A Summary and Discussion on the Current State of CVRP Research

The Vehicle Routing Problem (VRP)is a fundamental combinatorial optimization problem in the field of transportation. Among its important variants, the Capacitated Vehicle Routing Problem (CVRP) focuses on optimizing delivery routes under vehicle capacity constraints to minimize transportation costs and enhance service quality. This paper systematically reviews the current state of CVRP research, encompassing the applications of exact algorithms and approximate algorithms, including genetic algorithms, ant colony optimization, and simulated annealing. It also explores improvements to the parent genetic algorithm (PGA) in CVRP, such as polymorphic mutation strategies and hybrid jump strategies. Finally, the paper addresses the challenges faced by CVRP in practical applications and proposes future research directions, including algorithm adaptability, exploration of hybrid algorithms, and efficient processing in big data environments. This study aims to provide a comprehensive reference framework for researchers in the CVRP field, promoting the further development and application of related technologies.

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Publication Details

Journal
Applied and Computational Engineering
Published
2024-12-19
DOI
https://doi.org/10.54254/2755-2721/2025.18473
Primary Topic
Educational Reforms and Innovations
Type
article
Field-Weighted Citation Impact
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article

A Summary and Discussion on the Current State of CVRP Research

Yi Bao
Applied and Computational Engineering
Educational Reforms and Innovations
article

A Summary and Discussion on the Current State of CVRP Research

Yi Bao
article en

Abstract

The Vehicle Routing Problem (VRP)is a fundamental combinatorial optimization problem in the field of transportation. Among its important variants, the Capacitated Vehicle Routing Problem (CVRP) focuses on optimizing delivery routes under vehicle capacity constraints to minimize transportation costs and enhance service quality. This paper systematically reviews the current state of CVRP research, encompassing the applications of exact algorithms and approximate algorithms, including genetic algorithms, ant colony optimization, and simulated annealing. It also explores improvements to the parent genetic algorithm (PGA) in CVRP, such as polymorphic mutation strategies and hybrid jump strategies. Finally, the paper addresses the challenges faced by CVRP in practical applications and proposes future research directions, including algorithm adaptability, exploration of hybrid algorithms, and efficient processing in big data environments. This study aims to provide a comprehensive reference framework for researchers in the CVRP field, promoting the further development and application of related technologies.

Applied and Computational EngineeringVol. 115(1)
Southeast University (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 67%
Educational Reforms and Innovations
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