This study presents a comprehensive approach to solving an advanced Multi-Robot Task Allocation (MRTA) problem, in which heterogeneous agents, initially stationed at different depots, are required to perform a set of spatially distributed tasks. A key challenge lies in determining which agent performs each task and defining the order in which each agent executes the assigned tasks, while optimizing travel costs and respecting energy constraints. To address this problem, we propose an approach that combines the Greedy Randomized Adaptive Search Procedure (GRASP) with 2-Opt local search. A comparative analysis was conducted against a Mixed Integer Linear Programming (MILP) solver and standard GRASP variants. The results demonstrate that the GRASP + 2-Opt approach strikes a favorable balance between optimality and execution time. This work provides practical insights for applications such as industrial inspection and environmental monitoring, where autonomous multi-robot coordination and energy constraints are essential for sustained and reliable operation.

This study presents a comprehensive approach to solving an advanced Multi-Robot Task Allocation (MRTA) problem, in which heterogeneous agents, initially stationed at different depots, are required to perform a set of spatially distributed tasks. A key challenge lies in determining which agent performs each task and defining the order in which each agent executes the assigned tasks, while optimizing travel costs and respecting energy constraints. To address this problem, we propose an approach that combines the Greedy Randomized Adaptive Search Procedure (GRASP) with 2-Opt local search. A comparative analysis was conducted against a Mixed Integer Linear Programming (MILP) solver and standard GRASP variants. The results demonstrate that the GRASP + 2-Opt approach strikes a favorable balance between optimality and execution time. This work provides practical insights for applications such as industrial inspection and environmental monitoring, where autonomous multi-robot coordination and energy constraints are essential for sustained and reliable operation.

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This paper deals with the optimisation of a multi-robot inspection mission in an industrial area. We aim to solve a specific combinatorial optimisation problem where a team of sensing mobile robots must gather several measurement tasks distributed over the state space. Based on previous work where a Hybrid Filtered Beam Search (HFBS) approach solves task assignment and planning for this specific problem, we focus on the planning aspect to improve the computed solution. Therefore, this planning problem is modeled as a one-depot multiple Travelling Salesman Problem (mTSP). As the performance of HFBS depends on the challenging selection of its suitable parameters, and considering the coupling between task assignment and task planning problems, we propose a local search algorithm that improves the solution and deals with the optimality issue.

This paper concerns the diagnosis of faults for stochastic discrete event systems that behave according to non-Markovian dynamics. k -bounded partially observed Petri nets are used to model the system structure and the sensors. Stochastic processes with probability density functions (pdf) of finite support define the dynamics. Structural and temporal faults are considered. Structural faults correspond to specific sequences of events that should satisfy precedence conditions defined with patterns. Temporal faults are defined with time constraints that must be fulfilled by the firing durations. The probabilities of consistent trajectories are computed with a numerical scheme from the collected timed measurements. The advantage of the proposed scheme is that it can be used for a large variety of pdf that may be defined either with an analytical or a numerical description. It works also for various time semantics. Diagnosis in terms of probability for faulty patterns and temporal constraints is established as a consequence.

This paper is about the incremental computation of control sequences for complex discrete event systems (DES) in uncertain environments. Transition-timed Petri nets (T-TPN) that behave under earliest firing policy are used to model a class of flexible manufacturing systems (FMS) where operations are proceeded with partial routing flexibility (namely hybrid FMS). Uncertainties are assumed to occur due to interruption of operations or unreliable resources. The objective is to find a control sequence from an initial state to a reference one with a trade-off between performance and robustness. For that, a systematic T-TPN-based multi-level formalism is used for the modelling. Then a new cost function is introduced to estimate the time and risk to reach the reference. A modified beam search algorithm is also proposed to selectively explore the PN state space and search for the best control sequence.

This paper concerns the diagnosis of temporal faults for stochastic discrete event systems that behave according to non-Markovian dynamics. K-bounded partially observed Petri nets are used to model the system structure and the sensors. Stochastic processes with probability density functions of finite support model the dynamics. Temporal faults are defined according to time constraints that must be fulfilled by the firing durations. From the proposed modelling and the collected timed measurements, the probabilities of consistent trajectories are computed with a numerical scheme. The advantage of the proposed scheme is that it can be used for a large variety of probability density functions. It works also for various time semantics. Diagnosis in terms of probability is established as a consequence.

In this paper a systematic method is proposed for the modeling of hybrid flexible manufacturing systems (FMS) with timed Petri nets including buffer places. Hybrid FMS are workshops consisting of a combination of operations, some in job shop and others in open shop. Three basic functions are proposed to iteratively construct the complete model. The method automatically builds the incidence matrices, the initial marking vector and the temporal parameter vector from a synthetic description of the workshop.

This paper deals with problems of detection and isolation of temporal faults in timed stochastic discrete event systems. Partially labeled timed Petri nets are used to model the considered systems. Temporal faults corresponding to significant variations of the support of the probability density function (pdf) are considered. A pdf represents the firing duration of each transition. A Moving Average control chart (also known as a Moving Mean chart) is applied in order to detect the variation of mean duration. The advantages of the proposed analysis are to detect variations in time series when parameters vary slowly and to isolate the faults thanks to the signature table.

In this paper, we consider the identification problem of stochastic and deterministic stochastic Petri nets (PNs). The approach herein proposed consists of inferring a PN structure and identifying its parameters. Hence, the first step leads to the synthesis of a PN structure with the measurable sequence of events and states. This approach determines the measurable part and estimates the nonmeasurable part of the PN to be established. Once both parts are obtained, the PN structure and the initial marking of the nonmeasurable places are obtained thanks to the integer linear programming technique. In the second step of this approach, the parameters of the obtained model are estimated. Stochastic and deterministic stochastic PNs with deterministic and exponentially distributed transition durations are considered. A systematic identification method is proposed based on event sequences that are recorded by supervision systems. This method is based on a Markov model whose state space is isomorphic to the reachability graph of the untimed PN model.

This paper is about fault detection and identification of discrete event systems. The proposed approach is based on Petri nets (PNs) that are used to design reference and faulty models. The main contribution concerns the design and identification of these models according to the statistical analysis of the alarm sequences that are collected on the considered system. The model structure is described as a state graph, and the parameters of the probability density functions (pdfs) for transition firing periods are estimated. Normal and exponential pdfs are considered, and estimation is detailed in case of concurring behaviors. The reference models, described as timed PNs, are then used for fault detection and isolation issues. Finally, stochastic PNs with normal and exponential pdfs are considered to include a representation of the faulty behaviors.