Marginal-utility-oriented optimization model for collaborative medical supply rebalancing and allocating in response to epidemics

Large-scale epidemics impose significant burdens globally and cause an imbalance of medical supplies among different regions owing to the dissimilarly and unevenly distributed prevalence of the infection. Along with rebalancing the limited medical supplies to meet the demand and supply requirements, ensuring that the supplies are allocated to support the affected regions is also important. Hence, this study focuses on the collaborative medical supply rebalancing and allocating process to balance the demand and supply. The law of diminishing marginal utility is incorporated in this study to quantify the principle of fairness in rebalancing and allocating medical supplies. Accordingly, under uncertainty, a marginal-utility-oriented optimization model is proposed to formulate the rebalancing and allocation of collaborative medical supplies. Because the proposed model is nonlinear and computationally intractable, a linearization approach is adopted to obtain the global optimum that supports decision-making in response to epidemics. Furthermore, a real case study of the United States is implemented, where the sensitivity analysis of critical parameters is conducted on the coronavirus disease 2019. Computational results indicate that additional medical supplies, stock levels, and scenario constructions significantly influence the supply/demand point identification and outgoing/incoming shipments. Moreover, this study not only validates the effectiveness and feasibility of the method but also highlights the importance of incorporating the law of diminishing marginal utility into the collaborative medical supply rebalancing and allocating problem.