Abstract
The kidney exchange problem (KEP) is an optimization problem arising in the framework of transplant programs that allow exchange of kidneys between two or more incompatible patient-donor pairs. In this paper an approach based on a new decomposition model and branch-and-price is proposed to solve large KEP instances. The optimization problem considers, hierarchically, the maximization of the number of transplants and the minimization of the size of exchange cycles. Computational comparison of different variants of branch-and-price for the standard and the proposed objective functions are presented. The results show the efficiency of the proposed approach for solving large instances.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Abraham, D., Blum, A., Sandholm, T.: Clearing algorithms for Barter exchange markets: Enabling nationwide kidney exchanges. In: Proceedings of the 8th ACM Conference on Electronic Commerce, June 13-16, pp. 295–304 (2007)
Alvelos, F., de Sousa, A., Santos, D.: Combining Column Generation and Metaheuristics. In: Talbi, E.-G. (ed.) Hybrid Metaheuristics. SCI, vol. 434, pp. 285–334. Springer, Heidelberg (2013)
Ashlagi, I., Gilchrist, D., Roth, A., Rees, M.: Nonsimultaneous chains and dominos in kidney paired donation - revisited. American Journal of Transplantation 11(5), 984–994 (2011)
Barnhart, C., Johnson, E., Nemhauser, G., Savelsbergh, M., Vance, P.: Branch-and-price: column generation for solving huge integer programs. Operations Research 46, 316–329 (1998)
BBC: BBC news website. six-way kidney transplant first (9/04/2008) (2008), http://news.bbc.co.uk/1/health/7338437.stm (last accessed in December 2012)
Biro, P., Manlove, D., Rizzi, R.: Maximum weight cycle packing in directed graphs, wiht application to kidney exchange programs. Discrete Mathematics, Algorithms and Applications 1(4), 499–517 (2009)
Constantino, M., Klimentova, X., Viana, A., Rais, A.: New insights on integer-programming models for the kidney exchange problem. European Journal of Operational Research 231(1), 57–68 (2013)
Dantzig, G., Wolfe, P.: Decomposition principle for linear programs. Operations Research 8, 101–111 (1960)
Dickerson, J., Procaccia, A., Sandholm, T.: Optimizing kidney exchange with transplant chains: Theory and reality. In: AAMAS 2012: Proc. 11th Intl. Joint Conference on Autonomous Agents and Multiagent Systems (June 2011)
Dickerson, J., Procaccia, A., Sandholm, T.: Failure-aware kidney exchange. In: EC 2013: Proc. 14th ACM Conference on Electronic Commerce (June 2013)
Floyd, R.: Algorithm 97: Shortest path. Communications of the ACM 5(6), 345 (1962)
Gentry, S., Montgomery, R., Segev, D.: Kidney paired donation: Fundamentals, limitations, and expansions. American Journal of Kidney Disease 57(1), 144–151 (2010)
Gentry, S., Montgomery, R., Swihart, B., Segev, D.: The roles of dominos and nonsimultaneous chains in kidney paired donation. American Journal of Transplantation 9, 1330–1336 (2009)
Geoffrion, A.: Lagrangean relaxation for integer programming. Mathematical Programming Study 2, 82–114 (1974)
Glorie, K., Wagelmans, A., van de Klundert, J.: Iterative branch-and-price for large multi-criteria kidney exchange. Econometric Institute report (2012-11) (2012)
Hansen, P., Mladenovic, N., Perez, J.: Variable neighbourhood search: methods and applications. Annals of Operations Research 175, 367–407 (2010)
Huang, C.: Circular stable matching and 3-way kidney transplant. Algorithmica 58, 137–150 (2010)
de Klerk, M., Keizer, K., Claas, F., Haase-Kromwijk, B., Weimar, W.: The Dutch national living donor kidney exchange program. American Journal of Transplantation 5, 2302–2305 (2005)
Manlove, D.F., O’Malley, G.: Paired and altruistic kidney donation in the UK: Algorithms and experimentation. In: Klasing, R. (ed.) SEA 2012. LNCS, vol. 7276, pp. 271–282. Springer, Heidelberg (2012)
Montgomery, R., Gentry, S., Marks, W., Warren, D., Hiller, J., Houp, J., Zachary, A., Melancon, J., Maley, W., Simpkins, H.R.C., Segev, D.: Domino paired kidney donation: a strategy to make best use of live non-directed donation. The Lancet 368(9533), 419–421 (2006)
Nemhauser, G., Wolsey, L.: Integer and Combinatiorial Optimization. A Wiley-Interscience Publication (1999)
Pedroso, J.: Maximizing expectation on vertex-disjoint cycle packing. Technical Report DCC-2013-5 (2013)
Rees, M., Kopke, J., Pelletier, R., Segev, D., Rutter, M., Fabrega, A., Rogers, J., Pankewycz, O., Hiller, J., Roth, A., Sandholm, T., Ünver, M., Montgomery, R.: A nonsimultaneous, extended, altruistic-donor chain. The New England Journal of Medicine 360, 1096–1101 (2009)
Roth, A., Sönmez, T., Ünver, M.: Kidney exchange. Quarterly Journal of Economics 119(2), 457–488 (2004)
Roth, A., Sönmez, T., Ünver, M.: Efficient kidney exchange: Coincidence of wants in markets with compatibility-based preferences. The American Economic Review 97(3), 828–851 (2007)
Saidman, S., Roth, A., Sönmez, T., Ünver, M., Delmonico, F.: Increasing the opportunity of live kidney donation by matching for two- and three-way exchanges. Transplantation 81, 773–782 (2006)
Segev, D., Gentry, S., Warren, D., Reeb, B., Montgomery, R.: Kidney paired donation and optimizing the use of live donor organs. The Journal of the American Medical Association 293(15), 1883–1890 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Klimentova, X., Alvelos, F., Viana, A. (2014). A New Branch-and-Price Approach for the Kidney Exchange Problem. In: Murgante, B., et al. Computational Science and Its Applications – ICCSA 2014. ICCSA 2014. Lecture Notes in Computer Science, vol 8580. Springer, Cham. https://doi.org/10.1007/978-3-319-09129-7_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-09129-7_18
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09128-0
Online ISBN: 978-3-319-09129-7
eBook Packages: Computer ScienceComputer Science (R0)