Approximation schemes for the joint inventory selection and online resource allocation problem
Approximation schemes for the joint inventory selection and online resource allocation problem
复制标题
DOI：
10.1111/poms.13742
复制
发表时间：
2022-06-20
影响因子：
5
通讯作者：
Jung, Seung Hwan
中科院分区：
文献类型：
--
作者：
Chen, Xingxing;Feldman, Jacob;Jung, Seung Hwan
本文引入并研究了联合库存选择和在线资源分配问题，该问题具有两个不可撤销关联的连续决策集的特征。首先，决策者（DM）必须为一组可用资源选择起始库存水平。随后，DM必须以在线方式将到达的客户与可用资源相匹配，以最大化预期回报。我们首先从最一般的角度研究这个问题，然后再关注安海斯-布希英博（Anheuser Busch InBev）出现的一个特定版本。我们一般设置的这个特殊应用程序称为ABI拖车问题，它考虑ABI如何通过第三方送货卡车将啤酒运送到供应商。在这个问题中，ABI必须选择预先装载啤酒拖车的重量，然后以在线方式将其与到达的第三方送货卡车进行匹配。对于一般设置，我们开发了简单且易于实现的方法，这些方法具有健壮的最坏情况性能保证。对于ABI设置，我们揭示了与最优匹配策略相关的简化结构属性，这导致了我们原始方法的自然适应。我们通过大量的数值实验来测试这些政策的有效性，我们发现我们的方法要么接近最优，要么在最先进的基准上有所改进。特别是，使用ABI的数据集，我们能够生成ABI拖车问题的实例，在此基础上，我们的算法有可能每年产生数百万美元的收入改进。
In this paper, we introduce and study the joint inventory selection and online resource allocation problem, which is characterized by two sequential sets of decisions that are irrevocably linked. First, a decision maker (DM) must select starting inventory levels for a set of available resources. Subsequently, the DM must match arriving customers to available resources in an online fashion so as to maximize expected reward. We first study the problem in its most general form, before focusing on a specific version that arises at Anheuser Busch InBev (ABI). This particular application of our general setting is referred to as the ABI Trailer Problem, and it considers how ABI ships its beer to vendors via third-party delivery trucks. In this problem, ABI must select the weights of preloaded trailers of beer, which are then matched in an online fashion to the arriving third-party delivery trucks. For the general setting, we develop simple and easy-to-implement approaches that come with robust worst-case performance guarantees. For the ABI setting, we reveal a simplifying structural property related to the optimal matching policy, which gives rise to a natural adaptation of our original approach. We test the efficacy of these policies through extensive numerical experiments, where we find that our approaches are either near-optimal or improve upon state-of-the-art benchmarks. In particular, using a data set from ABI, we are able to generate instances of the ABI Trailer Problem, on which our algorithm has the potential to yield revenue improvements in the range of millions of dollars per year.