Online Stochastic Optimization in the Large: Application to Kidney Exchange
نویسندگان
چکیده
Kidneys are the most prevalent organ transplants, but demand dwarfs supply. Kidney exchanges enable willing but incompatible donor-patient pairs to swap donors. These swaps can include cycles longer than two pairs as well, and chains triggered by altruistic donors. Current kidney exchanges address clearing (deciding who gets kidneys from whom) as an offline problem: they optimize the current batch. In reality, clearing is an online problem where patient-donor pairs and altruistic donors appear and expire over time. In this paper, we study trajectory-based online stochastic optimization algorithms (which use a recent scalable optimal offline solver as a subroutine) for this. We identify tradeoffs in these algorithms between different parameters. We also uncover the need to set the batch size that the algorithms consider an atomic unit. We develop an experimental methodology for setting these parameters, and conduct experiments on real and generated data. We adapt the REGRETS algorithm of Bent and van Hentenryck for the setting. We then develop a better algorithm. We also show that the AMSAA algorithm of Mercier and van Hentenryck does not scale to the nationwide level. Our best online algorithm saves significantly more lives than the current practice of solving each batch separately.
منابع مشابه
Using Genetic Algorithm in Solving Stochastic Programming for Multi-Objective Portfolio Selection in Tehran Stock Exchange
Investor decision making has always been affected by two factors: risk and returns. Considering risk, the investor expects an acceptable return on the investment decision horizon. Accordingly, defining goals and constraints for each investor can have unique prioritization. This paper develops several approaches to multi criteria portfolio optimization. The maximization of stock returns, the pow...
متن کاملOperating Room Scheduling in Teaching Hospitals: A Novel Stochastic Optimization Model
Background and Objectives: Operating room (OR) scheduling is key to optimal operating room productivity. The significant uncertainty associated with surgery duration renders scheduling of surgical operation a challenging task. This paper proposes a novel computational stochastic model to optimize scheduling of surgeries with uncertain durations. The model considers various surgical operation co...
متن کاملApplication of Stochastic Optimal Control, Game Theory and Information Fusion for Cyber Defense Modelling
The present paper addresses an effective cyber defense model by applying information fusion based game theoretical approaches. In the present paper, we are trying to improve previous models by applying stochastic optimal control and robust optimization techniques. Jump processes are applied to model different and complex situations in cyber games. Applying jump processes we propose some m...
متن کاملNon-linear stochastic inversion of regional Bouguer anomalies by means of Particle Swarm Optimization: Application to the Zagros Mountains
Estimating the lateral depth variations of the Earth’s crust from gravity data is a non-linear ill-posed problem. The ill-posedness of the problem is due to the presence of noise in the data, and also the non-uniqueness of the problem. Particle Swarm Optimization (PSO) is a stochastic population-based optimizer, originally inspired by the social behavior of fish schools and bird flocks. PSO is ...
متن کاملSolving single facility goal Weber location problem using stochastic optimization methods
Location theory is one of the most important topics in optimization and operations research. In location problems, the goal is to find the location of one or more facilities in a way such that some criteria such as transportation costs, customer traveling distance, total service time, and cost of servicing are optimized. In this paper, we investigate the goal Weber location problem in which the...
متن کامل