Abstract：o ensure that emergency strategies are dynamically adjusted according to the evolution of epidemics, we propose a data-driven emergency decision model. In this model, the rolling horizon approach is first applied to divide the entire epidemic period into several discrete decision stages, then the optimal procurement and allocation strategies are determined according to the predicted demand. Also at the end of each stage, the least square method is applied to adjust the epidemic information. To solve the model, we developed a novel algorithm. Based on the COVID-19 data of Hubei in 2020, we obtained an integrated optimization plan for supplier selection, order quantity decision, emergency facility location and resource allocation through this model. In addition, the numerical study verifies the feasibility and effectiveness of our model and algorithm, and draws the conclusion that the data-driven model is better than the traditional prediction-decision models.
项寅. 数据驱动的疫情应急资源采购-分配鲁棒优化模型[J]. 管理学报, 2023, 20(7): 1084-.
XIANG Yin. A Data Driven Robust Optimization Model for Procurement and Allocation of Epidemic Emergency Resource. Chinese Journal of Management, 2023, 20(7): 1084-.
