In this article, we study the NP-Hard combinatorial optimization problem of the minimum initial marking (MIM) computation in labeled Petri net (L-PN) while considering a sequence of labels to minimize the resource consumption in a flexible manufacturing system (FMS), and we propose an approach based on the ant colony optimization (ACO) precisely the extension Rank-based ACO to optimal resource allocation and scheduling in FMS. The ACO meta-heuristic is inspired by the behavior of ants in foraging based on pheromones deposit. The numerical results show that the proposed algorithm obtained much better results than previous studies.

Safety and risks prevention in numerous industrial domains require a systematic monitoring of areas with high-risk level. The surveillance tasks can be assessed by smart sensor systems. This work is devoted to configure a patrol of mobile robots associated with sets of sensors in order to perform a given sequence of surveillance tasks at minimal cost. The environment to be monitored as the trajectories of the robots are modelled with Petri nets. The objective is reformulated as an initial marking optimization problem in the Petri net framework. A real case is considered with the port area in Le Havre City.

This paper deals with the problem of estimating the Minimum Initial Marking (MIM) of Labeled Petri Nets (L-PN). By the observation of a sequence of labels, we determine the set of possible MIMs related to a given L-PN through an approach based on GRASP (Greedy Randomized Adaptive Search Procedure) inspired method – GMIM. The objective is to get the maximum of feasible MIMs by exploring the search space and giving best solutions for real time cyber systems in short time. We consider four basic assumptions during the reasoning: (i) the L-PN structure is known; (ii) for each transition of L-PN, a label is associated, (iii) the label sequence is known, and (iv) all transitions of L-PN are observable. We show the validity and efficiency of our approach by applying the proposed GMIM metaheuristic to two validation examples: Initialization of two parallel machines (example widely cited in literature) and resources allocation in a monitoring problem via mobile robot network.

This work develops a technique for estimating the minimum initial marking (an initial marking with the minimum total token number) of a real-time system modelled by a P-Time labelled Petri net system under partial observation. Indeed, the set of events is partitioned into a set of observable events, which can be detected by an external agent and a set of unobservable ones. Furthermore, some observable events can be associated to the same observation, i.e., they produce the same output signal and are called undistinguishable. In addition, the obtained marking must be consistent with a given label occurrence vector (LOV). The proposed method is based on an iterative procedure combined with a schedulability analysis technique for particular behaviors of the studied system.

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This paper is concerned with the macroscopic modeling, simulation of traffic flow on junctions and application of traffic control on intersections. More precisely, we deal with a generic class of second order models, known in the literature as the GSOM family. While classical approaches focus on the Eulerian point-of-view, here we recast the model using its Lagrangian coordinates and we treat the junction as a specific discontinuity in Lagrangian framework. We propose a complete numerical methodology based on a finite difference scheme for solving such a model and we provide an application of traffic control on intersections and some numerical examples. Notice that in our scheme vehicles are discretized into packets of N particles. Hence, our scheme can be seen as a microscopic car-following model for the particular choice of N = 1.

This paper deals with the Job-shop scheduling problem. We propose to solve this problem by exploiting the Particle Swarm Optimization Global Velocity (PSOVG) algorithm. The PSOVG by its nature focus on the global optimum within a given set of solutions. In this paper a solution is PSO particle, it consists in a possible scheduling solution for the given problem. The PSO-VG-JSSP is a PSO-VGO with a constraints control policy embedded in, allowing to detect and remove non admissible solutions. In this paper the particle representing a non admissible solution is simply removed and replaced by a new random particle.

There is a growing interest in intelligent maintenance in which supervision and its main component - the diagnosis - occupy a fundamental place leaning more towards dynamic monitoring applications and therefore to prognosis. In this sense, the use of artificial intelligence techniques has been under much research. The ability to monitor without modeling the system in an environment or if its subject to modifications and permanent reconfigurations, is of obvious interest for manufacturers. In this paper, we plan to improve the performance of a chemical reactor. We wish to add changes to the operation of the reactor in order to make it more secure by: a Adding a chain that measures temperature, alerting the operator if the threshold is exceeded and requesting the operator to open a valve (or PLC) to open a deluge system that lowers the temperature.

This paper seeks to establish within a formal framework for modeling the robust control laws of manufacturing systems to temporal and non-temporal constraints. The goal is to conclude the type from the production system robustness. A methodology of robust control construction generating the margins of passive and active robustness is elaborated. The redundancy of the robustness of the elementary parameters between passive and active is used. Indeed, local models are developed; each of them modeling resource robustness. Then, a synchronized assembly of these models enabled us to constitute our final model. The Intervals Constrained Petri Nets (ICPN) tool is used. To this goal, a whole of definitions, lemmas and theorems are committed and affirmed per applicable examples.