Algorithms for exchange of kidneys is one of the key successful applications in market design, artificial intelligence, and operations research. Potent immunosuppressant drugs suppress the body's ability to reject a transplanted organ up to the point that a transplant across blood- or tissue-type incompatibility becomes possible. In contrast to the standard kidney exchange problem, we consider a setting that also involves the decision about which recipients receive from the limited supply of immunosuppressants that make them compatible with originally incompatible kidneys. We firstly present a general computational framework to model this problem. Our main contribution is a range of efficient algorithms that provide flexibility in terms of meeting meaningful objectives. Motivated by the current reality of kidney exchanges using sophisticated mathematical-programming-based clearing algorithms, we then present a general but scalable approach to optimal clearing with immunosuppression; we validate our approach on realistic data from a large fielded exchange.

中文翻译：

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与免疫抑制剂的最佳肾脏交换

肾脏交换算法是市场设计，人工智能和运营研究中的关键成功应用之一。强有力的免疫抑制剂药物抑制人体排斥移植器官的能力，直至可以跨血型或组织型不相容性进行移植。与标准的肾脏交换问题相反，我们考虑的环境还涉及以下决定：哪些接受者会从有限的免疫抑制剂供应中获得接受，从而使它们与最初不兼容的肾脏相容。我们首先提出一个通用的计算框架来对这个问题进行建模。我们的主要贡献是一系列有效的算法，这些算法可在满足有意义的目标方面提供灵活性。出于当前肾脏疾病的现实，使用复杂的基于数学程序的清除算法，我们提出了一种通用但可扩展的方法来通过免疫抑制实现最佳清除。我们通过大型现场交易所验证了我们对真实数据的处理方法。