Hybrid GA-based metaheuristics for production planning and scheduling optimization in intelligent flow-shop manufacturing systems

P. Semanco, V. Modrák
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引用次数: 1

Abstract

The paper introduces a proposal of three-metaheuristic versions to optimize flow-shop problem emphasized on total flow time criterion in Intelligent Manufacturing Systems. The approach employs constructive heuristic, namely CDS, Gupta's algorithm, and Palmer's Slope Index, in conjunction with GA-based metaheuristic. The approach is tested on Reeves' benchmark set of 21 flow-shop problems range from 20 to 75 jobs and 5 to 20 machines.
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基于混合遗传算法的智能流水车间生产计划与调度优化
提出了一种以智能制造系统中总流时间准则为重点的三元启发式优化方法。该方法采用建设性启发式,即CDS,古普塔算法和帕尔默斜率指数,结合基于遗传算法的元启发式。该方法在Reeves的21个流程车间问题基准集上进行了测试,这些问题包括20到75个工作岗位和5到20台机器。
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