@article{ref1,
title="Two-stage distributionally robust optimization for disaster relief logistics under option contract and demand ambiguity",
journal="Transportation research part E: logistics and transportation review",
year="2023",
author="Wang, Duo and Yang, Kai and Yang, Lixing and Dong, Jianjun",
volume="170",
number="",
pages="e103025-e103025",
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.<p /> <p>Language: en</p>",
language="en",
issn="1366-5545",
doi="10.1016/j.tre.2023.103025",
url="http://dx.doi.org/10.1016/j.tre.2023.103025"
}