Optimal Scheduling of Emergency Materials Based on Gray Prediction Model under Uncertain Demand

Abstract

In the context of long-term infectious disease epidemics, guaranteeing the dispatch of materials is important to emergency management. The epidemic situation is constantly changing; it is necessary to build a reasonable mechanism to dispatch emergency resources and materials to meet demand. First, to evaluate the unpredictability of demand during an epidemic, gray prediction is inserted into the proposed model, named the Multi-catalog Schedule Considering Costs and Requirements Under Uncertainty, to meet the material scheduling target. The model uses the gray prediction method based on pre-epidemic data to forecast the possible material demand when the disease appears. With the help of the forecast results, the model is able to achieve cross-regional material scheduling. The key objective of material scheduling is, of course, to reach a balance between the cost and the material support rate. In order to fulfil this important requirement, a multi-objective function, which aims to minimize costs and maximize the material support rate, is constructed. Then, an ant colony algorithm, suitable for time and region problems, is employed to provide a solution to the constructed function. Finally, the validity of the model is verified via a case study. The results show that the model can coordinate and deploy a variety of materials from multiple sources according to changes in an epidemic situation and provide reliable support in decisions regarding the dynamic dispatch of emergency materials during an epidemic period.