Supply Chain Asset Configuration via Approximation Algorithms

我们解决了供应链中的固定资产配置问题。具体来说，我们会考虑制造和配送中心应该设在哪里，哪些产品应该在这些设施中生产或储存，哪些客户应该从哪些设施中服务。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，并且随着问题规模的增长可以很好地扩展。与之竞争的方法则不那么稳健。我们需要解决的主要挑战是开发包含现实世界供应链关键方面的近似算法，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。我们将开发的算法既可以作为独立的算法，也可以与分支定界结合使用。要进行的主要活动是算法开发，以及使用生成的和公司的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，让本科生参与进来，并接触到代表性不足的群体。最后，我们打算与软件供应商（这类研究的主要实施者）密切互动，以确保我们对供应链驱动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也是最昂贵的方面之一。无论是国内还是全球，各种规模的公司都面临着这些问题。金融影响是巨大的。商业供应链与其他问题领域有很强的相似性，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，联合国）、电信系统等。这项研究也有可能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.