基于模糊物元模型的高维多目标FJSP研究
发布时间：2018-10-21 16:14
【摘要】：为解决高维多目标柔性作业车间调度问题,提出了一种基于模糊物元模型与粒子群算法的模糊粒子群算法(fuzzy particle swarm optimization,FPSO)。该算法以模糊物元分析理论为依据,采用复合模糊物元与基准模糊物元之间的欧氏贴近度作为适应度值引导粒子群算法的进化,并引入具有容量限制的外部存储器保留较优的Pareto非支配解以供决策者选择。此外,构建了优化目标为最大完工时间、设备总负荷、加工成本、最大设备负荷与加工质量的高维多目标优化模型,并以Kacem基准问题与实际生产数据为例进行仿真模拟与对比分析。结果表明,该算法具有良好的收敛性,且搜索到的非支配解分布性较好,能够有效地应用于求解高维多目标柔性作业车间调度问题。
[Abstract]:A fuzzy particle swarm optimization (fuzzy particle swarm optimization,FPSO) algorithm based on fuzzy matter-element model and particle swarm optimization (PSO) is proposed to solve the high dimensional flexible job shop scheduling problem. Based on the theory of fuzzy matter-element analysis, the Euclidean closeness between compound fuzzy matter-element and reference fuzzy matter-element is used as the fitness value to guide the evolution of PSO. Furthermore, the external memory with limited capacity is introduced to retain the optimal Pareto non-dominated solution for decision makers to choose. In addition, a high-dimensional multi-objective optimization model with maximum completion time, total equipment load, processing cost, maximum equipment load and machining quality is constructed. The Kacem benchmark problem and actual production data are taken as an example for simulation and comparative analysis. The results show that the proposed algorithm has good convergence and good distribution of non-dominated solutions. It can be effectively applied to solve high-dimensional multi-objective flexible job shop scheduling problems.
【作者单位】： 西北工业大学管理学院;
【基金】：国家自然科学基金资助项目(U1404702)
【分类号】：TB497;TP18
本文编号：2285654
[Abstract]:A fuzzy particle swarm optimization (fuzzy particle swarm optimization,FPSO) algorithm based on fuzzy matter-element model and particle swarm optimization (PSO) is proposed to solve the high dimensional flexible job shop scheduling problem. Based on the theory of fuzzy matter-element analysis, the Euclidean closeness between compound fuzzy matter-element and reference fuzzy matter-element is used as the fitness value to guide the evolution of PSO. Furthermore, the external memory with limited capacity is introduced to retain the optimal Pareto non-dominated solution for decision makers to choose. In addition, a high-dimensional multi-objective optimization model with maximum completion time, total equipment load, processing cost, maximum equipment load and machining quality is constructed. The Kacem benchmark problem and actual production data are taken as an example for simulation and comparative analysis. The results show that the proposed algorithm has good convergence and good distribution of non-dominated solutions. It can be effectively applied to solve high-dimensional multi-objective flexible job shop scheduling problems.
【作者单位】： 西北工业大学管理学院;
【基金】：国家自然科学基金资助项目(U1404702)
【分类号】：TB497;TP18
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