Title: Permutation flow shop scheduling problem under non-renewable resources constraints

Authors: Imane Laribi; Farouk Yalaoui; Zaki Sari

Addresses: MELT Laboratory, Department of Electrical and Electronics Engineering, University Abou-Bekr Belkaid, Tlemcen, Algeria; LOSI, UMR-CNRS 6281 Laboratory, Charles Delaunay Institute, University of Technology of Troyes, France ' LOSI, UMR-CNRS 6281 Laboratory, Charles Delaunay Institute, University of Technology of Troyes, France ' MELT Laboratory, Department of Electrical and Electronics Engineering, University Abou-Bekr Belkaid, Tlemcen, Algeria

Abstract: 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.

Keywords: scheduling; non-renewable resources; optimisation; mathematical programming; genetic algorithm; local search; permutation flow shop.

DOI: 10.1504/IJMMNO.2019.100494

International Journal of Mathematical Modelling and Numerical Optimisation, 2019 Vol.9 No.3, pp.254 - 286

Received: 14 Apr 2018
Accepted: 05 Jul 2018
Published online: 29 Jun 2019 *

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