REA Press Null REA Press3042-31203042-3120 REA Press https://doi.org/10.22105/ahse.vi.52 Research Article Blood supply chain, Bi-level stochastic programming, Improved clustering, Multi-criteria decision making, Uncertainty modeling, Combined MAUT-TOPSIS A Data-Driven Framework for Blood Supply Chain Network Design under Uncertainty Using Improved Clustering and Multi-Criteria Decision-MakingA Data-Driven Framework for Blood Supply Chain Network Design under Uncertainty Using Improved Clustering and Multi-Criteria Decision-Making Amou Jafari Amir Hossein Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran. Akbari Jokar Mohammad Reza Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran. 03 2026 12 03 2026 3 1 © 2026 REA Press 2026

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 A Data-Driven Framework for Blood Supply Chain Network Design under Uncertainty Using Improved Clustering and Multi-Criteria Decision-Making

The blood supply chain is a critical component of healthcare systems, where inefficient planning may lead to severe shortages, wastage, and delayed response to emergency needs. The perishability of blood products, demand fluctuations, limited collection capacities, and geographical coverage requirements make the design of a reliable and responsive blood supply network a challenging decision-making problem under uncertainty. Therefore, developing an integrated framework that can improve resource allocation, facility location, and demand coverage is essential for enhancing the operational performance of such systems. To address this challenge, this study proposes a bi-level stochastic model for the optimal design of a blood supply chain under uncertainty. Within the proposed framework, an improved constrained clustering approach is employed to group demand points, and the locations of temporary blood collection facilities are determined using a combined MAUT–TOPSIS ranking method. The weights of the decision-making criteria are calculated using the entropy method. By considering multiple scenarios, capacity limitations, and geographical coverage constraints, the developed model optimally addresses the resource allocation problem in the blood supply network. The numerical results obtained from a real-world case study in Tehran show that the proposed clustering structure and combined ranking method significantly reduce unmet blood demand under the worst-case scenario compared with the conventional approach. Specifically, the total unmet blood demand over three time periods decreased from 17,592 units to 14,050 units, representing an approximately 20% improvement in system performance. Furthermore, the comparison of multi-criteria decision-making methods indicates that the combined MAUT–TOPSIS approach increases the total collected blood from 1,699 units under TOPSIS and 1,885 units under MAUT to 2,460 units. This corresponds to improvements of approximately 45% and 31%, respectively, in the blood collection system under the worst-case scenario.

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