An Algorithm for Solving Flexible Job Shop Scheduling Problems Based on Multi-objective Particle Swarm Optimization

2010 Third International Symposium on Information Science and Engineering Pub Date : 2010-12-24 DOI:10.1109/ISISE.2010.128

Naiping Hu, Pei-li Wang

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引用次数: 11

Abstract

A new method based on multi-objective particle swarm optimization is proposed to deal with the flexible job shop scheduling problems with multiple objectives, minimizing completion time, total machine workload and the biggest machine workload. This algorithm adopts linear weighting method to change multi-objective optimization problem into the single objective optimization problem, and introduces random and uniform design method to produce weight coefficient, which ensures the diversity and uniform distribution of pare to set. Besides, elite reserved strategy and dynamic neighborhood operator are designed to maintain the diversity of population and improve search capabilities of particles. Particle is presented in the form of binary group. In order to solve process scheduling priority issues and machinery distribution, encoding process, consisting of extended operation and priority rule, is designed. Finally, the corresponding computational experiments are reported. The results indicate that the proposed algorithm is an efficient approach for the flexible job shop scheduling problems.

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基于多目标粒子群优化的柔性作业车间调度算法

针对多目标柔性作业车间调度问题，提出了一种基于多目标粒子群优化的柔性作业车间调度方法，以最小的完成时间、最小的机器总工作量和最大的机器工作量为目标。该算法采用线性加权法将多目标优化问题转化为单目标优化问题，并引入随机均匀设计方法产生权系数，保证了pareto集的多样性和均匀分布。设计了精英保留策略和动态邻域算子，保持种群的多样性，提高粒子的搜索能力。粒子以二元群的形式表示。为了解决工序调度优先级问题和机械分配问题，设计了由扩展运算和优先级规则组成的编码过程。最后，进行了相应的计算实验。结果表明，该算法是求解柔性作业车间调度问题的有效方法。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

2010 Third International Symposium on Information Science and Engineering

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