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

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.