Improving the Taiwan military’s disaster relief response to typhoons

Abstract

Taiwan is prone to many natural disasters, especially typhoons. This thesis adapts an existing stochastic prepositioning optimization model to create a tool for Taiwan military disaster recovery planners, and then uses experimental design techniques to systematically explore solutions. The goals are to minimize the expected number of casualties and unmet commodities demands, and to determine the average number of workers deployed in response to each scenario. A design of experiments methodology is applied to the optimization model to reveal how uncertainty in the parameters translates to uncertainty in objective function values. The approach can also identify the parameters with the greatest impact on the objective function, and result in more robust solutions. The analysis demonstrates that it is not always necessary to spend as much money and deploy as many workers as in the past in order to get the best results. Additionally, the approach shows how a decision maker, with more accurate and current weather reports, can refer to the path and intensity of typhoons while making rescue plans. In summary, this research shows that there is great potential for quantitative methods to improve the disaster-relief planning process.