Maximizing Weapon System Availability With a Multi-Echelon Supply Network

Weapon systems are comprised of parts that are subject to random failures. When a part fails, it must be replaced by an operable part that is provided by a supply network that supports the system. Supply networks consist of many locations where spare parts are held, known as echelons. Examples include depots, fulfillment centers, and customers. When many identical weapon systems operate in parallel and rely on a multi-echelon supply network for replacement parts, decision makers must choose where and how to invest their resources into the purchase of spare parts. This thesis uses stochastic optimization to leverage those decisions in order to maximize the expected number of time periods a set of weapon systems are available for use. Specifically, we develop a model that determines the optimal stock levels of spare parts to store at each echelon of the supply network. The formulation integrates part failure uncertainty, transit times, and monetary constraints. Model outputs also provide decision makers with a clear estimate of marginal availability gains for each dollar invested in purchasing spare parts.