Stochastic mixed-integer programming for a spare parts inventory management problem

The German Armed Forces provide an operation contingent to support the North Atlantic Treaty Organization (NATO) Response Force (NRF). To fulfill a mission, the NRF operates a number of technical systems, mostly vehicles. Each system is composed of several parts which might fail over time, and it can only be used again in the mission if all broken parts are replaced. For short deployments (e.g., one month), the NRF troops bring with them a tightly constrained warehouseof spare parts. To ensure an optimal use of the space, we present a two-stage stochastic programming model where in the first stage spare parts are chosen, then failures occur at random, and in the second stage the parts are assigned to the broken systems. We carry out a scenario-based approach, where the failures are simulated by a Monte-Carlo approach. We demonstrate that the resulting mixed-integer linear program can be solved using standard numerical solvers. Using real-world input data provided by the German Armed Forces and simulated data, we analyze the sensitivity of the solutions with respect to the size of the warehouse, the service level, and the number of scenarios, and compare our approach with simpler, doctrine based warehousing strategies.

Automated summary

The study focuses on optimizing the use of spare parts in a limited warehouse space for military operations to address parts failures. It utilizes a two-stage stochastic programming model and scenario-based approach.