Multi-Objective Optimization of Production Lines using Multi-Agent Systems Modeling and Genetic Algorithms: A Case Study

doi:10.23940/ijpe.25.04.p6.226234

Abstract

Abstract: This research focuses on multi-objective optimization of production lines using advanced techniques to improve decision-making and operational efficiency. As a part of Industry 4.0, our study uses multi-agent systems (MAS) modeling and genetic algorithms (GA) to solve the complexities of this process. A large-scale modeling and simulation framework was developed, demonstrating the effectiveness of these approaches in improving the performance and adaptability of production systems. This approach improves the flexibility and responsiveness of industrial environments while ensuring their compliance with contemporary industry standards.

Key words: industry 4.0, multi-agent systems modeling, genetic algorithms, multi-objective optimization, performance

Meroua Sahraoui, Ahmed Bellaouar, Abdoul-Razac Sané, and Fouad Maliki. Multi-Objective Optimization of Production Lines using Multi-Agent Systems Modeling and Genetic Algorithms: A Case Study [J]. Int J Performability Eng, 2025, 21(4): 226-234.

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References

[1] Tjahjono B., Esplugues C., Ares E., andPelaez G., 2017. What does industry 4.0 mean to supply chain?.Procedia Manufacturing, 13, pp. 1175-1182.
[2] Baptista L.F., andBarata J., 2021. Piloting industry 4.0 in SMEs with RAMI 4.0: an enterprise architecture approach.Procedia Computer Science, 192, pp. 2826-2835.
[3] Salih A., Alsalhi L., andAbou-Moghli A., 2024. Entrepreneurial orientation and digital transformation as drivers of high organizational performance: Evidence from Iraqi private bank. Uncertain Supply Chain Management,12(1), pp. 9-18.
[4] Abusalma A., Al-Oraini B., Al-Daoud K., andAlshurideh M.T., 2024. The impact of supply chain performance on financial performance: dimensions of the SCOR model.Uncertain Supply Chain Manag, 11(3), pp. 1409-1416, 2024.
[5] Velda A.M.E.,2019. Impact of supply chain management practices on financial performance: case study of automotive suppliers in morocco. In2019 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA), pp. 1-5.
[6] Collins A., Petty M., Vernon-Bido D., andSherfey S., 2015. A call to arms: standards for agent-based modeling and simulation.Journal of Artificial Societies and Social Simulation, 18(3), 12.
[7] Yang W., andTakakuwa S., 2017. Simulation-based dynamic shop floor scheduling for a flexible manufacturing system in the industry 4.0 environment. In2017 Winter Simulation Conference (WSC), pp. 3908-3916.
[8] Xie J., andLiu C.C., 2017. Multi-agent systems and their applications. Journal of International Council on Electrical Engineering,7(1), pp. 188-197.
[9] Shen W., Hao Q., Yoon H.J., andNorrie D.H., 2006. Applications of agent-based systems in intelligent manufacturing: an updated review. Advanced Engineering INFORMATICS,20(4), pp. 415-431.
[10] Leitão P., Colombo A.W., andKarnouskos S., 2016. Industrial automation based on cyber-physical systems technologies: prototype implementations and challenges.Computers in Industry, 81, pp. 11-25.
[11] Hussain M.S., andAli M., 2019. A multi-agent based dynamic scheduling of flexible manufacturing systems.Global Journal of Flexible Systems Management, 20, pp. 267-290.
[12] Sabar M., Montreuil B., andFrayret J.M., 2009. A multi-agent-based approach for personnel scheduling in assembly centers. Engineering Applications of Artificial Intelligence,22(7), pp. 1080-1088.
[13] Bouaouda A., andSayouti Y., 2022. Hybrid meta-heuristic algorithms for optimal sizing of hybrid renewable energy system: a review of the state-of-the-art. Archives of Computational Methods in Engineering,29(6), pp. 4049-4083.
[14] Jebari H., El Azzouzi S.R., andSamadi H., 2015. Hybridation des métaheuristiques pour la résolution de problème d'ordonnancement multi-objectif dans un atelier flow-shop. InXèMe ConféRence Internationale: Conception Et Production IntéGréEs.
[15] Ojstersek R., Brezocnik M., andBuchmeister B., 2020. Multi-objective optimization of production scheduling with evolutionary computation: A review. International Journal of Industrial Engineering Computations,11(3), pp. 359-376.
[16] Deb K., Pratap A., Agarwal S., andMeyarivan T.A.M.T., 2002. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation,6(2), pp. 182-197.
[17] Deb K.,2011. Multi-objective optimisation using evolutionary algorithms: an introduction. InMulti-Objective Evolutionary Optimisation for Product Design and Manufacturing, pp. 3-34.
[18] Zhou A., Qu B.Y., Li H., Zhao S.Z., Suganthan P.N., andZhang Q., 2011. Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm and Evolutionary Computation,1(1), pp. 32-49.