A multi-period fuzzy optimization strategy for managing a centralized blood supply chain

Abstract

Blood supply chains (BSCs) are highly complex systems that present many challenges in their optimal management, such as different collection methods, demand and supply uncertainty, blood perishability, blood group distinction, and compatible substitutions. This article presents the study of the critical problem of sizing and managing a centralized version of such BSCs in a developing country. The problem is initially formulated as a multi-period mixed-integer linear programming (MILP) model simultaneously addressing strategic, tactical, and operational decisions over a given time horizon. The formers are related to selecting the technology for blood collection and processing, the tactical ones determine not only where and when donation campaigns are made but also the periodic delivery of surplus plasma for further fractionation, while the operational ones specify the amounts of blood collected, donor allocation to each collection method, and quantities of blood components produced, distributed, and kept in stock daily. The problem aims to minimize three conflictive objective functions: the shortage, the total costs, and the number of substitutions. To appropriately address the multiple goals, their imprecise target values, and the fuzziness in some parameters, the model is reformulated as a fuzzy mixed-integer goal programming (FMIGP) one, which is then solved using a crisp strategy to find a compromise solution. A real-life case study from the public sector of Bahía Blanca city in Argentina shows the advantages of the presented approach. Numerical results demonstrate the integrated model can significatively increase demand satisfaction while reducing costs, less favorable substitutions, and wastes.