An Improved Genetic Algorithm for Vehicle Routing Problem with Hard Time Windows

May, Aye Thant; Jariyavajee, Chattriya; Polvichai, Jumpol

doi:10.1109/icecet52533.2021.9698698

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…The advantages of the genetic algorithm are that the solution is stable, and the computational efficiency is high; however, it suffers from weak local search ability, and it takes a long time to reach the optimal solution upon approaching it. In addition, if the fitness function is not properly selected, the genetic algorithm often converges to local optima, failing to identify the global optimum [174][175][176]. Genetic algorithms can be improved in four aspects: individuals and populations, selection operations, crossover operations, and mutation operations, thereby optimizing the population and addressing the drawbacks of early maturity and local optima; an example of an improved GA is MA [177][178][179].…”

Section: Metaheuristic Algorithmsmentioning

confidence: 99%

A Bibliometric Visualized Analysis and Classification of Vehicle Routing Problem Research

Tang

2023

Sustainability

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The vehicle routing problem (VRP), as a classic combinatorial optimization problem, has always been a hot research topic in operations research. In order to gain a deeper understanding of the VRP problem, this work uses the knowledge graph to comprehensively analyze and summarize the literature related to VRP from 1959 to 2022 in the Web of Science (WoS) database. Firstly, according to the basic statistical information of the literature, the annual publications, the authors, their institutions and countries, the keyword co-occurrence, and the literature co-citation network are analyzed to comprehensively summarize and generalize the research on VRP and predict its future development trend. The results show that, in the past 60 years, there have been abundant changes in the research on VRP. The United States and China have made the most important contributions in the field of VRP. According to the WoS literature retrieval results and classification methods, the VRP models and their solutions are comprehensively classified, and the model solving algorithms are divided into exact algorithms, heuristic algorithms, metaheuristic algorithms, hyper-heuristic algorithms, machine learning, etc. The results show that the development of information computing technology plays an important role in research on the VRP problem, and dynamic VRP, hyper-heuristic algorithms, deep reinforcement learning, etc. are the future development directions of the VRP model and its optimization. The results of this research can provide help and guidance for beginners and scholars outside the industry to comprehensively understand the development and research hotspots of VRP.

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