A novel approach to production planning of flexible manufacturing systems using an efficient multi-objective genetic algorithm |
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| Authors: | Jian-Hung Chen Shinn-Ying Ho |
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| Affiliation: | aDepartment of Information Engineering and Computer Science, Feng Chia University, 100 Wen-hwa Road, Taichung 407, Taiwan, ROC;bDepartment of Biological Science and Technology, Institute of Bioinformatics, National Chiao Tung University, Hsinchu 300, Taiwan, ROC |
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| Abstract: | In this paper, a novel approach using an efficient multi-objective genetic algorithm EMOGA is proposed to solve the problems of production planning of flexible manufacturing systems (FMSs) having four objectives: minimizing total flow time, machine workload unbalance, greatest machine workload and total tool cost. EMOGA makes use of Pareto dominance relationship to solve the problems without using relative preferences of multiple objectives. High efficiency of EMOGA arises from that multiple objectives can be optimized simultaneously without using heuristics and a set of good non-dominated solutions can be obtained providing additional degrees of freedom for the exploitation of resources of FMSs. Experimental results demonstrate effectiveness of the proposed approach using EMOGA for production planning of FMSs. |
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| Keywords: | Flexible manufacturing system Multi-objective optimization Genetic algorithm Production planning |
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