Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Dynamic Allocation of Kidneys to Candidates on the Transplant Waiting List
Operations Research
Dynamic Programming
The Optimal Timing of Living-Donor Liver Transplantation
Management Science
Patient Choice in Kidney Allocation: A Sequential Stochastic Assignment Model
Operations Research
Markov decision models for the optimal maintenance of a production unit with an upstream buffer
Computers and Operations Research
A Broader View of Designing the Liver Allocation System
Operations Research
Proceedings of the Winter Simulation Conference
A comparison of decision-maker perspectives for optimal cholesterol treatment
IBM Journal of Research and Development
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The only available therapy for patients with end-stage liver disease is organ transplantation. In the United States, patients with end-stage liver disease are placed on a waiting list and offered livers based on location and waiting time, as well as current and past health. Although there is a shortage of cadaveric livers, 45% of all cadaveric liver offers are declined by the first transplant surgeon and/or patient to whom they are offered. We consider the decision problem faced by these patients: Should an offered organ of a given quality be accepted or declined? We formulate a Markov decision process model in which the state of the process is described by patient state and organ quality. We use a detailed model of patient health to estimate the parameters of our decision model and implicitly consider the effects of the waiting list through our patient-state-dependent definition of the organ arrival probabilities. We derive structural properties of the model, including a set of intuitive conditions that ensure the existence of control-limit optimal policies. We use clinical data in our computational experiments, which confirm that the optimal policy is typically of control-limit type.