An adaptive artificial bee colony for hybrid flow shop scheduling with batch processing machines in casting process

Abstract

Hybrid flow shop scheduling problem (HFSP) with real-life constraints has been extensively considered; however, HFSP with batch processing machines (BPM) at a middle stage is seldom investigated. In this study, HFSP with BPM at a middle stage in hot & cold casting process is considered and an adaptive artificial bee colony (AABC) is proposed to minimise makespan. To produce high quality solutions, an adaptive search process with employed bee phase and adaptive search step is implemented. Adaptive search step, which may be onlooker bee phase or cooperation or empty, is decided by evolution quality and an adaptive threshold. Cooperation is performed between the improved solutions of one employed bee swarm and the unimproved solutions of another swarm. Six search operators are constructed and search operator is adaptively adjusted. A new scout phase is also given. A lower bound is provided and proved. Extensive experiments are conducted. The computational results validate that new strategies such as cooperation are effective and efficient and AABC can obtain better results than methods from existing literature on the considered problem.

Keywords:

• Hot & cold casting
• artificial bee colony
• hybrid flow shop scheduling problem
• batch processing machines

Disclosure statement

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

Data availability statement

Data supporting this study are described in the first paragraph of Section 5.1.

Additional information

Funding

This work is supported by the National Natural Science Foundation of China [61573264].

Notes on contributors

Jing Wang

Jing Wang received the bachelor's degree in industrial engineering from the Hubei University of Technology, Wuhan, China, in 2017 and the master's degree in industrial engineering from Fuzhou University, Fuzhou, China, in 2020. She is currently pursuing the doctoral degree with the School of Automation, Wuhan University of Technology, Wuhan, China. Her current research interest includes manufacturing systems intelligent optimisation and scheduling.

Deming Lei

Deming Lei received the master's degree in applied mathematics from Xi'an Jiaotong University, Xi'an, China, in 1996 and the doctoral degree in automation science and engineering from Shanghai Jiaotong University, Shanghai, China, in 2005. He is currently a professor with the School of Automation, Wuhan University of Technology, Wuhan, China. He has published over 100 journal papers. His current research interests include intelligent system optimisation and control, and production scheduling.

Hongtao Tang

Hongtao Tang received the bachelor's degree in material molding and control engineering from the Wuhan University of Technology, Wuhan, China, in 2008, and the doctoral degree in digital material forming from Huazhong University of Science and Technology, Wuhan, China, in 2014. He is currently an associate professor with the School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan, China. His current research interest includes digital design, digital manufacturing and intelligent optimisation algorithm and application.