Improved instance generation for kidney exchange programmes

Delorme, M., García, S., Gondzio, J., Kalcsics, J., Manlove, D. , Pettersson, W. and Trimble, J. (2022) Improved instance generation for kidney exchange programmes. Computers and Operations Research, 141, 105707. (doi: 10.1016/j.cor.2022.105707)

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Kidney exchange programmes increase the rate of living donor kidney transplants, and operations research techniques are vital to such programmes. These techniques, as well as changes to policy regarding kidney exchange programmes, are often tested using random instances created by a Saidman generator. We show that instances produced by such a generator differ from real-world instances across a number of important parameters, including the average number of recipients that are compatible with a certain donor. We exploit these differences to devise powerful upper and lower bounds and we demonstrate their effectiveness by optimally solving a benchmark set of Saidman instances in seconds; this set could not be solved in under thirty minutes with previous algorithms. We then present new techniques for generating random kidney exchange instances that are far more consistent with real-world instances from the UK kidney exchange programme. This new process for generating random instances provides a more accurate base for comparisons of algorithms and models, and gives policy-makers a better understanding of potential changes to policy leading to an improved decision-making process.

Item Type:Articles
Additional Information:This research was supported by the Engineering and Physical Science Research Council, United Kingdom through grant numbers EP/P028306/1 (Manlove and Pettersson), EP/P029825/1 (Delorme, García, Gondzio, and Kalcsics), and EP/R513222/1 (Trimble).
Glasgow Author(s) Enlighten ID:Manlove, Professor David and Trimble, Mr James and Pettersson, Dr William
Authors: Delorme, M., García, S., Gondzio, J., Kalcsics, J., Manlove, D., Pettersson, W., and Trimble, J.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Computers and Operations Research
ISSN (Online):1873-765X
Published Online:21 January 2022
Copyright Holders:Copyright © 2022 The Author(s)
First Published:First published in Computers and Operations Research 141: 105707
Publisher Policy:Reproduced under a Creative Commons licence

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
300808IP-MATCH: Integer Programming for Large and Complex Matching ProblemsDavid ManloveEngineering and Physical Sciences Research Council (EPSRC)EP/P028306/1Computing Science
305200DTP 2018-19 University of GlasgowMary Beth KneafseyEngineering and Physical Sciences Research Council (EPSRC)EP/R513222/1MVLS - Graduate School