A blood bank location model: A multiobjective approach

Sarul) E. Çetin, L. Sarul / Eur. J. Pure Appl. Math 01/2009; 2(2).


This effort derived a mathematical programming model, which is a hybrid from set covering model of discrete location approaches and center of gravity method of continuous location models, for location of blood banks among hospitals or clinics, rather than blood bank layout in health care institutions. It is initially unknown the number of blood banks will be located within capacity, their geographical locations and their covering area. The solution of the model enlightens the initial darkness in a multiobjective view. The objectives, which are handled via binary nonlinear goal programming, are minimizitation of total fixed cost of location blood banks, total traveled distance between the blood banks and hospitals and an inequality index as a fairness mechanism for the distances. A hypothetical numerical example is solved using MS Excel as a powerful spreadsheet tool. The recipe, which is an application of medical operations research, may be a useful tool for health care policy makers.

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    ABSTRACT: In this paper, we develop a sustainable network design/redesign model for the complex supply chain of human blood, which is a valuable yet highly perishable product. Specifically, we consider the optimal design (or redesign) of a blood banking system consisting of collection sites, blood centers, testing and processing labs, storage facilities, distribution centers as well as demand points. Our multicriteria system-optimization approach on networks with arc multipliers captures several critical concerns associated with blood banking systems including but not limited to the determination of the optimal capacities and the optimal allocations, the induced supply-side risk, and the induced cost of discarding potentially hazardous blood waste, while the uncertain demand for blood is satisfied as closely as possible.
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    ABSTRACT: Blood service operations are a key component of the healthcare system all over the world and yet the modeling and the analysis of such systems from a complete supply chain network optimization perspective have been lacking due to their associated unique challenges. In this paper, we develop a generalized network optimization model for the complex supply chain of human blood, which is a life-saving, perishable product. In particular, we consider a regionalized blood banking system consisting of collection sites, testing and processing facilities, storage facilities, distribution centers, as well as points of demand, which, typically, include hospitals. Our multicriteria system-optimization approach on generalized networks with arc multipliers captures many of the critical issues associated with blood supply chains such as the determination of the optimal allocations, and the induced supply-side risk, as well as the induced cost of discarding the waste, while satisfying the uncertain demands as closely as possible. The framework that we present is also applicable, with appropriate modifications, to the optimization of other supply chains of perishable products. KeywordsSupply chains–Perishable products–Blood banking–Healthcare–Medical waste–Network optimization–Cost minimization–Risk minimization–Multicriteria decision-making–Generalized networks–Variational inequalities
    Computational Management Science 05/2012; 9(2):1-27. DOI:10.1007/s10287-011-0133-z
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    ABSTRACT: The study is focused both on the Location-Allocation Problem and the inventory control problem for a blood banking supply chain. We consider a two-echelon supply chain in which each regional blood center (RBC) sends blood to different CBCs and then delivers it to different allocated hospital blood banks (HBBs). According to the perishable characteristic of blood product, we design a two-staged approach including two models. In strategic stage, we propose model 1 to obtain the location-allocation decisions by determining (a) how many community blood centers (CBCs) should be in an area and (b) where they should be located and (c) which services should be assigned to which CBCs. In tactic stage, we implement model 2 to acquire the inventory control decisions of the optimal blood replenishment quantity and the optimal length of blood order cycle for each CBC. In additions, two objectives are used to construct model 1 so as to make the total supply chain cost the smallest and responsiveness level the biggest, not just a single objective. To solve this multiple objectives programming problem, we use a non-dominated Sorting Genetic Algorithm II (NSGA-II) to search for the Pareto set. MATLAB was implemented to solve our established models. Some computational results for the models using the actual data from all Regional Blood Organizations in Taiwan are derived.
    Modern Advances in Applied Intelligence, 06/2014: pages 511-520;
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