Mixed-integer programming model and hybrid local search genetic algorithm for human–robot collaborative disassembly line balancing problem

ABSTRACT

Human–robot collaborative technology maximises the advantages of the capabilities of humans and robots, and provides diverse operating scenarios for the remanufacturing industry. Accordingly, this paper proposes an innovative human–robot collaborative disassembly line balancing problem (HRC-DLBP). First, a mixed-integer programming (MIP) model is devised for the HRC-DLBP to minimise the number of workstations, smoothness index, and various costs. Second, a hybrid local search genetic algorithm (HLSGA) is developed to solve the proposed HRC-DLBP efficiently. According to the problem characteristics, a four-layer encoding and decoding strategy was constructed. The search mechanism of the local search operator was improved, and its search strategy was adjusted to suit the genetic algorithm structure better. Furthermore, the accuracy of the proposed MIP model and HLSGA is verified through two HRC-DLBP examples. Subsequently, three HRC-DLBP examples are used to prove that the HLSGA is superior to five other excellent algorithms. The case of the two-sided disassembly line problem reported in the literature is also solved using the HLSGA. The results are found to be significantly better than the reported outputs of the improved whale optimisation algorithm. Besides, HLSGA also outperforms the results reported in the literature in solving EOL state-oriented DLBP. Finally, the HLSGA is applied to a power battery disassembly problem, and several optimal allocation schemes are obtained.

KEYWORDS:

• Human–robot collaborative
• disassembly line balancing
• mixed-integer programming
• hybrid local search genetic algorithm

Data availability statement

The Supplementary data that support the findings of this study are available at doi: 10.17632/zv6zdpns42.1.

Disclosure statement

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

Additional information

Funding

This research was partially funded by the National Natural Science Foundation of China [under Grant numbers 51205328, 51675450], Youth Foundation for Humanities and Social Sciences of Ministry of Education of China [grant number 18YJC630255], Sichuan Province Science and Technology Support Program [grant number 2022YFG0245, 2022YFG0241], and CRRC's 14th Five-Year Science and Technology Major Special Scientific Research Project [grant number 2021CHZ010-3].

Notes on contributors

Tengfei Wu

Tengfei Wu was born in 1992 and is a student pursuing a Ph.D. degree in Mechanical design and theory from Southwest Jiaotong University, Chengdu, China. His research directions include the optimisation algorithm and its application in the field of intelligent manufacturing system.

Zeqiang Zhang

Zeqiang Zhang received the Ph.D. degree in mechanical design and theory from Southwest Jiaotong University, Chengdu, China, in 2006. From 2011 to 2012, he was a Visiting Scholar with the Industrial and System Engineering Department, Auburn University, AL, USA. He is currently a Professor and Ph.D. Supervisor at Southwest Jiaotong University, where he has also served as Director of the Heavy Transport and Lifting Laboratory, Assistant Dean of the School of Mechanical Engineering, Member of the School of Mechanical Engineering Party Committee, Vice Director of the Party and Government Office and Director of the Legal Affairs Office. He is currently a Member and Chairman of the Logistics Engineering Professional Committee of the Chinese Mechanical Engineering Society, Executive Director of the Sichuan Mechanical Engineering Society, Chairman of the Logistics Engineering Professional Committee of the Sichuan Mechanical Engineering Society, and Member of the Sichuan Mechanical Standardization Technical Committee. He has published more than 100 academic papers in journals and conferences. He is also the author of more than 70 patents, including patents granted in the United States and Japan, and has contributed to important books such as Crane Design Handbook, Logistics Engineering Technology Roadmap, and "Numerical Control Generation" Case Collection (Logistics Technology and Equipment Volume). His research interests include mechanical intelligence optimization and dynamic simulation, industrial engineering, manufacturing systems, and intelligence optimization.

Yanqing Zeng

Yanqing Zeng was born in 1994 and is a student pursuing a Ph.D. degree in Mechanical design and theory from Southwest Jiaotong University, Chengdu, China. Her research interests include the optimisation algorithm and its application in the field of intelligent manufacturing system.

Yu Zhang

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