摘要： 在供应链环境下构建一个多目标Flow Shop调度优化模型,采用灰熵并行分析(GEPA)法优化该多目标模型。在表征序列间相似程度的灰关联分析法基础上引入信息熵理论建立GEPA法,推导出的灰熵并行关联度衡量多目标Pareto解与理想解的相似程度,并将其作为适应度值引导算法进化,避免多目标优化问题中直接对目标权重赋值。在此基础上建立基于灰熵并行分析的遗传算法。实验结果表明,该算法可有效解决供应链环境下高维多目标Flow Shop调度问题,在多目标最优解、性能评价指标等方面均优于基于随机权重的遗传算法。

关键词: 供应链, 多目标Flow Shop, 灰熵并行分析法, 灰熵并行关联度, 多目标优化, 遗传算法

Abstract: This paper establishes a multi-objective Flow Shop schedule optimization model under the environment of supply chain and optimized with the Grey Entropy Parallel Analysis(GEPA) method.Based on the grey relational analysis method,which expresses the similar degree between sequences,the information entropy theory is adopted to establish grey entropy parallel analysis method.The Grey Entropy Parallel Relational Degree(GEPRD) deduced by this method is used to measure the similar degree between multi-objective Pareto solutions and ideal solution and is used as the fitness to guide the evolution of the algorithm.By this way,the shortcoming that assignment the target weight directly in multi-objective optimization problem is overcome.The Genetic Algorithm based on Grey Entropy Parallel Analysis(GEPA_GA) is tablished.Experimental results show that GEPA_GA can solve high-dimensional multi-objective Flow Shop schedule problem under the environment of supply chain effectively.The multi-objective optimal solution and performance evaluation index of GEPA_GA are all superior to Genetic Algorithm based on Random Weighting(RW_GA).

Key words: supply chain, multi-objective Flow Shop, Grey Entropy Parallel Analysis(GEPA) method, Grey Entropy Parallel Relational Degree(GEPRD), multi-objective optimization, Genetic Algorithm(GA)

中图分类号: 

• TP18