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

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Daqing Gong , Mincong Tang , Gang Xue , Hankun Zhang , Borut Buchmeister ·

YOU? · · 2019 · Open Access · · DOI: https://doi.org/10.1109/access.2019.2945627

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.

Related Topics

Computer Science

Pareto Principle

Transportation Engineering

Algorithm

Particle Swarm Optimization

Concepts

Computer science Swarm intelligence Routing (electronic design automation) Pareto principle Path (computing) Transportation theory Multi-objective optimization Transport engineering Mathematical optimization Operations research Algorithm Particle swarm optimization Engineering Machine learning Mathematics Programming language Computer network

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Type: article
Language: en
Landing Page: https://doi.org/10.1109/access.2019.2945627
PDF: https://ieeexplore.ieee.org/ielx7/6287639/8600701/08859358.pdf
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Cited By: 4
References: 43
Related Works: 10
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DOI: https://doi.org/10.1109/access.2019.2945627

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

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Language: en

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Publication year: 2019

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Publication date: 2019-01-01

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Authors: Daqing Gong, Mincong Tang, Gang Xue, Hankun Zhang, Borut Buchmeister

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Landing page: https://doi.org/10.1109/access.2019.2945627

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Concepts: Computer science, Swarm intelligence, Routing (electronic design automation), Pareto principle, Path (computing), Transportation theory, Multi-objective optimization, Transport engineering, Mathematical optimization, Operations research, Algorithm, Particle swarm optimization, Engineering, Machine learning, Mathematics, Programming language, Computer network

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Cited by: 4

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