4.6 Article

Re-design of a blood supply chain organization with mobile units

Journal

SOFT COMPUTING
Volume 25, Issue 8, Pages 6311-6327

Publisher

SPRINGER
DOI: 10.1007/s00500-021-05618-3

Keywords

Blood supply chain; Blood; Humanitarian supply chain management; Mixed-integer programming

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This research presents a novel multi-objective mixed-integer location-allocation model for a blood supply chain design problem, which effectively plans a supply chain network consisting of mobile and permanent units. By prioritizing objectives using the Analytical Hierarchical Process, the model minimizes distances between blood supply chain elements and the length of mobile unit routes. Empirical results show that the proposed model offers at least 25% more effective solutions compared to the current situation.
This research analyses the re-organization of a blood supply chain organization. Blood supply chain network design is a hard problem. Uncertainties of the blood supply and demand, perishability of blood over time and compatibility of blood types are some factors that make the problem difficult. This paper presents a novel multi-objective mixed-integer location-allocation model for a blood supply chain design problem. Unlike many studies on blood supply chain design in the literature, supply chain network consisting of mobile and permanent units is planned together effectively with our mixed-integer programming model. Multi-objective structure of the model minimizes distances between the blood supply chain elements and the length of the mobile unit routes. The objectives are prioritized by experts using the Analytical Hierarchical Process. Finally, the model is implemented on a real life case study using real data from the Eastern Anatolia region of Turkey for various supply demand scenarios. The solutions offered by the model are compared with the current situation in the region. It is shown that the proposed model gives at least %25 more effective solutions. Moreover, sensitivity analysis on the budget constraint is conducted, and robustness of the model is empirically illustrated.

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