Solving Tactical Logistics Planning Problems Under Uncertainty

University Of Florida, Gainesville FL

Investigators

Abstract

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.