An efficient multiobjective genetic algorithm for mixed-model assembly line balancing problem considering demand ratio-based cycle time
Mixed-model assembly lines (mALs) are becoming more and more important by producing different models of the same product on an assembly line. How to calculate the cycle time based on demand of different models also making problem more difficult. According to different work experiences and skill level, the processing time of a given task and the operating costs such as wages differ among workers. Appointing the proper worker to the proper station and assigning the suitable task to the suitable station in order to decrease the cycle time, increase the line efficiency, and reduce the total cost make the problem more complex. This paper proposes a new concept for calculating the cycle time based on demand ratio of each model and another one for calculating the human resource cost. A generalized Pareto-based scale-independent fitness function genetic algorithm (gp-siffGA) is described for solving mixed-model assembly lines balancing (mALB) problems to minimize the cycle time, the variation of workload and the total cost under the constraint of precedence relationships at the same time. The gp-siffGA uses Pareto dominance relationship to solve the problems without using relative preferences of multiple objectives. Comparisons with existing multiobjective genetic algorithms demonstrate that our approach efficiently solves mALB problems.
KeywordsMixed-model assembly line balancing Worker allocation Multiobjective genetic algorithm Demand ratio-based cycle time Pareto dominance relationship
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- Bean J. (1994) Genetic algorithms and random keys for sequencing and optimization. ORSA Journal On Computing 6(2): 154–160Google Scholar
- Dar-EI E. (1973) MALB-a heuristic technique for balancing large single-model assembly lines. AIIE Transactions 34: 343–356Google Scholar
- Deb K. (2001) Multi-objective optimization using evolutionary algorithms. Wiley Interscience Series in Systems and Optimization. Wiley, New YorkGoogle Scholar
- Gen M., Cheng R. (1997) Genetic algorithms and engineering design. Wiley, New YorkGoogle Scholar
- Gen M., Cheng R. (2000) Genetic algorithms and engineering optimization. Wiley, New YorkGoogle Scholar
- Gen M., Cheng R., Lin L. (2008) Network models and optimization: Multiobjective genetic algorithm approach. Springer, LondonGoogle Scholar
- Moed, M. C., Stewart, C. V., & Kelly, R. B. (1991). Reducing the search time of a steady state genetic algorithm using the immigration operator. Proceedings of IEEE international conference tools for artificial intelligence (pp. 500–501).Google Scholar
- Scholl, A. (1993). Data of assembly line balancing problems. Schriften zur Quantitativen Betriebswirtschaftslehre 16/93, Th Darmstadt.Google Scholar
- Weng J. H., Banno M., Onari H. (2006) A study on line operation planning for sewing works. Journal of Japan Industrial Management Association 56(6): 471–477Google Scholar
- Weng J., Banno M., Okubo H., Onari H. (2007) An integrated algorithm for operation assignment and worker allocation in assembly lines. Journal of Japan Industrial Management Association 58(5): 383–394Google Scholar
- Zhang W., Lin L., Gen M. (2008) A multiobjective genetic algorithm based approach to assembly line balancing problem with worker allocation. Journal of Society of Plant Engineers Japan (SOPEJ) 19(4): 61–72Google Scholar