Pharmaceutical Supply Chain

A well-functioning medical supply chain is essential for effective patient care and medical supply while preventing stockouts and delays, ensuring cost efficiency. To address these challenges, this paper presents a novel hybrid simulation-optimization methodology for optimizing pharmaceutical warehouse allocation, focusing on high-priority medicine flow and supply readiness. The proposed approach integrates K-means clustering, mixed integer linear programming, and Monte Carlo simulation to overcome the limitations of traditional models by accounting for the stochastic nature of demand and cost for obtaining strategic supply chain decisions. The hybrid methodology considers real-world constraints to effectively manage trade-offs between cost and time, especially under high-priority medicine supply scenarios. The effectiveness of several network design structures is verified by comparing the results obtained from a case study in the Black Sea region of Turkiye. The results demonstrate that centralized network design can provide advantages given the cost and time trade-offs. Additionally, the integrated approach presents a hierarchical solution approach for finding efficient solutions to real-world optimization problems. This study contributes to the literature by filling a critical gap in hybrid simulation-optimization for designing pharmaceutical supply chains.