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

机译:基于混合的GA基础术,用于智能流店制造系统的生产规划和调度优化

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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.
机译:本文介绍了三种成交学版本,以优化在智能制造系统中的总流量时间标准上强调的流量店问题。 该方法与基于GA的成群质造影相结合,采用建设性启发式,即CDS,Gupta算法和Palmer的斜率指数。 该方法在REEVES的基准组合的21个流店问题上进行了测试,其范围为20至75个作业,5到20台机器。

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