Multi-Objective Cluster Intelligent Algorithms for Railway Door-to-Door Transportation Routing Design

• Main Authors: Daqing Gong, Mincong Tang, Gang Xue, Hankun Zhang, Borut Buchmeister
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Intelligent water drops algorithms; multi-objective cluster intelligent algorithms; random frog-leaping algorithm; random multi-neighborhood structure; routing design
• Online Access: https://ieeexplore.ieee.org/document/8859358/

In this study, we aim to develop a system optimization model of Railway Freight Transportation Routing Design (RFTRD) and conduct solution analysis which is based on the improved multi-objective swarm intelligence algorithm. The proposed improved multi-objective swarm intelligence algorithm is applied to solve the combinatorial optimization problem of railway door-to-door freight transportation through design, and provide decision support for railway vehicle door-to-door freight transportation through design. The optimization results shows that, the random multi-neighborhood based multi-objective shuffled frog-leaping algorithm with path relinking (RMN-MOSFLA-PR) can be better applied to solve the combined multi-objective optimization problem, and this proposed improved algorithm can find Pareto frontier through the comparative analysis in the design example of railway door-to-door freight transportation. The frontier can provide support for railway transportation enterprises, arrange the decision-making of the starting and ending stations for multiple shippers, and optimize the use of existing transportation resources, so as to reduce the transportation cost and time of the system.