Modeling and Scheduling a Constrained Flowshop in Distributed Manufacturing Environments - Wang, Bing-Tao; Pan, Quan-Ke; Gao, Liang; Miao, Zhong-Hua; Sang, Hong-Yan (Journal of manufacturing systems, 2024)
This paper addresses a constrained distributed flowshop scheduling problem (CDFSP) that widely exists in modern manufacturing industry but has not been investigated before. Different from the distributed flowshop scheduling problem (DFSP) in the literature, CDFSP considers that all jobs are grouped for processing efficiently, and part of the groups subject to production requirements have a common deadline while the others do not have such a constraint. This constraint not only increases the complexity of the problem but also makes many solutions generated by the existing algorithms infeasible. Therefore, in this paper, a mathematical model is first established for the CDFSP with makespan criterion. After that, we explore the problem-specific knowledge to exclude infeasible solutions, and provide an acceleration method to evaluate feasible solutions in the insertion neighborhood. By automatically selecting parameters to match different search stages, a self-adaptive cooperative iterated greedy algorithm is designed where re-insertion and re-destruction mechanisms are integrated to address the infeasible solutions caused by a paradox and greedy mechanism. A cooperative mechanism is proposed to strengthen the coordination between family scheduling and job scheduling. Finally, the comprehensive experiments verify the effectiveness of the proposed algorithmic components and self-adaptive mechanism, and demonstrate the significant superiority of the proposed algorithm over five state-of-the-art metaheuristics. •This is the first study of DFSP with group technology and part of jobs subject to a hard due date.•Two counterintuitive paradoxes are found to avoid the invalid search of the algorithm.•A Self-adaptive Cooperative Iterated Greedy Algorithm is proposed.•Re-insertion and re-destruction mechanisms are provided to address the infeasible solutions.