Next Article in Journal
Research on Construction Countermeasures for Freeze–Thaw Deformation of Permafrost Subgrade in Forest Regions of Northeast China
Previous Article in Journal
Treatment of Water Contaminated with Cr(VI) Using Bacterial Cellulose and FeCl3 in a Continuous System
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Modeling and Optimization of a Mixed-Model Two-Sided Assembly Line Balancing Problem Considering a Workstation-Sharing Mechanism

by
Lingling Hu
1,2,* and
Vatcharapol Sukhotu
1,*
1
Faculty of Logistics and Digital Supply Chain, Naresuan University, Phitsanulok 65000, Thailand
2
College of Mechanical and Automotive Engineering, Guangxi University of Science and Technology, Liuzhou 545006, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2025, 15(23), 12809; https://doi.org/10.3390/app152312809
Submission received: 2 November 2025 / Revised: 25 November 2025 / Accepted: 27 November 2025 / Published: 3 December 2025

Abstract

In the context of the rapid development of the new energy vehicle industry, how to achieve the mixed production of fuel vehicles and electric vehicles has become an important issue for the transformation and flexible manufacturing of automotive production lines. This paper addresses the balance problem of the mixed assembly line for electric vehicles and fuel vehicles and proposes a mathematical modeling method based on the product structure differences and workstation sharing. An improved genetic algorithm is designed for optimization. The established optimization model includes mathematical models of process priority relationships, cycle time constraints, synchronization constraints, and exclusive process co-placement constraints, with the optimization goals of minimizing workstation quantity and balancing workstation load. To solve such models, the decoding process of the genetic algorithm is redesigned in the algorithm design. The improved genetic algorithm can be well used to solve the workstation-sharing model. A case study of the chassis assembly line of an automotive manufacturing enterprise is used for verification. The results show that the method considering workstation sharing can effectively reduce the number of workstations, improve the distribution of workstation loads, and increase the utilization rate of the production line, while ensuring the cycle time constraints. The conclusions of this study expand the theoretical framework of the balance problem of mixed assembly lines and provide practical references for the transformation of fuel vehicle production lines into new energy vehicles.
Keywords: mixed-model two-sided assembly line balancing; workstation sharing; multi-objective optimization; improved genetic algorithm; flexible manufacturing; EV–FV hybrid production mixed-model two-sided assembly line balancing; workstation sharing; multi-objective optimization; improved genetic algorithm; flexible manufacturing; EV–FV hybrid production

Share and Cite

MDPI and ACS Style

Hu, L.; Sukhotu, V. Modeling and Optimization of a Mixed-Model Two-Sided Assembly Line Balancing Problem Considering a Workstation-Sharing Mechanism. Appl. Sci. 2025, 15, 12809. https://doi.org/10.3390/app152312809

AMA Style

Hu L, Sukhotu V. Modeling and Optimization of a Mixed-Model Two-Sided Assembly Line Balancing Problem Considering a Workstation-Sharing Mechanism. Applied Sciences. 2025; 15(23):12809. https://doi.org/10.3390/app152312809

Chicago/Turabian Style

Hu, Lingling, and Vatcharapol Sukhotu. 2025. "Modeling and Optimization of a Mixed-Model Two-Sided Assembly Line Balancing Problem Considering a Workstation-Sharing Mechanism" Applied Sciences 15, no. 23: 12809. https://doi.org/10.3390/app152312809

APA Style

Hu, L., & Sukhotu, V. (2025). Modeling and Optimization of a Mixed-Model Two-Sided Assembly Line Balancing Problem Considering a Workstation-Sharing Mechanism. Applied Sciences, 15(23), 12809. https://doi.org/10.3390/app152312809

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop