Affiliations: [a] School of Automation, Wuhan University of Technology, Wuhan, China
| [b] Department of Electronic and Information Engineering, Shantou University, Shantou, China
| [c] School of Economics and Management, Anhui Polytechnic University, Wuhu, China
| [d] School of Mechanical and Electronic Engineering, Wuhan University of Technology, Wuhan, China
Correspondence:
[*]
Corresponding author. Pan Wang, School of Automation, Wuhan University of Technology, Wuhan 430070, China. E-mail: [email protected].
Abstract: Assembly flow shop scheduling problem (AFSP) in a single factory has attracted widespread attention over the past decades; however, the distributed AFSP with DPm → 1 layout considering uncertainty is seldom investigated. In this study, a distributed assembly flow shop scheduling problem with fuzzy makespan minimization (FDAFSP) is considered, and an efficient artificial bee colony algorithm (EABC) is proposed. In EABC, an adaptive population division method based on evolutionary quality of subpopulation is presented; a competitive employed bee phase and a novel onlooker bee phase are constructed, in which diversified combinations of global search and multiple neighborhood search are executed; the historical optimization data set and a new scout bee phase are adopted. The proposed EABC is verified on 50 instances from the literature and compared with some state-of-the-art algorithms. Computational results demonstrate that EABC performs better than the comparative algorithms on over 74% instances.
