Dynamic optimization model for allocating medical resources in epidemic controlling
PublisherSchool of Industrial and Aeronautic Engineering of Terrassa (ETSEIAT). Universitat Politècnica de Catalunya (UPC)
Rights accessOpen Access
Purpose: The model proposed in this paper addresses a dynamic optimization model for allocating medical resources in epidemic controlling. Design/methodology/approach: In this work, a three-level and dynamic linear programming model for allocating medical resources based on epidemic diffusion model is proposed. The epidemic diffusion model is used to construct the forecasting mechanism for dynamic demand of medical resources. Heuristic algorithm coupled with MTLAB mathematical programming solver is adopted to solve the model. A numerical example is presented for testing the model’s practical applicability. Findings: The main contribution of the present study is that a discrete time-space network model to study the medical resources allocation problem when an epidemic outbreak is formulated. It takes consideration of the time evolution and dynamic nature of the demand, which is different from most existing researches on medical resources allocation. Practical implications: In our model, the medicine logistics operation problem has been decomposed into several mutually correlated sub-problems, and then be solved systematically in the same decision scheme. Thus, the result will be much more suitable for real operations. Originality/value: In our model, the rationale that the medical resources allocated in early periods will take effect in subduing the spread of the epidemic spread and thus impact the demand in later periods has been for the first time incorporated. A win-win emergency rescue effect is achieved by the integrated and dynamic optimization model. The total rescue cost is controlled effectively, and meanwhile, inventory level in each urban health departments is restored and raised gradually.
CitationLiu, Ming; Liang, Jiang. Dynamic optimization model for allocating medical resources in epidemic controlling. "Journal of Industrial Engineering and Management", Abril 2013, vol. 6, núm. 1, p. 73-88.