基于混沌粒子群算法的绿色农产品供应链网络优化

Q. Tao, Zhexue Huang, Chunqin Gu, Chenxin Zhang
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引用次数: 6

摘要

本文提出了一种混沌粒子群算法(混沌粒子群算法)来求解绿色农产品供应链网络(GASCN)。GASCN设计对于降低总运输成本,实现高效和有效的供应链管理至关重要。传统的供应链不能充分满足所有客户的期望,因此开发新的供应链模式迫在眉睫。本文的主要贡献在于找到了GASCN问题的最优解,并提出了一种基于CPSO的GASCN优化方案。为了验证CPSO算法的有效性,在三种情况下对该算法进行了测试。结果表明,在GASCN中,CPSO在优化速度和解质量上都优于遗传算法和CGA,特别是在问题规模较大时。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimization of green agri-food supply chain network using chaotic PSO algorithm
In this paper, a chaotic Particle Swarm Optimization (CPSO) algorithm is presented to solve the green agri-food supply chain network (GASCN). The GASCN design is critical to reduce the total transportation cost for efficient and effective supply chain management. The traditional supply chain does not adequately satisfy the expectance of all the customers, therefore new model of supply chain of great urgency to be exploited. The main contribution of this paper is to find an optimal solution for GASCN problem and propose a new solution based on CPSO to optimize the GASCN. To show the efficacy of the CPSO algorithm, the algorithm is tested on three cases. Results show better performance of the CPSO in GASCN by both optimization speed and solution quality as compared to GA and CGA, especially when the scale of problem is large.
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