https://utt.hal.science/hal-02502522Laribi, ImaneImaneLaribiMELT - Manufacturing engineering laboratory of Tlemcen - Université Aboubekr Belkaid - University of Belkaïd Abou Bekr [Tlemcen]Yalaoui, FaroukFaroukYalaouiLOSI - Laboratoire d'Optimisation des Systèmes Industriels - ICD - Institut Charles Delaunay - UTT - Université de Technologie de Troyes - CNRS - Centre National de la Recherche ScientifiqueSari, ZakiZakiSariLOSI - Laboratoire d'Optimisation des Systèmes Industriels - ICD - Institut Charles Delaunay - UTT - Université de Technologie de Troyes - CNRS - Centre National de la Recherche ScientifiqueMELT - Manufacturing engineering laboratory of Tlemcen - Université Aboubekr Belkaid - University of Belkaïd Abou Bekr [Tlemcen]Permutation flow shop scheduling problem under non-renewable resources constraintsHAL CCSD2019schedulingnon-renewable resourcesoptimisationmathematical programminggenetic algorithmlocal searchpermutation flow shop[INFO.INFO-RO] Computer Science [cs]/Operations Research [cs.RO][INFO.INFO-DS] Computer Science [cs]/Data Structures and Algorithms [cs.DS][MATH.MATH-OC] Mathematics [math]/Optimization and Control [math.OC]Gavrysiak, Daniel2020-03-09 12:26:032023-03-24 14:53:152020-03-09 12:26:03enJournal articles10.1504/IJMMNO.2019.1004941The majority of flow shop scheduling problems considers machines as the only resource. However, in most real-life manufacturing environments, jobs for their processing on machines may require additional non-renewable resources. Considering such resources, the scheduling problem is more realistic and much harder to solve. In this paper, we investigate the permutation flow shop scheduling problem subject to non-renewable resources constraints. The objective is to find a schedule that minimises the maximum completion time. An integer linear programming model is developed. Because of the computation time constraint, we propose an approximate resolution method based on genetic algorithm. To obtain better and more robust solutions, the Taguchi method is performed for tuning the parameters and operators of the algorithm. Furthermore, a local search is proposed to enhance the searching ability. Finally, computational experiments are conducted to evaluate the performance of both mathematical model and algorithm on different configurations of non-renewable resources availability.