Modeling and multi-neighborhood iterated greedy algorithm for distributed hybrid flow shop scheduling problem

As economic globalization, large manufacturing enterprises build production centers in different places to maximize profit. Therefore, scheduling problems among multiple production centers should be considered. This paper studies a distributed hybrid flow shop scheduling problem (DHFSP) with makespan criterion, which combines the characteristic of distributed flow shop scheduling and parallel machine scheduling. In the DHFSP, a set of jobs are assigned into a set of identical factories to process. Each job needs to be through same route with a set of stages, and each stage has several machines in parallel and at least one of stage has more than one machine. For solving the DHFSP, this paper proposes two algorithms: DNEH with smallest-medium rule and multi-neighborhood iterated greedy algorithm. The DNEH with smallest-medium rule constructive heuristic first generates a seed sequence by decomposition and smallest-medium rule, and then uses a greedy iteration to assign jobs to factories. In the iterated greedy algorithm, a multi-search construction is proposed, which applies the greedy insertion to the factory again after inserting a new job. Then, a multi-neighborhood local search is utilized to enhance local search ability. The proposed algorithms are evaluated by a comprehensive comparison, and the experimental results demonstrate that the proposed algorithms are very competitive for solving the DHFSP. (C) 2020 Elsevier B.V. All rights reserved.