Ordered Genetic Algorithm for Entrance Dependent Vehicle Routing Problem in Farms

2025-02-25

AI Summary

This paper addresses the Entry-Dependent Vehicle Routing Problem (EDVRP) in farm environments, where entrance geometry and passage constraints critically influence path optimization. We propose the Ordered Genetic Algorithm (OGA) framework, featuring an entrance-aware encoding scheme and novel crossover/mutation operators that preserve sequence ordering—thereby explicitly modeling spatial dependencies among entrances within the VRP solution process for the first time. Experiments on multiple synthetic farm instances demonstrate that OGA reduces average path cost by 12.7% compared to conventional unordered genetic algorithms and by 34.2% over random strategies. Ablation studies confirm the essential roles of both the entrance topology encoding and the order-preserving operators. This work provides a novel, interpretable, and robust methodology for customized VRPs under strong spatial constraints.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

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.

Problem

• Addresses Vehicle Routing Problem influenced by city entrances
• Proposes Ordered Genetic Algorithm for farm-specific EDVRP
• Validates algorithm effectiveness through extensive experimental cases

Innovation

• Ordered Genetic Algorithm
• Entrance Dependent VRP
• Novel Operators Enhancement