Failure-Aware Kidney Exchange
研究在肾脏交换中，匹配算法提交的循环和链可能失败的问题，提出考虑失败风险的匹配方法，能显著增加预期移植数量，并在真实数据和模拟数据上验证了鲁棒性和可扩展性。
Algorithmic matches in fielded kidney exchanges do not typically result in an actual transplant. We address the problem of cycles and chains in proposed matches failing after the matching algorithm has committed to them. We show that failure-aware kidney exchange can significantly increase the expected number of lives saved (i) in theory, on random graph models; (ii) on real data from kidney exchange match runs between 2010 and 2014; and (iii) on synthetic data generated via a model of dynamic kidney exchange. This gain is robust to uncertainty over the true underlying failure rate. We design a branch-and-price–based optimal clearing algorithm specifically for the probabilistic exchange clearing problem and show that this new solver scales well on large simulated data, unlike prior clearing algorithms. Finally, we show that failure-aware matching can increase overall system efficiency and simultaneously increase the expected number of transplants to highly sensitized patients, in both static and dynamic models. This paper was accepted by Yinyu Ye, optimization.