Determining the optimal decision time of relief allocation in response to disaster via relief demand updates

Relief allocation has recently attracted increased interest in research. Most studies in the literature have not focused on the trade-off between demand forecast accuracy and relief allocation efficiency. Thus, we employ the Bayesian updating framework, which allows the decision-maker to postpone his decision until sufficient accurate demand information is available. Different from the previous studies, we propose a novel method of relief demand updating, and devise a lost function as a function of time delay. Then, we develop a relief allocation model as an optimal stopping problem, to determine the optimal delay in response to typhoon disaster. Finally, the proposed model is applied to a case of post-disaster rescue. The computational results indicate the potential advantages of the model.