3066-280XISSN (Online)
Volume 2 , Issue 2 , PP: 48-58, 2024 | Cite this article as | XML | Html | PDF | Full Length Article
Ali Wagdy Mohamed 1 *
Doi: https://doi.org/10.54216/MOR.020205
Vehicle Routing Problem (VRP) variants and modifications are significant problems in combinatorial programming and logistics. They relate to efficient and optimal transport routing for customer demand fulfillment while monitoring operational costs. Traditional methods have been exact algorithms, heuristics, and metaheuristics; however, it has yet to be known to cater to the scalability, computational, efficiency, and adaptability challenges posed by dynamic and large-scale VRPs. Recent advances have shown enormous promise in combining this with learning approaches in hybrid forms: ML and metaheuristic and optimization techniques to overcome them. Such hybrid approaches now promise even better quality solutions, computational speeds, and real-world applicability for two actual ML methods: deep reinforcement learning and meta-learning. The present study surveys the current state of the art of hybrid methods applying to VRPs to find strengths, weaknesses, and directions that future research could intensify to enhance efficiency, scalability, and applicability to transportation and logistics systems.
Hybrid Approaches , Machine Learning , Metaheuristics , Vehicle Routing Problems , Optimization
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DOI: https://doi.org/10.54216/MOR.020205
Received: June 16, 2024 Revised: September 25, 2024 Accepted: December 14, 2024
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