Incorporating a Bayesian Network into Two-Stage Stochastic Programming for Blood Bank Location-Inventory Problem in Case of Disasters

Abstract

This paper is concerned with the optimal decisions of blood banks in a blood logistics network (BLN) with the consideration of natural disasters. One of the biggest challenges is how to deal with unexpected disasters. Our idea is to consider the disasters as the natural consequences of interaction among multiple interdependent uncertain factors, such as the locations and the levels of disasters, the number of casualties, and the availabilities of rescue facilities, which work together to influence the rescue effects of the BLN. Thus, taking earthquakes as the example, a Bayesian Network is proposed to describe such uncertainties and interdependences and, then, we incorporate it into a dedicated two-stage multi-period stochastic programming model for the BLN. The planning stage in the model focuses on blood bank location and inventory decisions. The subsequent operational stage is composed of multiple periods, some of which may suffer disasters and initiate corresponding rescue operations. Numerical tests show that the proposed approach can be efficiently applied in blood management under the complicated disaster scenarios.