hal-02502522
https://utt.hal.science/hal-02502522
doi:10.1504/IJMMNO.2019.100494
[CNRS] CNRS - Centre national de la recherche scientifique
[UNIV-TROYES] Université de Technologie de Troyes
[INSMI] CNRS-INSMI - INstitut des Sciences Mathématiques et de leurs Interactions
[TDS-MACS] Réseau de recherche en Théorie des Systèmes Distribués, Modélisation, Analyse et Contrôle des Systèmes
[UTT] UTT
[LOSI] LOSI
Permutation flow shop scheduling problem under non-renewable resources constraints
Laribi, Imane
Yalaoui, Farouk
Sari, Zaki
[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]
ART
scheduling
non-renewable resources
optimisation
mathematical programming
genetic algorithm
local search
permutation flow shop
The 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.
2019
en
International Journal of Mathematical Modelling and Numerical Optimisation
Inderscience