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A comprehensive simulation-optimization framework for pharmaceutical distribution networks
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DOI：10.1080/21681015.2025.2564407.png)
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Abstract
En 中文
This paper presents a hierarchical simulation-optimization framework for pharmaceutical warehouse allocation, incorporating demand variability and critical medicine prioritization. The proposed approach uses 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 effectiveness of various network design structures, which incorporate different numbers of warehouses for each region, is evaluated through a comparative analysis of the results obtained from a case study conducted in the Black Sea region of Turkiye. The results show the critical trade-offs between centralized and decentralized network designs under varying cost and demand conditions. Thus, strategically selected warehouses and the optimal number to open were determined for each city. Additionally, the integrated approach presents a hierarchical solution approach for finding efficient solutions to real-world optimization problems.
Keywords:
Pharmaceutical supply chain
healthcare management
pharmaceutical warehouses
optimization
monte carlo simulation
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