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A Joint Location-Inventory Model

机译:联合位置库存模型

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We consider a joint location-inventory problem involving a single supplier and multiple retailers. Associated with each retailer is some variable demand. Due to this variability, some amount of safety stock must be maintained to achieve suitable service levels. However, risk-pooling benefits may be achieved by allowing some retailers to serve as distribution centers (and therefore inventory storage locations) for other retailers. The problem is to determine which retailers should serve as distribution centers and how to allocate the other retailers to the distribution centers. We formulate this problem as a nonlinear integer-programming model. We then restructure this model into a set-covering integer-programming model. The pricing problem that must be solved as part of the column generation algorithm for the set-covering model involves a nonlinear term in the retailer-distribution-center allocation terms. We show that this pricing problem can (theoretically) be solved efficiently, in general, and we show how to solve it practically in two important cases. We present computational results on several instances of sizes ranging from 33 to 150 retailers. In all cases, the lower bound from the linear-programming relaxation to the set-covering model gives the optimal solution.
机译:我们考虑一个涉及单个供应商和多个零售商的联合位置库存问题。与每个零售商相关联的是一些可变的需求。由于这种可变性,必须保持一定数量的安全库存以达到合适的服务水平。但是,可以通过允许某些零售商充当其他零售商的分销中心(并因此充当库存中心)来实现风险分担收益。问题在于确定哪些零售商应充当配送中心,以及如何将其他零售商分配给配送中心。我们将此问题公式化为非线性整数编程模型。然后,我们将此模型重构为一个集覆盖整数的编程模型。作为集合覆盖模型的列生成算法的一部分必须解决的定价问题涉及零售商-分销中心分配项中的非线性项。我们证明了这个定价问题(理论上)通常可以有效地解决,并且我们展示了如何在两种重要情况下实际解决这一问题。我们展示了33家至150家零售商的几种规模实例的计算结果。在所有情况下,从线性规划松弛到集覆盖模型的下限均提供了最佳解决方案。

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