Improved social spider algorithm for partial disassembly line balancing problem considering the energy consumption involved in tool switching

Abstract

As the waste products have a variety of connection structure characteristics, the energy consumed in tool switching in the disassembly process is considered to better comprehensively optimise the energy consumption index. A mixed-integer non-linear programming (MINLP) model of multi-objective partial disassembly line balancing problem (PDLBP) is constructed to minimise four optimisation objectives which are the number of workstations, workstation load, number of the tools are switched, and energy consumption. Based on the characteristics of PDLBP, we constructed an energy consumption matrix of tool switching and proposed a multi-objective improved social spider algorithm (ISSA). The random movement and mask change operations of ISSA were improved, and the artificial spiders were added to enhance the global optimisation capabilities of ISSA. ISSA was applied to optimise two typical benchmark instances, which have different scales, respectively. And the computational results were compared with several algorithms of existing literature to verify the superiority of ISSA. Finally, ISSA was applied to a partial disassembly instance of a printer, which considered the energy consumed in tool switching. Then, multiple better disassembly schemes were provided for decision-makers.

KEYWORDS:

• Partial disassembly
• energy consumed in tool switching
• multi-objective optimisation
• social spider algorithm
• artificial spider

Disclosure statement

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

Data availability statement

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

Additional information

Funding

This work was supported by the National Natural Science Foundation of China: [Grant Number No. 51205328, No. 51675450]; the Youth Foundation for Humanities and Social Sciences of Ministry of Education of China: [Grant Number No. 18YJC630255]; Sichuan Province Science and Technology Support Program: [Grant Number No. 2019YFG0285].

Notes on contributors

Wei Liang

Wei Liang was born in 1998 and is a student pursuing a master’s degree in Mechanical design and theory from Southwest Jiaotong University, Chengdu, China. His research interests focus on production line balancing and intelligent optimisation.

Zeqiang Zhang

Zeqiang Zhang (corresponding author) was born in 1978, and is a professor and doctoral tutor of School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China. His current research interest is manufacturing systems and intelligent optimisation.

Yu Zhang

Yu Zhang was born in 1997 and is a student pursuing a PhD degree in Mechanical design and theory from Southwest Jiaotong University, Chengdu, China. His research interest is focused on production line balancing and intelligent optimisation.

Peiyu Xu

Peiyu Xu was born in 1995 and is a student pursuing a master’s degree in Mechanical design and theory from Southwest Jiaotong University, Chengdu, China. His research interests focus on production line balancing and intelligent optimisation.

Tao Yin

Tao Yin was born in 1990 and is a student pursuing a PhD degree in Mechanical design and theory from Southwest Jiaotong University, Chengdu, China. His research interests include the optimisation algorithm and its application in the field of intelligent manufacturing system.