Whole blood or apheresis donations? A multi-objective stochastic optimization approach
Whole blood or apheresis donations? A multi-objective stochastic optimization approach
In the blood supply chain, several alternative technologies are available for collection and processing. These technologies differ in cost and efficiency: for example, collection by apheresis requires very expensive machines but the yield of blood products is considerably greater than whole blood collection. Blood centre managers are faced with the difficult strategic problem of choosing the best combination of technologies, as well as the equally difficult operational problem of assigning donors to collection methods. These decisions are complex since so many factors have to be taken into account, including stochastic demand, blood group compatibilities, donor availability, the proportions of blood types in both donor and recipient populations, fixed and
variable costs, and process efficiencies. The use of deterministic demand forecasts is rarely adequate and a robust decision must consider uncertainty and variability in demand as well as trade-offs between several potentially conflicting objectives. This paper presents a multi objective stochastic integer linear programming model to support such decisions. The model treats demand as stochastic and seeks to optimize two objectives: the total cost and the number of donors required. To solve this problem, we apply a novel combination of Sample Average Approximation and the Augmented Epsilon-Constraint algorithm. This approach is illustrated using real data from Bogota, Colombia.
OR in health services, Blood supply chain, Blood fractionation, Apheresis, Stochastic programming, Multiple objective programming
193-204
Osorio, Andres F.
26b91481-7f68-4fea-90f8-4b3e027d7959
Brailsford, Sally C.
634585ff-c828-46ca-b33d-7ac017dda04f
Smith, Honora K.
1eaef6a6-4b9c-4997-9163-137b956c06b5
1 April 2018
Osorio, Andres F.
26b91481-7f68-4fea-90f8-4b3e027d7959
Brailsford, Sally C.
634585ff-c828-46ca-b33d-7ac017dda04f
Smith, Honora K.
1eaef6a6-4b9c-4997-9163-137b956c06b5
Osorio, Andres F., Brailsford, Sally C. and Smith, Honora K.
(2018)
Whole blood or apheresis donations? A multi-objective stochastic optimization approach.
European Journal of Operational Research, 266 (1), .
(doi:10.1016/j.ejor.2017.09.005).
Abstract
In the blood supply chain, several alternative technologies are available for collection and processing. These technologies differ in cost and efficiency: for example, collection by apheresis requires very expensive machines but the yield of blood products is considerably greater than whole blood collection. Blood centre managers are faced with the difficult strategic problem of choosing the best combination of technologies, as well as the equally difficult operational problem of assigning donors to collection methods. These decisions are complex since so many factors have to be taken into account, including stochastic demand, blood group compatibilities, donor availability, the proportions of blood types in both donor and recipient populations, fixed and
variable costs, and process efficiencies. The use of deterministic demand forecasts is rarely adequate and a robust decision must consider uncertainty and variability in demand as well as trade-offs between several potentially conflicting objectives. This paper presents a multi objective stochastic integer linear programming model to support such decisions. The model treats demand as stochastic and seeks to optimize two objectives: the total cost and the number of donors required. To solve this problem, we apply a novel combination of Sample Average Approximation and the Augmented Epsilon-Constraint algorithm. This approach is illustrated using real data from Bogota, Colombia.
Text
Osorio et al Version 4 (final)
- Accepted Manuscript
More information
Accepted/In Press date: 5 September 2017
e-pub ahead of print date: 14 September 2017
Published date: 1 April 2018
Keywords:
OR in health services, Blood supply chain, Blood fractionation, Apheresis, Stochastic programming, Multiple objective programming
Identifiers
Local EPrints ID: 414202
URI: http://eprints.soton.ac.uk/id/eprint/414202
ISSN: 0377-2217
PURE UUID: a9be18ef-8ee9-4fa4-b561-5242f88d5b55
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Date deposited: 18 Sep 2017 16:31
Last modified: 17 Dec 2019 05:55
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Author:
Andres F. Osorio
University divisions
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