A stochastic, multi-commodity multi-period inventory-location problem: Modeling and solving an industrial application

Abstract

This paper addresses the real-world supply chain network design problem with a strategic multi-commodity and multi-period inventory-location problem with stochastic demands. The proposed methodology involves a complex non-linear, non-convex, mixed integer programming model, which allows for the optimization of warehouse location, demand zone’s assignment, and manufacturing settings while minimizing the fixed costs of a distribution center (DC), along with the transportation and inventory costs in a multi-commodity, multi-period scenario. In addition, a genetic algorithm is implemented to obtain near-optimal solutions at competitive times. We applied the model to a real-world industrial case of a Colombian rolled steel manufacturing company, where a new, optimized supply chain distribution network is required to serve customers at a national level. The proposed approach provides a practical solution to optimize their distribution network, achieving significant cost reductions for the company.