# The Two-Echelon Unmanned Ground Vehicle Routing Problem: Extreme-Weather Goods Distribution as a Case Study

## TL;DR

This paper introduces a new routing problem for unmanned ground vehicles in extreme weather and proposes a superior algorithm to solve it.

## Contribution

A novel hybrid algorithm combining Artificial Bee Colony and Wild Horse Optimizer is proposed for the two-echelon UGV routing problem.

## Key findings

- The HABC-WHO algorithm outperformed GA, DWHO, and DABC-FNS on 43 benchmark instances.
- The algorithm showed strong solving capability and high precision in extreme-weather material distribution scenarios.
- Strategies like large neighborhood search and 2-Opt improved the algorithm's performance.

## Abstract

In extreme weather conditions, the use of unmanned ground vehicles (UGVs) for material distribution enhances safety. We introduce a two-echelon unmanned ground vehicle routing problem (2E-UGVRP) and proposes a hybrid Artificial Bee Colony–Wild Horse Optimizer (HABC-WHO) algorithm to solve it. In this approach, the optimal solution obtained from the Artificial Bee Colony algorithm replaces the worst solution of the Wild Horse Optimizer. To further improve the algorithm’s performance, strategies such as large neighborhood search, two-optimization (2-Opt) operation, and satellite subpath crossover are incorporated. The algorithm’s effectiveness is demonstrated through the solution of 43 benchmark instances, with performance comparisons against a Genetic Algorithm (GA), Discrete Wild Horse Optimizer (DWHO), and Discrete Artificial Bee Colony–Fixed Neighborhood Search (DABC-FNS). The results clearly show the significant superiority of the proposed algorithm. Additionally, the algorithm is applied to material distribution by two-echelon UGVs under extreme weather conditions, yielding promising results. Experimental findings indicate that the algorithm exhibits strong solving capability and high precision.