Two-stage distributionally robust optimization for disaster relief logistics under option contract and demand ambiguity

Abstract

Disaster relief logistics (DRL) provides adequate relief supplies to victims of natural disasters (e.g., earthquakes and volcanic eruptions). This study explicitly considers supplier selection and inventory pre-positioning corresponding to static preparedness decisions, and post-disaster procurement and delivery associated with dynamic response decisions in actual DRL operations. To tackle issues triggered by shortage and surplus of multi-class relief resources, a flexible option contract is adopted to purchase relief items from suppliers. To measure the risk of demand ambiguity, a worst-case mean-quantile-deviation criterion is introduced to reflect the decision-maker’s risk-averse attitude. To handle the ambiguity in the probability distribution of demand, a novel two-stage distributionally robust optimization (DRO) model is developed for the addressed DRL problem. The proposed DRO model can be transformed into equivalent mixed-integer linear programs when the ambiguity sets incorporate all distributions within L1-norm and joint L1- and L∞-norms from a nominal (reference) distribution. A computational study of earthquakes in Iran is conducted to illustrate the applicability of the proposed DRO model to real-world problems. The experimental results demonstrate that our proposed DRO model has superior out-of-sample performance and can mitigate the effect of Optimization Bias compared to the traditional stochastic programming model. Some managerial insights regarding the proposed approach are provided based on numerical results.