Multi-objective multi-fidelity optimisation for position-constrained human-robot collaborative disassembly planning

AbstractHuman-robot collaborative disassembly lines are widely used by remanufacturing companies to disassemble end-of-life (EOL) products. When disassembling large-sized EOL products, each workstation on a disassembly line is generally divided into multiple operating positions, so that different operators can disassemble the same product at their respective positions at the same time, thereby greatly improving efficiency. This paper focuses on a position-constrained human-robot collaborative disassembly planning (PC-HRCDP) problem for the above-mentioned lines, including three subproblems of disassembly sequence planning, disassembly line balancing and robot path planning. A multi-objective mixed integer programming model for PC-HRCDP is developed to solve small-scale instances. Furthermore, a multi-objective multi-fidelity optimisation (MO-MFO) algorithm is proposed to solve large-scale instances. Comprehensive experiments are conducted based on 10 problem instances generated in this study. Experimental results show that the proposed MO-MFO is better than a high-fidelity optimisation algorithm in terms of running time. In addition, benefiting from the strategy of MO-MFO to allocate the limited high-fidelity computational budget to solutions in the two stages of multi-objective optimisation and optimal sampling, MO-MFO is significantly better than the existing representative multi-fidelity optimisation algorithms in terms of the hyper-volume and the inverted generational distance.KEYWORDS: Disassembly linedisassembly planninghuman-robot collaborationmulti-fidelity optimisationmulti-objective optimisation Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data used to support the findings of this study are available from the corresponding author upon request or from Mendeley Data, V2, doi: 10.17632/c5k8d775bp.2.Additional informationFundingThis work was supported by the National Natural Science Foundation of China under Grant 52075402.Notes on contributorsYilin FangYilin Fang received the Ph.D. degree in communication engineering from the Wuhan University of Technology, Wuhan, China, in 2011. In 2017, he was a visiting scholar with the School of Engineering, University of Birmingham, U.K. He is currently a professor at the Wuhan University of Technology, China. His research interests include disassembly planning, multi-objective optimisation and evolutionary algorithms.Zhiyao LiZhiyao Li is currently pursuing the Ph.D. degree with the Wuhan University of Technology. His research interests include disassembly planning and multi-objective optimisation.Siwei WangSiwei Wang received the bachelor’s degree in communication engineering from Wuhan University of Technology, Wuhan, China, in 2020. She is currently pursuing the master’s degree with the Wuhan University of Technology. Her research is principally on multi-objective optimisation.Xinwei LuXinwei Lu received the bachelor’s degree in telecommunications engineering from Wuhan University of Technology, Wuhan, China, in 2021. She is currently pursuing the master’s degree with the Wuhan University of Technology. Her research interests include robotic disassembly and evolutionary computation.