A Holistic Genetic Algorithm Framework for the 3L-CVRP: Integrating Route Optimization with 3D Load Visualization

Abstract

The Capacitated Vehicle Routing Problem with Three-Dimensional Loading Constraints (3L-CVRP) is a challenging NP-hard optimization problem in logistics. While existing literature often focuses on complex hybrid algorithms, this research addresses a gap by developing a holistic and reproducible standard Genetic Algorithm (GA) framework. Its primary objective is to validate the GA's capability to find feasible solutions and to present a comprehensive analysis that integrates 2D route visualizations and 3D load layouts as proof of practical feasibility. Our methodology employs a Python-based GA with a penalty-based fitness function to handle routing and loading constraints simultaneously. Tested on 10 benchmark instances, the algorithm consistently finds feasible solutions with an average gap of 8.24% from the Best Known Solution (BKS). The novelty of this research is the presentation of a transparent GA framework that utilizes 3D load visualization as empirical proof of solution feasibility, thereby establishing a solid baseline for future research.