Distribution Path Optimization for Intelligent Logistics Vehicles of Urban Rail Transportation Using VRP Optimization Model

Path optimization of logistics distribution vehicles is researched to improve the efficiency of intelligent logistics systems. The logistics distribution system based on urban rail transportation is analyzed. A mathematical model is constructed for the Vehicle Routing Problem (VRP) of multiple distr...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2022-02, Vol.23 (2), p.1661-1669
Main Authors: Leng, Kaijun, Li, Shanghong
Format: Article
Language:English
Subjects:
Algorithms
Antibodies
Distribution centres
distribution vehicle
immune algorithm particle swarm optimization (IAPSO)
intelligent logistics system
Logistics
Mathematical model
Mathematical models
Optimization
Optimization models
Particle swarm optimization
Rail transportation
Rails
Route planning
Routing
Transportation
Urban rail
urban rail transportation
Vehicle routing
Vehicle routing problem
Vehicles
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Summary:Path optimization of logistics distribution vehicles is researched to improve the efficiency of intelligent logistics systems. The logistics distribution system based on urban rail transportation is analyzed. A mathematical model is constructed for the Vehicle Routing Problem (VRP) of multiple distribution centers. A novel Concentration-Immune Algorithm Particle Swarm Optimization (C-IAPSO) is proposed based on the respective advantages of C-IA and PSO in vehicle path optimization combining the concept of antibody concentration. C-IAPSO first calculates the concentration selection probability of particles in the swarm and updates the immune memory bank as per the optimal particle retention strategy to ensure the diversity of the antibody swarm. To assess the performance of C-IAPSO, seven standard test functions are selected for comparison experiments; results prove that it can provide the fastest convergence rate. On unimodal functions Sphere and Quadric, the accuracy of C-IAPSO gets improved significantly. The specific conditions of the distribution center and the demand spots are analyzed; the vehicle travels 508.40 km in total under the optimal distribution path calculated by C-IAPSO, which is a notable decrease compared with the 600.40 km of Adaptive PSO (APSO). To sum up, applying C-IAPSO to vehicle path optimization of intelligent logistics systems can improve transportation efficiency and reduce transportation costs.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2021.3105105