Solving Tactical Logistics Planning Problems Under Uncertainty

该基金支持在不确定需求下开发战术物流规划问题的新理论和模型。本研究的核心决策问题的重点在于优化匹配下游的要求与上游的资源能力，在生产和物流系统。建模方法隐含地考虑了实际物流系统的重要结构特征，这需要识别唯一的上游源设施（例如，工厂，仓库）用于服务于每个下游阶段（例如，仓库、零售商）。这种在生产和物流系统中普遍存在的结构既降低了下游订购和交付接收的复杂性，又减少了上游跨设施协调的需要。作为这些中期决策的结果，短期运营规划和执行问题分解为单独的子问题（每个上游设施一个），这降低了系统范围的运营规划的复杂性。本研究从综合的角度来优化物流网络的性能，将运输决策与其他物流网络决策（如生产和库存决策）结合起来考虑，同时明确考虑需求的不确定性。本项目将提供一种综合的方法来有效地解决战术物流规划问题，从而为简化配送系统成本提供机会。特别是，将开发新的和有效的算法，可以解决中型到大型的问题，以接近最优的计算工作量有限，也享有正式的性能保证。相关的算法将在一般模型设置中开发，允许针对广泛的特定问题上下文进行专业化。此外，通过与一家优化软件开发公司合作，它将提供一种解决方法，使为解决一个问题而开发的大部分软件能够重新用于解决其他问题，从而加快软件开发时间。

This grant supports the development of new theory and models for tactical logistics planning problems under uncertain demand. The core decision problem focus of this research lies in optimally matching downstream requirements with upstream resource capabilities in production and logistics systems. The modeling approach implicitly accounts for an important structural feature of practical logistics systems, which requires identifying a unique upstream source facility (e.g., plant, warehouse) for serving each downstream stage (e.g., warehouse, retailer). This pervasive structure found in production and logistics systems both reduces downstream ordering and delivery receiving complexity, and decreases the need for upstream cross-facility coordination. As a result of these medium-term decisions, short-term operations planning and execution problems decompose into separate subproblems (one for each upstream facility), which reduces system-wide operations planning complexity. This research takes an integrated view of optimizing logistics network performance, where transportation decisions are considered jointly with other logistics network decisions that drive overall performance, such as production and inventory storage decisions, while explicitly accounting for demand uncertainty.This project will provide an integrated approach for solving tactical logistics planning problems effectively, thus providing opportunities for streamlining distribution system costs. In particular, new and effective algorithms will be developed that can solve medium- to large-size problems to near-optimality with limited computational effort, and also enjoy a formal performance guarantee. The associated algorithms will be developed in a general model setting, allowing for specialization to a broad range of specific problem contexts. In addition, through collaboration with an optimization software development firm, it will provide a solution methodology that will allow for the reuse of much of the software developed for one problem in the solution of other problems, thereby speeding up software development time.