Abstract
To meet the multi-cooperation production demand of enterprises, the distributed permutation flow shop scheduling problem (DPFSP) has become the frontier research in the field of manufacturing systems. In this paper, we investigate the DPFSP by minimizing a makespan criterion under the constraint of sequence-dependent setup times. To solve DPFSPs, significant developments of some metaheuristic algorithms are necessary. In this context, a simple and effective improved iterated greedy (NIG) algorithm is proposed to minimize makespan in DPFSPs. According to the features of DPFSPs, a two-stage local search based on single job swapping and job block swapping within the key factory is designed in the proposed algorithm. We compare the proposed algorithm with state-of-the-art algorithms, including the iterative greedy algorithm (2019), iterative greedy proposed by Ruiz and Pan (2019), discrete differential evolution algorithm (2018), discrete artificial bee colony (2018), and artificial chemical reaction optimization (2017). Simulation results show that NIG outperforms the compared algorithms.
Highlights
Introduction manufacturing and scheduling have become a trend in the production industry because many enterprises are Under the influence of globalization, distributed gradually turning to multiregional cooperation
We proposed an NIG algorithm based on a two-stage local search strategy for solving DPFSPSDST
Based on the distributed feature of DPFSPSDST, a single job swapping operator is proposed to disrupt the current solution within the critical factory in the first local search stage
Summary
The DPFSP-SDST is a multifactory production model; it has significant applications in real-world problems. Fernandez-Viagas et al.[15] proposed some constructive heuristics and an iterative improvement algorithm to minimize the total flow time. Wang et al.[20] employed a multiobjective whale swarm algorithm (MOWSA), in which a problem-specific coding scheme, crossover, and variational operations, as well as efficient local search, are proposed to solve multiobjective DPFSP-SDST. Mirabi[23] proposed an improved ant colony optimization algorithm to solve the permutation flow shop scheduling problem with SDST/PFSP. Considering the distributed heterogeneous hybrid flow shop scheduling problem with unrelated parallel machines and the SDST, Li et al.[29] studied a machine position-based mathematical model and designed an improved artificial bee colony algorithm. IG has shown good performance in solving DPFSP, for most existing IG algorithms, the local search based on insertion operator is often adopted. Yi,l: When job J j is processed in factory Fl, the value of the decision variable is 1; otherwise, it is 0
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