Supply Chain Asset Configuration via Approximation Algorithms

我们解决供应链中的固定资产配置问题。具体来说，我们会研究制造和配送中心应该位于何处，哪些产品应该在这些设施中生产或储存，以及哪些客户应该从哪些设施中获得服务。我们的研究将基于近似算法，原因如下：解决方案的质量和运行时间都很强，是可预测的，并且随着问题大小的增长而扩展。相互竞争的方法不那么稳健。我们需要解决的主要挑战是开发近似算法，该算法结合了现实世界供应链的关键方面，包括更准确的容量模型，基于概率场景的建模，库存的随机需求，以及更灵活的分销网络拓扑结构。我们将开发的算法既可以作为独立的算法，也可以与分支定界算法结合使用。拟开展的主要活动是算法开发，以及利用生成的数据集和共同数据集进行计算测试。我们研究的计算方面为我们提供了一个很好的机会，让本科生参与，并接触到代表性不足的群体。最后，我们打算与软件供应商密切互动，这类研究的主要实施者，以确保我们对供应链驱动的组织的需求的看法是正确的，并作为工业数据的来源。资产配置是供应链设计中最重要和最昂贵的方面之一。这些问题都面临着大量的公司，各种规模，无论是在国内还是全球。财政影响是巨大的。商业供应链与其他问题领域有很大的相似性，例如全球和国家救济网络（例如，红十字会）、分发人道主义援助的网络（例如，这项研究也有很大的潜力对这些领域产生影响。

We address the problem of fixed asset configuration in supply chains. Specifically, we look at where manufacturing and distribution centers should be located, which products should be produced or stocked at those facilities, and which clients should be served from which facilities. Our research will be based on approximation algorithms, for the following reason: both solution quality and run times are strong, are predictable, and scale very well as the problem sizes grow. Competing approaches are less robust. The major challenge we need to address is to develop approximation algorithms that incorporate key aspects of real-world supply chains, including more accurate models of capacity, probabilistic-scenario-based modeling, stochastic demand for inventory, and more flexible topologies for distribution networks. The algorithms we will develop are useful both as stand-alone algorithms, and in conjunction with branch-and-bound. The main activities to be carried out are algorithm development, and computational testing with generated and corporate data sets. The computational aspects of our research provide us with an excellent opportunity to involve undergraduate students, and to reach out to underrepresented groups. Finally, we intend to interact closely with software vendors, the primary implementers of research of this type, to be sure that our view of the needs of supply-chain-driven organizations is correct, and as a source of industrial data.Asset configuration is one of the most important and expensive aspects of supply chain design. These issues are faced by a vast array of companies, of all sizes, both nationally and globally. The financial implications are huge. Commercial supply chains have strong similarities to other problems domains, such as global and national relief networks (e.g., The Red Cross), networks that distribute humanitarian aid (e.g., the United Nations), telecommunications systems, etc. This research has a strong potential to have an impact on those domains as well.