18 Int. J. Computational Intelligence Studies, Vol. 9, Nos. 1/2, 2020 Solving flexible job-shop problem with sequence dependent setup time and learning effects using an adaptive genetic algorithm Ameni Azzouz*, Meriem Ennigrou and Lamjed Ben Said Institut Sup´ erieur de Gestion, SMART Lab, Universit´ e de Tunis, Tunisia Email: ameni.azzouz@isg.rnu.tn Email: meriem.ennigrou@enit.rnu.tn Email: Lamjed.bensaid@isg.rnu.tn *Corresponding author 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. Reference to this paper should be made as follows: Azzouz, A., Ennigrou, M. and Ben Said, L. (2020) ‘Solving flexible job-shop problem with sequence dependent setup time and learning effects using an adaptive genetic algorithm’, Int. J. Computational Intelligence Studies, Vol. 9, Nos. 1/2, pp.18–32. Biographical notes: Ameni Azzouz received her BSc and MS from the University of Mannouba, Tunis, Tunisia, in 2010 and 2013, respectively and her PhD from the University of Tunis in 2017, all in Computer Science. She is currently a research member of SMART Lab. with University of Tunis, Copyright © 2020 Inderscience Enterprises Ltd.