Scheduling Optimization of a Vehicle Power Battery Workshop Based on an Improved Multi-Objective Particle Swarm Optimization Method

Jinjun Tang, Tongyu Dou, Fan Wu, Lipeng Hu and Tianjian Yu ()
Additional contact information
Jinjun Tang: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Tongyu Dou: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Fan Wu: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Lipeng Hu: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China
Tianjian Yu: School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China

Mathematics, 2025, vol. 13, issue 17, 1-33

Abstract: Power batteries are one of the important components of electric vehicles, but the manufacturing process of vehicle power batteries is complex and diverse. Traditional scheduling methods face challenges such as low production efficiency and inadequate quality control in complex production environments. To address these issues, a multi-objective optimization model with makespan, total machine load, and processing quality as the established objectives, and a Multi-objective Particle Swarm Energy Valley Optimization (MPSEVO) is proposed to solve the problem. MPSEVO integrates the advantages of Multi-objective Particle Swarm Optimization (MOPSO) and Energy Valley Optimization (EVO). In this algorithm, the particle stability level is combined in MOPSO, and different update strategies are used for particles of different stability to enhance both the convergence and diversity of the solutions. Furthermore, a local search strategy is designed to further enhance the algorithm to avoid the local optimal solutions. The Hypervolume ( H V ) and Inverted Generational Distance ( I G D ) indicators are often used to evaluate the convergence and diversity of multi-objective algorithms. The experimental results show that MPSEVO’s H V and I G D indicators are better than other algorithms in 10 computational experiments, which verifies the effectiveness of the proposed strategy and algorithm. The proposed method is also applied to solve the actual battery workshop scheduling problem. Compared with MOPSO, MPSEVO reduces the total machine load by 7 units and the defect rate by 0.05%. In addition, the effectiveness of each part of the improved algorithm was analyzed by ablation experiments. This paper provides some ideas for improving the solution performance of MOPSO, and also provides a theoretical reference for enhancing the production efficiency of the vehicle power battery manufacturing workshop.

Keywords: workshop scheduling; multi-objective optimization; vehicle power battery; particle swarm optimization; flexible job shop scheduling (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/17/2790/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/17/2790/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:17:p:2790-:d:1738004

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().