An effective memetic algorithm for the distributed flowshop scheduling problem with an assemble machine

Abstract

The distributed flowshop has been a hot topic in research in recent years. This paper considers a distributed permutation flowshop scheduling problem with an assemble machine, so-called the distributed assembly permutation flowshop scheduling problem (DAPFSP), with total tardiness criterion. We propose an effective memetic algorithm (EMA). Firstly, a constructive heuristic combining the well-known earliest due date rule and largest processing time rule is presented for producing a seed sequence. On this basis, an effective initialisation method is used to generate an initial population with a high level of quality and diversity. The EMA uses a new structure of a small iteration nested within a large iteration. Moreover, an improved crossover and mutation expand the solution space in a good direction. According to different operation situations and objects, we design four targeted and flexible local search methods. We evaluate the strategies of the EMA, compare and analyse it with seven efficient algorithms based on the 810 benchmark instances. The experimental results confirm the effectiveness and efficiency of the proposed EMA.

KEYWORDS:

• Distributed assembly permutation flowshop scheduling problem
• total tardiness
• memetic algorithm
• crossover
• local search

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This research is partially supported by the National Science Foundation of China [grant numbers 61973203, 51575212]; and the Program of Shanghai Academic/Technology Research Leader 21XD140100; and Shanghai Key Laboratory of Power station Automation Technology. We are grateful for the support of the Cloud computer platform of Shanghai University Computer Center and Prof. Zi-Peng Shan.

Notes on contributors

Ying-Ying Huang

Ying-Ying Huang received the B.S. degree from Ningbo University, China, in 2017. She is currently working toward the MA degree at Shanghai University, China. Her research focuses on algorithm design of distributed flowshop scheduling.

Quan-Ke Pan

Quan-Ke Pan received the B.Sc. degree and the Ph.D. degree from Nanjing university of Aeronautics and Astronautics, Nanjing, China, in 1993 and 2003, respectively. From 2003 to 2011, he was with School of Computer Science Department, Liaocheng University, where he became a Full Professor in 2006. From 2011 to 2014, he was with State Key Laboratory of Synthetical Automation for Process Industries (Northeastern University), Shenyang, China. From 2014 to 2015, he was with State Key Laboratory of Digital Manufacturing and Equipment Technology (Huazhong University of Science & Technology). He has been with School of Mechatronic Engineering and Automation, Shanghai University since 2015. His current research interests include intelligent optimization and scheduling algorithms.

Liang Gao

Liang Gao received the B.Sc. degree in mechatronic engineering from Xidian University, Xi’an, China, in 1996, and the Ph.D. degree in mechatronic engineering from the Huazhong University of Science and Technology (HUST), Wuhan, China, in 2002. He is a Professor of the Department of Industrial and Manufacturing System Engineering, School of Mechanical Science and Engineering, HUST and Vice Director of State Key Laboratory of Digital Manufacturing Equipment. His current research interests include optimization in design and manufacturing.