An Optimized Supply Chain Network Model Based on Modified Genetic Algorithm
An Optimized Supply Chain Network Model Based on Modified Genetic Algorithm
复制标题
基于改进遗传算法的优化供应链网络模型
DOI：
10.1049/cje.2017.03.018
复制
发表时间：
2017
影响因子：
1.2
通讯作者：
Zhang YK
中科院分区：
文献类型：
--
作者：
Zhang Yikun;Liu Shufen;Zhang Xinjia;Zhang YK
针对复杂的多源、多产品、多阶段的供应链网络（SCN）设计问题，我们提出了一种优化供应链网络模型。我们将现金转换周期视为该模型的目标，并利用修改后的遗传算法来解决该问题。为了描述供应链网络的结构，我们提出了一种新的编码方法和带有修改遗传算子的遗传算法。我们使用帕累托方法来获得帕累托最优解集。为了评估改进遗传算法的性能并验证模型，我们与标准遗传算法和模拟退火遗传算法进行了比较。实验结果表明，与替代算法相比，改进的遗传算法获得了更好的CPU时间和帕累托最优解的准确性，并且模型是有效的。
For complex multi-source, multi-product, multi-stage Supply chain network (SCN) design problem, we propose an optimization supply chain network model. We consider cash conversion cycle as an objective to this model and utilize a modified genetic algorithm to solve the problem. To describe the structure of supply chain network, we propose a new encoding method and a genetic algorithm with modified genetic operators. We use the Pareto approach to obtain the set of Pareto-optimal solutions. In order to evaluate the performance of the modified genetic algorithm and validate the model, we conduct comparisons with standard genetic algorithm and the simulated annealing genetic algorithm. Experimental results show that the modified genetic algorithm achieved better CPU time and the accuracy of the Pareto-optimal solutions than the alternative algorithms and the model was effective.