Research on balancing and sequencing problems of flexible mixed model assembly lines with alternative precedence relations

Abstract

To tackle the balancing and sequencing problems of flexible mixed model assembly lines with alternative precedence relations, If–then rules and AND/OR graphs are adopted as modelling tools to replace precedence graphs that have limitations in representing alternative precedence relations. Mixed integer linear programming (MILP) and constraint programming (CP) models are established respectively. Moreover, an iterative decomposition method is developed to deal with large-sized problems. Computational experiments on different scales are carried out to test those methods. The computational results reveal that the performance of CP is better than MILP especially when more OR relations exist. And the iterative decomposition method outperforms MILP and CP in terms of solution quality and time.

KEYWORDS:

• Mixed-model assembly line
• balancing and sequencing
• alternative precedence relations
• constraint programming
• iterative decomposition method

Data available statement

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

Disclosure statement

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

Additional information

Funding

This research is supported by Shanghai Pujiang Program (22PJC051).

Notes on contributors

Yunfang Peng

Yunfang Peng received her Ph.D. degree in Industrial Engineering from Huazhong University of Science and Technology, Wuhan, China, in 2010. She is currently an Associate Professor in the School of Management at Shanghai University. Her current research interests include system modelling and optimisation, production scheduling, and material distribution system optimisation.

Lijun Zhang

Lijun Zhang graduated from Management science and Engineering in Shanghai University, Shanghai, China, in 2022. Her major research field is assembly line balancing.

Beixin Xia

Beixin Xia received the Ph.D. degree in Industrial Engineering from Shanghai Jiaotong University, Shanghai, China, in 2013. He joined Shanghai University, Shanghai, China in 2013, where he is currently an Associate Professor in the School of Management. His current research interests include system modelling and simulation, production scheduling, fault diagnosis and machine learning.

Yajuan Han

Yajuan Han received the Ph.D. degree in Management Science and Engineering from Tianjin University, Tianjin, China, in 2008. After graduation, she joined Shanghai University, Shanghai, China. Her current research interests include quality management, system evaluation and optimisation, fault diagnosis and traceability.