Kidney exchange simulation and optimization

Abstract

One of the challenges in a kidney exchange program (KEP) is to choose policies that ensure an effective and fair management of all participating patients. In order to understand the implications of different policies of patient allocation and pool management, decision makers should be supported by a simulation tool capable of tackling realistic exchange pools and modeling their dynamic behavior. In this paper, we propose a KEP simulator that takes into consideration the wide typology of actors found in practice (incompatible pairs, altruistic donors, and compatible pairs) and handles different matching policies. Additionally, it includes the possibility of evaluating the impact of positive crossmatch of a selected transplant, and of dropouts, in a dynamic environment. Results are compared to those obtained with a complete information model, with knowledge of future events, which provides an upper bound to the objective values. Final results show that shorter time intervals between matches lead to higher number of effective transplants and to shorter waiting times for patients. Furthermore, the inclusion of compatible pairs is essential to match pairs of specific patient-donor blood type. In particular, O-blood type patients benefit greatly from this inclusion.

Kidney Exchange: An Operations Perspective

“…Matching infrequently without reoptimizing while having high match failures is likely to lead to a very low match rate (as this delays discovering the actual compatibility graph). 47 Simulations that consider this tension can be found in Santos et al (2017) and Ashlagi et al (2018b). 48 Some studies consider the problem of maximizing the expected number of matches (Klimentova et al 2016, Wang et al 2017, Bidkhori et al 2020 or using robust optimization (McElfresh et al 2019).…”

“…See alsoCarvalho et al (2020) andBidkhori et al (2020).49 Indeed this is still poorly understood and just a few simulations of matching policies have been considered(Santos et al 2017, Ashlagi et al 2018). Ashlagi and Roth: Kidney Exchange: An Operations Perspective Management Science, Articles in Advance, pp.…”

Outpatient clinic scheduling with limited waiting area capacity

“…Simulation optimisation refers to solving an ILP model with stochastic elements (Karatas et al, 2017;Santos et al, 2017). In many cases simulation is used to estimate the objective function (Fu, 2015).…”

IP solutions for international kidney exchange programmes

“…We conduct long-term simulations with agents arriving and leaving the pool such as in Santos et al (2017), (e.g. with 3-months matching runs for 3 years).…”