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A GA Model Development for Decision Making Under Reverse Logistics

机译:逆向物流决策的遗传算法模型开发

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Managing products' end-of-life and recovery of used products is gaining significant importance during last years. Therefore, managing the reverse flow of products can be an important potential for winning consumers in future competitive markets. In this context, establishing reverse logistics networks is becoming a main problem in reverse supply chains. Genetic Algorithm (GA) is utilized to solve the proposed NP-hard problem and find the best possible design for different facilities. In order to test the applicability of proposed GA, we suppose a tire reverse logistic case and solve the problem. The results show that the least cost will be achieved by using the free space of distribution centers and integrating collection and inspection centers within them. In addition, we suggest using hybrid algorithm in future allocation problems to obtain best solutions.
机译:在最近几年中,管理产品的报废和旧产品的回收变得越来越重要。因此,管理产品的反向流动可能是在未来竞争市场中赢得消费者的重要潜力。在这种情况下,建立反向物流网络已成为反向供应链中的主要问题。遗传算法(GA)用于解决提出的NP难题,并为不同的设施找到最佳的设计方案。为了测试所提出的遗传算法的适用性,我们假设一个轮胎逆向逻辑情况并解决该问题。结果表明,通过使用配送中心的自由空间并在其中整合收集和检查中心,可以实现最低的成本。另外,我们建议在将来的分配问题中使用混合算法以获得最佳解决方案。

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