An Adaptive Variable Neighborhood Search for a Family of Set Covering Routing Problems with an Application in Disaster Relief Operations

TL;DR

This paper introduces an adaptive variable neighborhood search algorithm for a complex set covering routing problem tailored to disaster relief, combining routing, location, and covering decisions with real-world application.

Contribution

It develops a novel AVNS method that effectively solves a hybrid routing and covering problem inspired by humanitarian logistics, incorporating new mechanisms for covering decisions.

Findings

The AVNS achieves competitive solutions on benchmark instances.

The approach demonstrates practical effectiveness in a real-world disaster case study.

Results provide insights into efficient distribution strategies in disaster response.

Abstract

This paper studies a variant of the Set Covering Routing Problem (SCRP) motivated by post-disaster humanitarian logistics. We consider a hybrid distribution concept in which the majority of transportation is performed by helicopters, while ground transport is limited to the last mile, addressing severe accessibility constraints in disaster-affected regions. The resulting problem integrates landing site location, routing, and covering decisions, incorporating features of the Multi-Vehicle Covering Tour Problem (m-CTP) and the Vehicle Routing with Demand Allocation Problem (VRDAP) in a facility-capacitated, multi-depo setting. Due to the computational complexity of the problem, we develop an Adaptive Variable Neighborhood Search (AVNS) that combines established routing operators with novel mechanisms for covering decisions. The performance of the proposed approach is evaluated on benchmark instances for the related m-CTP and VRDAP problems, demonstrating competitive solution quality compared to problem-specific state-of-the-art approaches. Furthermore, we apply our AVNS to a real-world case study based on the 2024 flash floods in Afghanistan. The results highlight the practical relevance of the proposed framework and provide managerial insights into effective distribution strategies for disaster response operations.