apply the synthesis procedure. For this reason, the proposed procedure has been called modular structural synthesis.

In this paper the Information System management problem is considered, with special reference to the ones which model Flexible Manufacturing Systems. An approach based on Relational Databases and Petri net integration is presented: the former are used to represent passive and static information whereas the latter to model working processes of information inside the Information System. In the presented solution also Petri nets are modeled by means of relations in order to make totally homogeneous the whole Information System structure.

Information systems (ISs) development process is usually characterized by several phases, among which a critical role is played by conceptual and logical design. To model static information structures, entity-relationship (ER) diagrams are probably the widespread formalism. To represent dynamic information, Petri nets are a flexible formalism as they are able to model processes from an aggregate point of view to a very detailed one. The purpose of this work is to try to integrate these two different formalisms in order to allow an automatic translation of conceptual models of process into a relational structure. Then, the functionality of an RDBMS could be extended by including in the relational database also a representation of dynamic information, and allowing the RDBMS to directly control the process execution.

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.