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

库存管理已成为服务行业关注的主要问题。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。公司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。我们将开发一种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)拥有多少设施，(2)这些库存地点应该在哪里，(3)每个地点的库存数量，(4)重新订购库存的频率，以及(5)将哪些客户分配到每个地点，以最大限度地降低总成本，同时满足特定的客户服务要求。这项研究将建立在早期pi工作的基础上，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，包括少于卡车（LTL）装运，(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，并在资源限制下优化服务。然后，我们将开发多目标模型，探索服务和成本之间的权衡。另一项重要任务是将未来场景纳入位置/库存模型，以确定针对未来不确定性的稳健解决方案。我们还将基于可靠性的概念开发一类新的位置模型。如果系统的某些部分（例如数据中心或网络链接）被移除，系统仍能继续正常运行，则该解决方案被认为是可靠的。最后，我们将探讨将信息源整合到综合设施位置/库存模型中。要开发的模型将采用各种解决程序，包括在可能的情况下基于最优非线性整数规划的算法，以及在无法设计最优算法时采用启发式程序。

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.