Stochastic integer programming models for reducing wastages and shortages of blood products at hospitals

Major challenges in the management of the blood supply chain are related to the shortage and wastage of the blood products. Given the perishable characteristics of this product, storing an excessive number of blood units on inventory could result on the wastage of this limited resource. On the other...

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Vydáno v:Computers & operations research Ročník 54; s. 129 - 141
Hlavní autoři: Gunpinar, Serkan, Centeno, Grisselle
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 01.02.2015
Pergamon Press Inc
Témata:
Blood
Blood & organ donations
Blood products
Cost engineering
Cost reduction
Demand
Health care application
Hospitals
Integer programming
Mathematical models
Medical wastes
Optimization
Patients
Perishable inventory
Shortages
Stochastic models
Stochasticity
Studies
Supply chain
Waste disposal
Supply chain
Integer programming
Health care application
Perishable inventory
Blood products
Optimization
ISSN:0305-0548, 1873-765X, 0305-0548
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Shrnutí:Major challenges in the management of the blood supply chain are related to the shortage and wastage of the blood products. Given the perishable characteristics of this product, storing an excessive number of blood units on inventory could result on the wastage of this limited resource. On the other hand, having shortages may result in cancellations of critical health related activities and as a result a potential increase on fatality rates at hospitals. This paper presents integer programming models to minimize the total cost, shortage and wastage levels of blood products at a hospital within a planning horizon. The primary focus is on the red blood cells and the platelet components of the whole blood cells. The stochastic and deterministic models included consider uncertain demand rates, demand for two types of patients, and crossmatch-to-transfusion ratio. Results show wastage rates decreasing from 19.9% to 2.57% on average. In addition, the shortages and total cost are reduced 91.43% and 20.7% respectively for a given capacity increases. Computational results are included and discussed.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2014.08.017