Urban residents' distribution needs such as small batch, personalization, convenience and high efficiency, as well as increasingly congested urban traffic, pose higher challenges to urban distribution efficiency. Logistics distribution is an important link in logistics activities, so it is necessary to optimize the dispatching of logistics distribution vehicles, rationally plan the driving routes of vehicles, and realize the minimum transportation cost. Scientific and reasonable planning of vehicle distribution path can reduce the distribution cost of enterprises, increase vehicle loading, improve customer satisfaction, and increase the profit and industry competitiveness of logistics enterprises. Through the analysis of vehicle scheduling and route selection in the process of logistics distribution, a multi type logistics distribution scheduling mathematical model is established, and an improved Genetic Algorithm (GA) is constructed on this basis, so as to optimize the algorithm efficiency of the scheduling model and reduce the calculation time and algorithm complexity, so as to obtain the optimal solution of the scheduling problem more effectively.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multi-Objective Optimization of Logistics Distribution Route Based on Improved Genetic Algorithm


    Contributors:
    Fu, Fang (author)


    Publication date :

    2022-12-01


    Size :

    328813 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Ship route multi-objective optimization model based on hybrid genetic algorithm

    Cui, Jinlong / Li, Yuankui / Qiao, Chunfu et al. | British Library Conference Proceedings | 2023


    Ship route multi-objective optimization model based on hybrid genetic algorithm

    Cui, Jinlong / Li, Yuankui / Qiao, Chunfu et al. | SPIE | 2023




    Analysis of Multi-objective Multimodal Transport Route Planning Based on Genetic Algorithm

    Kang, Fengwei / Li, Xuemei / Li, Jinyu | Springer Verlag | 2021