期刊
COMPUTERS & INDUSTRIAL ENGINEERING
卷 152, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.cie.2020.107021
关键词
Distributed assembly permutation flowshop scheduling; Total flowtime; Iterated greedy algorithm; Heuristic; Local search
资金
- National Science Foundation of China [61973203, 51575212]
- Shanghai Key Laboratory of Power station Automation Technology
This paper proposes an improved iterative greedy algorithm based on groupthink for solving the distributed assembly permutation flowshop scheduling problem with total flowtime criterion, and experimental results show that the proposed algorithm significantly outperforms other algorithms in comparison.
This paper considers a distributed assembly permutation flowshop scheduling problem (DAPFSP) with total flowtime (TF) criterion, which is of great significance to both industry and research community. We propose an improved iterative greedy algorithm based on the groupthink (gIGA) for solving the problem. Firstly, based on the solution representation, we present an effective initialization procedure by combining the well-known NEH heuristic and Palmer method. Secondly, in order to improve the efficiency of the algorithm, both the destruction reconstruction process and the local search process are elaborately designed for the products and jobs separately. Next, our algorithm adaptively extracts jobs in the destruction stage with regard to the size of instances. In addition, we employ a novel selection method that is based on the objective values and the ages of the individuals in the population. Through a total of 810 benchmark instances, the proposed algorithm is compared with seven state-of-the-art algorithms in the literature. The experimental results show that the proposed algorithm performs significantly better than the other algorithms in comparison by three analytical methods for solving the DAPFSP with TF criterion.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据