A bi-objective optimization model for technology selection and donor’s assignment in the blood supply chain

Andrés Felipe Osorio Muriel, Sally Brailsford, Honora Smith

Abstract


Decision-making processes often contain more than one objective. In technology selection in the blood collection processes, the cost related to the collection technology and the amount of donors required to meet the demand are in conflict. In the same way, in the blood supply chains decisions become more complex when features of the system such as blood type proportions and compatibilities are considered. In order to generate solutions to this problem, an Integer Linear Programming is proposed considering total cost minimisation and amount of donors required. This model also considers distinct constraints such as capacity, proportionality, and demand fulfilment among others. Open Solver 2.1 was used to solve this problem in combination with Visual Basic for Applications for generating the set of efficient solutions that make up the Pareto front through the augmented Epsilon constraint algorithm.


Keywords


Blood supply chain; multi-objective optimization, Epsilon constraint; blood fractionation; aphaeresis.

References


Alfonso, E., Xie, X.L., Augusto, V., Garraud, O. (2012). Modeling and simulation of blood collection systems. Health Care Management Science, 15(1), 63-78.

Alfonso, E., Xie, X.L., Augusto, V., Garraud, O. (2013). Modelling and simulation of blood collection systems: Improvement of human resources allocation for better cost-effectiveness and reduction of candidate donor abandonment. Vox Sanguinis, 104(3), 225-233.

Baesler, F., Martinez, C., Yaksic, E., & Herrera, C. (2011). Logistic and production process in a regional blood center: modeling and analysis. Revista Medica de Chile, 139(9), 1150-1156.

Baesler, F., Nemeth, M., Martínez, C., & Bastías, A. (2013). Analysis of inventory strategies for blood components in a regional blood center using process simulation. Transfusion, 54(2), 323-330.

Beltran, M., Ayala, M., & Jara, J. (1999). Frecuencia de grupos sanguíneos y factor Rh en donantes de sangre, Colombia , 1996, Biomédica, 19(1), 39-44.

Boppana, R.V. & Chalasani, S. Analytical models to determine desirable blood acquisition rates. In 2007 IEEE International Conference on System of Systems Engineering, San Antonio, TX, 2007. Piscataway, NJ: IEEE.

Chazan, D. & Gal, S. (1977). A Markovian model for a perishable product inventory. Management Science, 23(5), 512-521.

Cohen, M.A. (1976). Analysis of single critical number ordering policies for perishable inventories. Operations Research, 24(4), 726-741.

Cumming, P.D., Kendall, K.E., Pegels, C.C., Seagle, J.P., & Shubsda, J.F. (1976). A collections planning model for regional blood suppliers: description and validation. Management Science, 22(9), 962-971.

Decreto 2423 de 1996. (1997, enero 31). Diario Oficial No. 42.961. Bogotá, Colombia: Imprenta Nacional

Ehrgott, M. & Ruzika, S. (2005). An improved Epsilon-Constraint method for multiobjective programming. [on line]. Retrieved from file:///C:/Users/Jos%C3%A9Ignacio/Downloads/nr96.pdf

Glynn, S.A., et al. (2003). Effect of a national disaster on blood supply and safety: The September 11 experience. JAMA, 289(17), 2246-2253.

Haimes, Y., Lasdon, L. Wismer,D. (1971). On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE Transactions on Systems, Man and Cybernetics,SMC-1(3), 296-297.

Instituto Nacional de Salud [INS]. (2012). Informe nacional de indicadores. Bogotá, Colombia: INS.

Jagannathan, R. & Sen, T. (1991). Storing crossmatched blood: a perishable inventory model with prior allocation. Management Science, 37(3), 251-266.

Lowalekar, H. & Ravichandran, N. (2010). Model for blood collections management. Transfusion, 50(12-pt2), 2778-2784.

Madden, E., Murphy, E.L., Custer, B. (2007). Modeling red cell procurement with both double-red-cell and whole-blood collection and the impact of European travel deferral on units available for transfusion. Transfusion, 47(11), 2025-2037.

Marler, R.T. & Arora, J.S. (2004). Survey of multi-objective optimization methods for engineering. Structural and Multidisciplinary Optimization, 26, 369-395.

Mavrotas, G. (2009). Effective implementation of the ε-constraint method in Multi-Objective Mathematical Programming problems. Applied Mathematics and Computation, 213(2),455-465.

Melnyk, S.A., Pagell, M., Jorae, G., & Sharpe, A.S. (1995). Applying survival analysis to operations management: Analyzing the differences in donor classes in the blood donation process. Journal of Operations Management, 13(4), 339-356.

Nahmias, S. & Pierskalla, W.P. (1976). A two-product perishable/nonperishable inventory problem. SIAM Journal on Applied Mathematics, 30(3), 483-500.

Pierskalla, W.P. & Roach, C.D. (1972). Optimal issuing policies for perishable inventory. Management Science, 18(11), 603-614.

Rytilä, J.S. & Spens, K.M. (2006). Using simulation to increase efficiency in blood supply chains. Management Research News, 29(12), 801-819.

Seifried, E., et al. (2011). How much blood is needed? Vox Sanguinis, 100(1), 10-21.

Simonetti, A., Forshee, R.A., Anderson, S.A., & Walderhaug, M. (2013). A stock-and-flow simulation model of the US blood supply. Transfusion54(3), 828-838.

Sonmezoglu, M., et al. (2005). Effects of a major earthquake on blood donor types and infectious diseases marker rates. Transfusion Medicine, 15(2), 93-97.




DOI: http://dx.doi.org/10.18046/syt.v12i30.1854

Refbacks

  • There are currently no refbacks.