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Heuristics for Base-Stock Levels in Multi-Echelon Distribution Networks

机译：多级分销网络中基本库存水平的启发式

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We study inventory optimization for locally controlled, continuous-review distribution systems with stochastic customer demands. Each node follows a base-stock policy and a first-come, first-served allocation policy. We develop two heuristics, the recursive optimization (RO) heuristic and the decomposition-aggregation (DA) heuristic, to approximate the optimal base-stock levels of all the locations in the system. The RO heuristic applies a bottom-up approach that sequentially solves single-variable, convex problems for each location. The DA heuristic decomposes the distribution system into multiple serial systems, solves for the base-stock levels of these systems using the newsvendor heuristic of Shang and Song (2003), and then aggregates the serial systems back into the distribution system using a procedure we call backorder matching. A key advantage of the DA heuristic is that it does not require any evaluation of the cost function (a computationally costly operation that requires numerical convolution). We show that, for both RO and DA, changing some of the parameters, such as leadtime, unit backordering cost, and demand rate, of a location has an impact only on its own local base-stock level and its upstream locations' local base-stock levels. An extensive numerical study shows that both heuristics perform well, with the RO heuristic providing more accurate results and the DA heuristic consuming less computation time. We show that both RO and DA are asymptotically optimal along multiple dimensions for two-echelon distribution systems. Finally, we show that, with minor changes, both RO and DA are applicable to the balanced allocation policy.
机译：我们研究具有随机客户需求的本地控制，连续审查分销系统的库存优化。每个节点都遵循基本库存策略和先到先得的分配策略。我们开发了两种启发式算法，即递归优化（RO）启发式算法和分解汇总（DA）启发式算法，以逼近系统中所有位置的最佳基本库存水平。 RO启发式方法采用了一种自下而上的方法，该方法可以依次解决每个位置的单变量凸问题。 DA启发式方法将分发系统分解为多个串行系统，并使用Shang and Song（2003）的新闻供应商启发式方法求解这些系统的基本库存水平，然后使用我们称为“过程”的方法将串行系统聚合回分发系统中缺货匹配。 DA启发式算法的主要优势在于，它不需要对成本函数进行任何评估（计算上需要大量卷积的运算）。我们显示，对于RO和DA而言，更改某个地点的某些参数（例如提前期，单位缺货成本和需求率）仅会影响其自身的本地基础库存水平和上游地点的本地基础库存水平。大量的数值研究表明，两种启发式方法都表现良好，其中RO启发式方法提供更准确的结果，而DA启发式方法消耗的计算时间更少。我们显示，对于两级分布系统，RO和DA都沿着多个维度渐近最优。最后，我们表明，经过微小的更改，RO和DA均适用于平衡分配策略。

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