OPTIMIZATION OF MEDICAL EQUIPMENT REPLACEMENT STRATEGIES

Abstract

In this dissertation, we use dynamic programming to optimize medical equipment replacement strategies. Two models of medical equipment replacement were developed using multistage deterministic dynamic programming (DDP), and multistage stochastic dynamic programing (SDP) methods. The outcome is an optimal path which shows whether to keep an existing piece of medical equipment (defender) or replace it with a more economical alternative (challenger) for several revenue and depreciation scenarios. For each scenario, we estimated the optimal-sequence benefit as the difference between the highest returns (or the lowest costs) and returns (costs) of keeping the medical equipment until the end of its expected life. In addition, we investigated this benefit for the scenarios of no revenue for the defender and the challenger, the scenario of equal revenues for both, and the scenario of higher revenue for the challenger.