Advances in Production Engineering & Management
Volume 20 | Number 3 | September 2025 | pp 299–308
https://doi.org/10.14743/apem2025.3.541
Improving AGV path planning efficiency using Genetic Algorithms with Hamming distance-based initialization
Breznikar, Ž.; Gotlih, J.; Artič, Ž.; Brezocnik, M.
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A B S T R A C T
This paper presents a Genetic Algorithm (GA) framework for warehouse navigation as a Travelling Salesman Problem (TSP) variant for Automated Guided Vehicles (AGVs). The warehouse layout is represented as a graph, where pick-up locations serve as terminal nodes. A distance matrix, computed via Breadth-First Search (BFS) enables efficient route evaluation. To promote diversity in the initial population, a Hamming distance-based vectorized initialization strategy is employed, ensuring that the chromosomes are maximally distinct. The GA balances exploration and exploitation by dynamically adjusting the fitness function. Early generations emphasize diversity, while later ones focus on solution refinement, improving convergence and avoiding premature stagnation. Our key contribution demonstrates that the Hamming distance-based approach achieves comparable or better results with significantly fewer chromosomes. This reduces computational cost and runtime, making the method well-suited for real-time AGV routing in warehouses. The framework is adaptable to structured environments and shows strong potential for integration into real-world logistics and robotics applications. Future work will focus on optimizing the algorithm and integrating it into the ROS 2 environment. The simplified version of the algorithm can be accessed at: https://github.com/IntoTheVoid-61/Warehouse-Pathfinder.
A R T I C L E I N F O
Keywords • Automated guided vehicles (AGV); Warehouse routing; Genetic algorithms (GA); Combinatorial optimization; Hamming distance initialization; Robot operating system 2 (ROS 2)
Corresponding author • Breznikar, Ž.
Article history • Received 6 July 2025, Revised 24 October 2025, Accepted 27 October 2025
Published on-line • 28 October 2025
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