https://orcid.org/0000-0003-0388-8597
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ABSTRACT
We analyse the joint scheduling of spare parts production and service engineers in a multi-site maintenance system. In the system, for each failure, a service engineer with the required spare part needs to be allocated for on-site maintenance. If one of the required resources is not readily available, the maintenance task can be satisfied via an external channel with quick responsiveness but high involved costs. Service providers need to make an efficient plan, including production scheduling of spare parts, allocation of service engineers and outsourcing strategy. First, a mathematical model with the objectives of minimum total outsourcing costs and minimum weighted tardiness is formulated. Then, a novel and knowledge-based heuristic algorithm is developed, namely progressive Pareto algorithm based on step-size (PPAS). To verify the performance of PPAS, it is compared against other well-known algorithms, including NSGA-II, MOEA/D, NNIA, RIPG, MMOIG and MNNIA. Finally, the effectiveness of the joint scheduling mode is demonstrated by comparing it with the separate optimisation mode of spare parts production and workforce allocation.
Highlights
Analyse the joint scheduling of spare parts production and service engineers
Construct a bi-objective optimisation model
Develop a progressive Pareto algorithm based on step-size
Verify the superiority of the proposed algorithm and joint scheduling mode
Acknowledgements
This work was supported by the National Key R&D Program of China [grant numbers 2018YFB1701400, 2020YFB1712100]; the Foshan Technological Innovation Project, China [grant number 1920001000041] and the National Natural Science Foundation of China [grant number 61973108].
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The instances that support the findings of this study are openly available via the link: https://github.com/mbx1998/JSPE.
Additional information
Funding
Notes on contributors
Bingxin Miao
Bingxin Miao is currently an M.S. student in mechanical engineering at Hunan University, Changsha, China. She received her B.S. degree in industrial engineering from Xi’an University of Technology, Xian, China in 2020. Her research interest includes intelligent optimisation algorithm and resource scheduling problems.
Qianwang Deng
Qianwang Deng was born in 1972. He obtained his doctoral degree from Otto-von-Guericke University Magdeburg, Germany. He is now a professor at the School of Mechanical & Vehicle Engineering at Hunan University, China. His research fields include industrial information, intelligent production & service system engineering.
Like Zhang
Like Zhang received his Ph.D. degree in mechanical engineering from Hunan University, Changsha, P.R. China, in 2022. He is currently a lecturer of the College of Mechanical and Electrical Engineering of Zhengzhou University of Light Industry. His research interest includes intelligent optimisation algorithm and production scheduling problems.
Zhangwen Huo
Zhangwen Huo is currently an M.S. student in mechanical engineering at Hunan University, Changsha, Hunan. He received his B.S. degree in mechanical engineering from China University of Mining and Technology, Xuzhou, Jiangsu in 2020. His research interest includes intelligent optimisation algorithm and scheduling problems.
Weifeng Han
Weifeng Han, a senior engineer, holds a bachelor's degree and graduated from Henan University of Science and Technology with a major in mechanical design, manufacturing and automation. He is mainly engaged in research on shield tunnelling equipment and its application technology.