Collaborative Research: Integrated Supply Chain Design for Customer Service, Robustness and Information Systems

库存管理已成为服务行业的主要关注点。 客户服务期望的提高和产品寿命的缩短使库存决策变得复杂。 企业必须决定在哪里储存库存以及维持多少库存以满足客户需求。 我们将开发一个新的综合设施位置和库存模型，使管理人员能够更好地决定（1）有多少设施，（2）这些库存位置应该在哪里，（3）在每个地点库存多少库存，（4）多久重新订购库存，以及（5）将哪些客户分配给每个地点，以最大限度地降低总成本，同时满足特定的客户服务要求。 这项研究将建立在PI的早期工作中，我们已经开发了一个基本的位置/库存模型。 我们将以多种不同的方式增强建模方法，使模型能够更真实地捕捉实际的位置/库存问题。 特别是，我们将纳入（1）配送中心（DC）和各种运输渠道的容量限制，（2）从配送中心到零售商的更现实的本地交付路线，包括零担（LTL）运输，（3）从供应商到DC的多种运输模式，以及（4）多种库存类别和多种客户类型。 我们将开发考虑客户服务需求的模型，并根据资源限制优化服务。 然后，我们将开发多目标模型，探索服务和成本之间的权衡。另一个重要的任务将是将未来的场景纳入位置/库存模型，以确定解决方案，是强大的关于未来的不确定性。 我们还将开发一类新的基于可靠性概念的定位模型。 如果系统可以继续充分运行，即使系统的某些部分（例如，DC或网络链接）。 最后，我们将探讨将信息源纳入综合设施选址/库存模型。各种解决方案的程序将被用于开发的模型，包括最佳的，非线性整数规划为基础的算法时，可能和启发式程序时，最佳算法无法设计。

Inventory management has become a major concern in the service industries. Heightened customer service expectations and reduced product life spans have complicated inventory decision-making. Firms must decide where to stock inventory and how much inventory to maintain to satisfy customer demands. We will develop a new integrated facility location and inventory models that will allow managers to better decide (1) how many facilities to have, (2) where those stocking locations should be, (3) how much inventory to stock at each site, (4) how often to reorder inventory, and (5) which customers to assign to each site to minimize overall costs while satisfying specified customer service requirements. The research will build on earlier work of the PIs in which we have developed an elementary location/inventory model. We will enhance the modeling approach in a number of different ways to allow the model to capture actual location/inventory problems more realistically. In particular, we will incorporate (1) capacity restrictions at the distribution centers (DCs) and in the various transport channels, (2) more realistic local delivery routing from the distribution center(s) to the retailers including less-than-truckload (LTL) shipments, (3) multiple shipment modes from suppliers to DCs, and (4) multiple classes of inventory and multiple customer types. We will develop models that consider customer service requirements and that optimize service subject to resource constraints. We will then develop multi-objective models that explore tradeoffs between service and cost.Another significant task will be incorporating future scenarios in location/inventory models to identify solutions that are robust with respect to uncertainty about the future. We also will develop a new class of location models based on concepts of reliability. A solution is deemed reliable if the system can continue to function adequately even if parts of the system (e.g., DCs or network links) are removed. Finally, we will explore the incorporation of information sources into integrated facility location/inventory models. A variety of solution procedures will be employed for the models to be developed including optimal, non-linear integer programming based algorithms when possible and heuristic procedures when optimal algorithms cannot be devised.