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Inventory replenishment decision model for the supplier selection problem using metaheuristic algorithms.
Mathematical Biosciences and Engineering ( IF 2.6 ) Pub Date : 2019-12-23 , DOI: 10.3934/mbe.2020107
Avelina Alejo-Reyes 1 , Elias Olivares-Benitez 1 , Abraham Mendoza 1 , Alma Rodriguez 2
Affiliation  

In supply chain management, fast and accurate decisions in supplier selection and order quantity allocation have a strong influence on the company's profitability and the total cost of finished products. In this paper, a novel and non-linear model is proposed for solving the supplier selection and order quantity allocation problem. The model is introduced for minimizing the total cost per time unit, considering ordering, purchasing, inventory, and transportation cost with freight rate discounts. Perfect rate and capacity constraints are also considered in the model. Since metaheuristic algorithms have been successfully applied in supplier selection, and due to the non-linearity of the proposed model, particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution (DE), are implemented as optimizing solvers instead of analytical methods. The model is tested by solving a reference model using PSO, GA, and DE. The performance is evaluated by comparing the solution to the problem against other solutions reported in the literature. Experimental results prove the effectiveness of the proposed model, and demonstrate that metaheuristic algorithms can find lower-cost solutions in less time than analytical methods.

中文翻译:

使用元启发式算法的供应商选择问题的库存补货决策模型。

在供应链管理中,快速准确的供应商选择和订单数量分配决策会严重影响公司的盈利能力和成品总成本。本文提出了一种新颖的非线性模型来解决供应商选择和订单数量分配问题。引入该模型是为了最大程度地降低每时间单位的总成本,同时考虑订购,采购,库存和带有运费折扣的运输成本。模型中还考虑了完美的速率和容量约束。由于元启发式算法已成功应用于供应商选择,并且由于所提出模型的非线性,粒子群优化(PSO),遗传算法(GA)和差异进化(DE),被实现为优化求解器,而不是分析方法。通过使用PSO,GA和DE求解参考模型来测试模型。通过将问题的解决方案与文献中报告的其他解决方案进行比较来评估性能。实验结果证明了该模型的有效性,并证明了与启发式算法相比,启发式算法可以在更短的时间内找到成本更低的解决方案。
更新日期:2019-12-23
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