Advancing Hybrid Metaheuristics: Evaluating NN-IHGA for Vehicle Routing Problem With Time Windows

Aysha Sohail, Jumpol Polvichai, Taninnuch Lamjiak and Aye Thant May
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Aysha Sohail: King Mongkut's University of Technology, Thonburi, Thailand
Jumpol Polvichai: King Mongkut's University of Technology, Thonburi, Thailand
Taninnuch Lamjiak: King Mongkut's University of Technology, Thonburi, Thailand
Aye Thant May: King Mongkut's University of Technology, Thonburi, Thailand

International Journal of Applied Metaheuristic Computing (IJAMC), 2025, vol. 16, issue 1, 1-30

Abstract: The vehicle routing problem with time windows is an NP-hard optimization problem vital to logistics and supply chain management. It involves optimizing vehicle routes to serve customers within time windows and capacity limits. This study proposes a hybrid genetic algorithm combining a nearest neighbor-based initialization with advanced mutation operators. The nearest neighbor method ensures high-quality initial solutions by prioritizing proximity and constraints, while multiple mutation operators enhance exploration and exploitation. Tested on the Solomon 100-customer dataset, NN-IHGA outperformed benchmarks, especially on random and mixed datasets, reducing travel costs and vehicle counts. Results highlight NN-IHGA's robustness and adaptability, offering a practical solution for real-world logistics optimization.

Date: 2025
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