A novel bi-level multi-objective genetic algorithm for integrated assembly line balancing and part feeding problem

Abstract

The manufacturing industry has been pursuing an efficient and economical assembly system. By considering assembly line balancing (ALB) and part feeding (PF) as an integrated problem and programming them simultaneously opens additional opportunities to improve the performance of the entire assembly system. However, the integrated ALB and PF problem is a non-deterministic polynomial (NP) hard problem. This implies that exact solutions cannot be obtained in a reasonable computation time and its near-optimal solutions can only be realised by meta-heuristics. In this study, we propose a novel bi-level multi-objective genetic algorithm (NBMGA) to solve the integrated ALB and PF problem. First, a bi-level mathematical model is established to simultaneously minimise the number of stations and workload smoothness of ALB in the upper level as well as the number of supermarkets of PF in the lower level. Second, the NBMGA with two modified strategies, including extending fitness evaluation and adaptive termination condition, is designed for problem solving. Finally, a series of computational experiments are conducted to demonstrate the efficacy of the proposed algorithm. The computational results indicate that the proposed algorithm outperforms the bi-level nondominated sorting genetic algorithm (NSGA) II in terms of the approximation to the true frontier without sacrificing computational efficiency.

KEYWORDS:

• Integrated problem
• assembly line balancing
• part feeding
• bi-level multi-objective genetic algorithm
• Workload balancing
• Cost optimization

Disclosure statement

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

Data availability statement

The data that support the findings of this study are available from the corresponding author, JX, upon reasonable request.

Additional information

Funding

This work was supported by the National Nature Science Foundation of China [grant number 52075452].

Notes on contributors

Junhao Chen

Junhao Chen received the M.S. degree in Mechanical Engineering from the Northwestern Polytechnical University, Xi’an, China, in 2015. He is currently a PhD candidate in Mechanical Engineering at Northwestern Polytechnical University, Xi’an, China. His research interests include smart manufacturing, assembly planning, augmented reality, and human–computer interaction.

Xiaoliang Jia

Xiaoliang Jia received the B.Eng., M.S., and PhD degrees in Mechanical Engineering all from Northwestern Polytechnical University, Xi’an, China, in 1996, 1999 and 2004, respectively. He is currently a Professor with the Department of Mechanical Engineering and Automation, School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an, China. His research interests include smart manufacturing, aircraft MRO, digital twin, digital factory, industrial big data, and industrial software.

Qixuan He

Qixuan He received the M.S. degree in Mechanical Engineering from the Northwestern Polytechnical University, Xi’an, China, in 2021. He is currently an engineer of Bytedance. His research interests include algorithms for the design and optimisation of assembly lines. He has been involved in several projects in the area of production scheduling and flexible manufacturing system.