Memetic social spider optimization algorithm for scheduling two-stage assembly flowshop in a distributed environment. (November 2018)

• Record Type: Journal Article
• Authors: Zhang, Guanghui; Xing, Keyi
• Is Part Of: Computers & industrial engineering. Volume 125(2018)
• Page Start: 423
• Page End: 433
• Publication Date: 2018-11
• Subjects: Assembly flowshop -- Distributed scheduling -- Memetic algorithm -- Separate setup time -- Social spider optimization; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.09.007
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: A memetic algorithm (MA) based on social spider optimization (SSO) is proposed. We improve the original SSO to adapt to the scheduling problem. We first explore SSO-based MA and apply SSO to the combinational optimization. We integrate a problem-special local search and self-adaptive restart strategy with SSO in MA framework. Comparisons with the existing algorithms show the algorithmic competitiveness. Abstract: 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.