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首页> 外文会议>2012 6th International Conference on New Trends in Information Science, Service Science and Data Mining >Hybrid Multi-Objective PSO with Solution Diversity Extraction for job-shop scheduling management
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Hybrid Multi-Objective PSO with Solution Diversity Extraction for job-shop scheduling management

机译：带有解决方案多样性提取的混合多目标PSO，用于作业车间调度管理

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摘要

The Multi-Objective Flexible Job-Shop Scheduling Problem (FJSP), which concerned with allocating limited resources to optimize some performance criteria, is difficult to find optimal scheduling solutions because of NP-hard complexity. In this paper, the particle encoding representation named Particle Segment Operation-Machine Assignment (PSOMA) is proposed to always produce feasible candidate solutions for the FJSP. Then a solution searching strategy called Solution Diversity Extraction is adopted to improve the Particle Swarm Optimization (PSO) to deal with the diversity in Pareto-optimal solutions. To test the performance of the proposed method, the experiments contain six representative benchmarks and to compare the proposed method with the published algorithms. The simulation results indicate the proposed method can find more wide range potential solutions, and outperform related methods.
机译：多目标柔性作业车间调度问题（FJSP）涉及分配有限的资源以优化某些性能标准，但由于NP难的复杂性而难以找到最佳调度解决方案。在本文中，提出了一种名为粒子段操作机器分配（PSOMA）的粒子编码表示形式，以始终为FJSP生成可行的候选解。然后采用一种称为“解决方案多样性提取”的解决方案搜索策略来改进粒子群优化（PSO），以处理帕累托最优解中的多样性。为了测试该方法的性能，实验包含六个代表性基准，并将该方法与已发布的算法进行比较。仿真结果表明，该方法可以找到更多的潜在解决方案，并且优于相关方法。

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