A PSO-based multi-objective optimization approach to the integration of process planning and scheduling

Yifa Wang, Yunfeng Zhang, J. Fuh
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引用次数: 21

Abstract

This paper has presented a particle swarm optimization (PSO) based approach to handle a multi-objective integrated process planning and scheduling problem. The aim is to find a set of high-quality trade-off solutions. This is a combinatorial optimization problem with substantially large solution space, suggesting that it is highly difficult to find the best solutions with the exact search method. To account for it, a PSO-based algorithm is proposed by fully utilizing the capability of the exploration search and fast convergence. To fit the continuous PSO in the discrete modeled problem, a novel solution representation is introduced in the algorithm. Moreover, to improve the solution quality, a local search algorithm is used to perform on the stored elite solutions, which would facilitate the exploitation search in the regions with promising solutions. The numerical experiments have been performed to demonstrate the effectiveness of the proposed algorithm.
基于pso的工艺规划与调度集成多目标优化方法
提出了一种基于粒子群算法的多目标集成工艺规划与调度问题的求解方法。其目的是找到一套高质量的权衡解决方案。这是一个具有相当大的解空间的组合优化问题,这表明用精确的搜索方法找到最佳解是非常困难的。为此,充分利用粒子群算法的搜索能力和快速收敛能力,提出了一种基于粒子群算法的算法。为了适应离散模型问题的连续粒子群,在算法中引入了一种新的解表示。此外,为了提高解的质量,采用局部搜索算法对存储的精英解进行搜索,有利于在有希望解的区域进行挖掘搜索。数值实验验证了该算法的有效性。
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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