A Hybrid Multi-Objective Genetic Heuristic for Large-Scale Vehicle Routing Optimization Problems

International Journal of Computer Science and Technology | Vol. 8, No. 11, October 2006 | pp. 81–88

DOI: 10.46882/2006/IJCST/081126

Research Article

Title: A Hybrid Multi-Objective Genetic Heuristic for Large-Scale Vehicle Routing Optimization Problems

Names of Authors: Amara Chinedu¹, Sofia Alvarez²

Authors’ Affiliations: ¹Department of Computer Science, University of Nigeria, Nsukka, Nigeria; ²Departamento de Ingeniería de Sistemas, Universidad Nacional de Colombia, Bogotá, Colombia

Abstract:
The vehicle routing problem with time windows (VRPTW) is an NP-hard logistic challenge that requires balancing fleet operational costs with strict customer arrival schedules. This paper describes a hybrid multi-objective genetic algorithm (H-MOGA) integrated with a localized variable neighborhood search (VNS) framework. The optimization engine uses a Pareto-dominance classification system to balance total transit distance constraints alongside fleet size requirements. The framework was benchmarked against the standard Solomon 100-customer routing test instances. The empirical results show that the H-MOGA model reduces total transportation distances by 14.8% and reduces fleet requirements by 11.2% compared to standard ant colony heuristics. The optimization process reached steady convergence criteria within an execution time of t = 48.6 seconds, proving that the hybrid approach is highly suitable for integration into automated fleet dispatching platforms.

Keywords: Vehicle Routing Problem, Genetic Algorithms, Multi-Objective Optimization, Pareto Efficiency, Logistics

Manuscript Timeline: Received July 05, 2006; Revised August 18, 2006; Accepted September 12, 2006; Published October 25, 2006

Citation: Chinedu, A., & Alvarez, S. (2006). A Hybrid Multi-Objective Genetic Heuristic for Large-Scale Vehicle Routing Optimization Problems. International Journal of Computer Science and Technology, 8(11), 81–88.

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