Ordered Genetic Algorithm for Entrance Dependent Vehicle Routing Problem in Farms

TL;DR

This paper introduces the Entrance Dependent Vehicle Routing Problem (EDVRP) in farms and proposes an Ordered Genetic Algorithm (OGA) with novel operators, demonstrating improved performance over baseline methods through extensive experiments.

Contribution

The paper formulates EDVRP considering entrance effects and develops an Ordered Genetic Algorithm with new operators to solve it effectively.

Findings

OGA outperforms random and unordered genetic algorithms.

Novel operators significantly improve algorithm performance.

Experimental results validate the effectiveness of the proposed method.

Abstract

Vehicle Routing Problems (VRP) are widely studied issues that play important roles in many production scenarios. We have noticed that in some practical scenarios of VRP, the size of cities and their entrances can significantly influence the optimization process. To address this, we have constructed the Entrance Dependent VRP (EDVRP) to describe such problems. We provide a mathematical formulation for the EDVRP in farms and propose an Ordered Genetic Algorithm (OGA) to solve it. The effectiveness of OGA is demonstrated through our experiments, which involve a multitude of randomly generated cases. The results indicate that OGA offers certain advantages compared to a random strategy baseline and a genetic algorithm without ordering. Furthermore, the novel operators introduced in this paper have been validated through ablation experiments, proving their effectiveness in enhancing the performance of the algorithm.