Modeling and optimization of cooperative multi-warehouse inventory systems under uncertainty: A scenario-based probabilistic programming approach

Abstract An effective inventory system is critical for enhancing overall profitability and performance in supply chain management. This study proposes a novel decision-support system tailored to address optimization of cooperative multi-warehouse inventory operating under uncertainty. In addition, supplier selection is incorporated. The model considers collaborative interactions between warehouses, allowing them to coordinate to meet aggregate demand. Furthermore, the model also incorporates uncertain factors, such as fluctuating demand and variable supply conditions. A robust mathematical programming model was formulated and solved in Python programming language using Gurobi solver. Computational experiments demonstrate the model’s capability to generate optimal decisions under the given uncertainties. The findings provide valuable managerial insights and practical tools for supply chain stakeholders aiming to enhance decision-making and operational resilience under uncertain conditions.