A multi-objective genetic algorithm for mixed-model assembly line rebalancing

Authors:
Caijun Yang;Jie Gao;Linyan Sun
Affiliations:
School of Management, Xi'an Jiaotong University, Xi'an 710049, China and The State Key Laboratory for Manufacturing Systems Engineering, Xi'an 710049, China and The Key Lab of the Ministry of Educ ...;School of Management, Xi'an Jiaotong University, Xi'an 710049, China and The State Key Laboratory for Manufacturing Systems Engineering, Xi'an 710049, China and The Key Lab of the Ministry of Educ ...;School of Management, Xi'an Jiaotong University, Xi'an 710049, China and The State Key Laboratory for Manufacturing Systems Engineering, Xi'an 710049, China and The Key Lab of the Ministry of Educ ...
Venue:
Computers and Industrial Engineering
Year:
2013

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Abstract

When demand structure or production technology changes, a mixed-model assembly line (MAL) may have to be reconfigured to improve its efficiency in the new production environment. In this paper, we address the rebalancing problem for a MAL with seasonal demands. The rebalancing problem concerns how to reassign assembly tasks and operators to candidate stations under the constraint of a given cycle time. The objectives are to minimize the number of stations, workload variation at each station for different models, and rebalancing cost. A multi-objective genetic algorithm (moGA) is proposed to solve this problem. The genetic algorithm (GA) uses a partial representation technique, where only a part of the decision information about a candidate solution is expressed in the chromosome and the rest is computed optimally. A non-dominated ranking method is used to evaluate the fitness of each chromosome. A local search procedure is developed to enhance the search ability of moGA. The performance of moGA is tested on 23 reprehensive problems and the obtained results are compared with those by other authors.