Flexible job shop scheduling problem with reconfigurable machine tools: An improved differential evolution algorithm

• Published in: Applied soft computing 2020-09, Vol.94, p.106416, Article 106416
• Main Authors: Mahmoodjanloo, Mehdi, Tavakkoli-Moghaddam, Reza, Baboli, Armand, Bozorgi-Amiri, Ali
• Format: Article
• Language: English
• Subjects: Computer Science; Configuration-dependent setup times; Flexible job shop; Industry 4.0; Nelder–Mead mutation strategy; Operations Research; Self-adaptive differential evolution
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2020.106416

Developing reconfigurable machine tools (RMTs) has attracted increasing attention recently. An RMT can be utilized as a group of machines, which can obtain different configurations to satisfy manufacturing requirements. This paper deals with a production scheduling problem in a shop-floor with RMTs as an extension of a flexible job shop scheduling problem (FJSSP). To begin with, two mixed-integer linear programming models with the position- and sequence-based decision variables are formulated to minimize the maximum completion time (i.e., makespan). The CPLEX solver is used to solve the small- and medium-sized instances. The computational experiments show that the sequence-based model significantly outperforms the other one. Since even the sequence-based model cannot optimally solve most of the medium-sized problems, a self-adaptive differential evolution (DE) algorithm is proposed to efficiently solve the given problem. Moreover, the effectiveness of the proposed algorithm is enhanced by introducing a new mutation strategy based on a searching approach hired from a Nelder–Mead method. The performance of the proposed method and three other well-known variants of the DE algorithm are first validated by comparing their results with the results of the sequence-based model. Additional experiments on another data set including large-sized problems also confirm that the proposed algorithm is extremely efficient and effective. •Addressing the scheduling problem in a shop-floor that contains reconfigurable machines.•Proposing two MILP models with position- and sequence-based decision variables.•Deriving a lower bound of the makespan for the considered problem.•Developing a self-adaptive differential evolution algorithm to solve the problem.•Introducing a new mutation strategy inspired from the Nelder–Mead method.