Production chain modeling based on learning flow stochastic petri nets

• Published in: Soft computing (Berlin, Germany) Vol. 28; no. 19; pp. 10767 - 10779
• Main Authors: Mesmia, Walid Ben, Barkaoui, Kamel
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2024; Springer Nature B.V
• Subjects: Algebraic; Algorithms; Analytical Methods in Soft Computing; Artificial Intelligence; Automation; Burning time; Case studies; Computational Intelligence; Control; Empowerment; Engineering; Foundation; Graphical representations; Machine learning; Mathematical Logic and Foundations; Mechatronics; Modelling; Multiagent systems; Performance evaluation; Petri nets; Robotics; Simulation; Specification; Verification; Stochastic Petri Nets; Multi-agent system; Machine learning
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-024-09865-y

In this study, we propose a model called   LFSPN ,  which serves as an extension of stochastic Petri nets dedicated to the multi-agent systems paradigm. The main objective is to specify, verify, validate, and evaluate the flow of materials within an automated production chain. We illustrate the practicality of our model by engaging in a systematic process of modeling and simulation of a production chain involving material flow. To evaluate the performance, we employ a mobile learning agent, which has distinct characteristics, namely mobility and learning. So, the distinctive characteristics of the learning agent are manifested in two key behaviors: mobility and learning. Notably, the learning agent is equipped with a flexible learning algorithm that integrates stochastic elements based on transitions. We suggest using a MATLAB simulation to determine the firing time of each transition within a sequence, guided by three different probability laws (exponential, normal, and log-normal). This sequence is designed to optimize the production process objective while facilitating learning cycles through agent rewards, specified by a production and consumption of tokens in our evolving model. We validate the effectiveness of our model by performing a comparative analysis with similar existing works. The advantages of our   LFSPN   model are twofold. Firstly, it offers a representation with two levels of abstraction: a graph representing the classic components of an SPN, and an additional layer encompassing the learning and migration aspects inherent to a mobile learning agent. Secondly, our model stands out for its flexibility and simulation simplicity.