A Robust Optimization Framework for Two-Echelon Vehicle and UAV Routing for Post-Disaster Humanitarian Logistics Operations

TL;DR

This paper introduces a robust optimization framework for two-echelon vehicle and UAV routing in post-disaster humanitarian logistics, effectively managing demand uncertainty and network failures to improve aid delivery.

Contribution

It develops a novel two-stage robust optimization model with a column-and-constraint generation approach for routing trucks and drones under demand uncertainty.

Findings

Successfully models demand uncertainty in disaster scenarios

Efficiently generates worst-case demand scenarios and routes

Improves aid delivery robustness in simulated post-hurricane context

Abstract

Providing first aid and other supplies (e.g., epi-pens, medical supplies, dry food, water) during and after a disaster is always challenging. The complexity of these operations increases when the transportation, power, and communications networks fail, leaving people stranded and unable to communicate their locations and needs. The advent of emerging technologies like uncrewed autonomous vehicles can help humanitarian logistics providers reach otherwise stranded populations after transportation network failures. However, due to the failures in telecommunication infrastructure, demand for emergency aid can become uncertain. To address the challenges of delivering emergency aid to trapped populations with failing infrastructure networks, we propose a novel robust computational framework for a two-echelon vehicle routing problem that uses uncrewed autonomous vehicles, or drones, for the deliveries. We formulate the problem as a two-stage robust optimization model to handle demand uncertainty. Then, we propose a column-and-constraint generation approach for worst-case demand scenario generation for a given set of truck and drone routes. Moreover, we develop a decomposition scheme inspired by the column generation approach to heuristically generate drone routes for a set of demand scenarios. Finally, we combine the heuristic decomposition scheme within the column-andconstraint generation approach to determine robust routes for both trucks and drones, the time that affected communities are served, and the quantities of aid materials delivered. To validate our proposed computational framework, we use a simulated dataset that aims to recreate emergency aid requests in different areas of Puerto Rico after Hurricane Maria in 2017.