Blood Supply Chain Network Design under Uncertainty by Integrating Improved Clustering and Multi-Criteria Decision Making

Abstract

In this study, a bi-level stochastic programming model is proposed for the optimal design of a blood supply chain under uncertainty. Within the proposed framework, an improved K-means clustering approach is utilized to cluster demand points, and the location of temporary blood collection facilities is determined using a combined MAUT-TOPSIS ranking method. The decision-making criteria are weighted using the entropy method. The developed model, considering multiple scenarios, capacity limitations, and geographic coverage constraints, aims to allocate resources within the blood supply network optimally. Numerical results based on real data from Tehran indicate that the proposed clustering structure and ranking approach significantly reduce the amount of unmet blood demand under the worst-case scenario. Specifically, the total unmet blood units over three periods decreased from 17,592 to 14,050, reflecting an improvement of approximately 20% in system performance. Furthermore, comparing multi-criteria decision-making methods shows that the combined MAUT-TOPSIS approach increases the total collected blood units from 1,699 (in TOPSIS) and 1,885 (in MAUT) to 2,460 units, corresponding to improvements of approximately 45% and 31%, respectively, in collection performance under the worst-case scenario.