ENHANCING PPE SUPPLY CHAIN RESILIENCE DURING THE COVID-19 PANDEMIC USING MULTI-OBJECTIVE OPTIMIZATION UNDER UNCERTAINTY

Abstract

This study presents a multi-period bi-objective mixed-integer supply planning model and applies it to a case study inspired by the operational challenges of a Canadian provincial healthcare provider's PPE supply chain during the COVID-19 pandemic. Uncertainties in the supply, price, and demand of PPE are considered. The cost minimization objective function is formulated using stochastic, robust, and distributionally robust optimization. The service-level objective function follows minimax robustness by minimizing the maximum shortage of any product in any period and scenario. The epsilon-constraint method is used to generate Pareto-optimal solutions and analyze the trade-off between the two competing objectives. Numerical experiments analyze model behaviour and the efficacy of emergency inventory and increased inventory levels as risk mitigation strategies. The distributionally robust optimization model is recommended with its ambiguity set size determined by the decision makers' relative preferences for average cost performance, worst-case cost performance, or cost variance.