Title: Solving flexible job-shop problem with sequence dependent setup time and learning effects using an adaptive genetic algorithm
Authors: Ameni Azzouz; Meriem Ennigrou; Lamjed Ben Said
Addresses: Institut Supérieur de Gestion, SMART Lab, Université de Tunis, Tunisia ' Institut Supérieur de Gestion, SMART Lab, Université de Tunis, Tunisia ' Institut Supérieur de Gestion, SMART Lab, Université de Tunis, Tunisia
Abstract: For the most schedulling problems studied in literature, job processing times are assumed to be known and constant over time. However, this assumption is not appropriate for many realistic situations where the employees and the machines execute the same task in a repetitive manner. They learn how to perform more efficiently. As a result, the processing time of a given job is shorter if it is scheduled later, rather than earlier in the sequence. In this paper, we consider the flexible job-shop problem (FJSP) with two kinds of constraint, namely, the sequence-dependent setup times (SDST) and the learning effects. Makespan is specified as the objective function to be minimised. To solve this problem, an adaptive genetic algorithm (AGA) is proposed. Our algorithm uses an adaptive strategy based on: 1) the current specificity of the search space; 2) the preceding results of already used operators; 3) their associated parameter settings. We adopt this strategy in order to maintain the balance between exploration and exploitation. Experimental studies are presented to assess and validate the benefit of the incorporation of the learning process to the SDST-FJSP over the original problem.
Keywords: schedulling problem; genetic algorithm; adaptive strategy; learning effects.
International Journal of Computational Intelligence Studies, 2020 Vol.9 No.1/2, pp.18 - 32
Received: 28 Feb 2018
Accepted: 06 Sep 2018
Published online: 02 Apr 2020 *