Since cadaveric liver is scarce and life-saving resource for end-stage liver disease patients, it is critical to design schemes to prioritize the patients for receiving liver transplants. We propose a single-score ranking formula, a weighted combination of four criteria that are commonly used in liver allocation, to assess the priority of waiting candidates when a cadaveric liver is procured. Different weight combinations correspond to alternative prioritization schemes. We implement the formula in an independently developed liver-allocation simulation model and construct the response surfaces for a variety of system outcomes. The derived optimal prioritization schemes based on the response surfaces are superior to the current prioritization scheme with respect to the corresponding outcomes.
Furthermore, we consider multiple system outcomes simultaneously and incorporate the simulation model into a genetic algorithm solution framework to obtain Pareto-optimal policies. To accommodate the stochastic nature of the system, we adapt a ranking and selection procedure to determine the required sample size sequentially for each candidate solution. We also adapt an approach to evaluate the probabilistic dominance of the solution. Through computational experiments, we identify promising algorithmic parameters and obtain a set of Pareto solutions for the multi-objective stochastic optimization problem.
|Advisor:||Wan, Hong, Kong, Nan|
|Commitee:||Liu, Lu, Uhan, Nelson A.|
|School Location:||United States -- Indiana|
|Source:||DAI-B 72/06, Dissertation Abstracts International|
|Subjects:||Biomedical engineering, Industrial engineering|
|Keywords:||Cadaveric donations, Liver disease, Liver transplants, Multiobjective optimization|
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