文献类型：期刊文献

英文题名：Memetic social spider optimization algorithm for scheduling two-stage assembly flowshop in a distributed environment

作者：Zhang, Guanghui Xing, Keyi

第一作者：Zhang, Guanghui;张广辉

通信作者：Xing, KY[1];Xing, KY[2]

机构：[1]Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 710049, Shaanxi, Peoples R China;[2]Xi An Jiao Tong Univ, Syst Engn Inst, 28 Xianning West Rd, Xian 710049, Shaanxi, Peoples R China;[3]Guizhou Inst Technol, Sch Sci, Guiyang 550003, Guizhou, Peoples R China

第一机构：Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 710049, Shaanxi, Peoples R China

通信机构：corresponding author), Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 710049, Shaanxi, Peoples R China;corresponding author), Xi An Jiao Tong Univ, Syst Engn Inst, 28 Xianning West Rd, Xian 710049, Shaanxi, Peoples R China.

年份：2018

卷号：125

起止页码：423-433

外文期刊名：COMPUTERS & INDUSTRIAL ENGINEERING

收录：;EI(收录号：20183805820941);Scopus(收录号：2-s2.0-85053144449);WOS:【SCI-EXPANDED(收录号:WOS:000449569500034)】；

基金：This research is supported by the National Natural Science Foundation of China under Grant 61573278. The authors would like to thank the editor, associate editor, and all anonymous reviewers for their comments and suggestions that greatly helped improve the presentation and technical quality of this paper.

语种：英文

外文关键词：Assembly flowshop; Distributed scheduling; Memetic algorithm; Separate setup time; Social spider optimization

摘要：This paper studies the distributed two-stage assembly flowshop problem with separate setup times, which is a generalisation for the regular two-stage assembly flowshop problem in the distributed manufacturing environment. The optimization objective is to find a suitable job schedule such that the criterion of total completion time is minimized. To deal with such a problem, we propose a novel memetic algorithm (MA) based on a recently developed social spider optimization (SSO). To the best of our knowledge, it is the first effort to explore the SSO-based MA (MSSO) and to apply SSO in the field of combinational optimization. In the proposed MSSO algorithm, we first modify the original version of SSO to adapt to the distributed problems, and then integrate two improvement techniques, problem-special local search and self-adaptive restart strategy, within MA framework. In the numerical experiment, the parameters used in MSSO are calibrated and suitable parameter values are suggested based on the Taguchi method. Experimental results and comparisons with the existing algorithms validate the effectiveness and efficiency of the proposed MSSO for addressing the considered problem. In addition, the effect of problem scale parameters on MSSO and the effectiveness of the proposed improvement techniques are also investigated and demonstrated.