Multi-Objective Cluster Intelligent Algorithms for Railway Door-to-Door Transportation Routing Design
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 appli...
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doaj-920cb810a361407893328e868626fec52021-03-29T23:56:30ZengIEEEIEEE Access2169-35362019-01-01714670214672310.1109/ACCESS.2019.29456278859358Multi-Objective Cluster Intelligent Algorithms for Railway Door-to-Door Transportation Routing DesignDaqing Gong0Mincong Tang1https://orcid.org/0000-0003-1367-4242Gang Xue2https://orcid.org/0000-0002-6566-8162Hankun Zhang3Borut Buchmeister4School of Economics and Management, Beijing Jiaotong University, Beijing, ChinaSchool of Economics and Management, Beijing Jiaotong University, Beijing, ChinaSchool of Economics and Management, Beijing Jiaotong University, Beijing, ChinaSchool of Economics and Management, Beijing Jiaotong University, Beijing, ChinaFaculty of Mechanical Engineering, University of Maribor, Maribor, SloveniaIn 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.https://ieeexplore.ieee.org/document/8859358/Intelligent water drops algorithmsmulti-objective cluster intelligent algorithmsrandom frog-leaping algorithmrandom multi-neighborhood structurerouting design |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Daqing Gong Mincong Tang Gang Xue Hankun Zhang Borut Buchmeister |
spellingShingle |
Daqing Gong Mincong Tang Gang Xue Hankun Zhang Borut Buchmeister Multi-Objective Cluster Intelligent Algorithms for Railway Door-to-Door Transportation Routing Design IEEE Access Intelligent water drops algorithms multi-objective cluster intelligent algorithms random frog-leaping algorithm random multi-neighborhood structure routing design |
author_facet |
Daqing Gong Mincong Tang Gang Xue Hankun Zhang Borut Buchmeister |
author_sort |
Daqing Gong |
title |
Multi-Objective Cluster Intelligent Algorithms for Railway Door-to-Door Transportation Routing Design |
title_short |
Multi-Objective Cluster Intelligent Algorithms for Railway Door-to-Door Transportation Routing Design |
title_full |
Multi-Objective Cluster Intelligent Algorithms for Railway Door-to-Door Transportation Routing Design |
title_fullStr |
Multi-Objective Cluster Intelligent Algorithms for Railway Door-to-Door Transportation Routing Design |
title_full_unstemmed |
Multi-Objective Cluster Intelligent Algorithms for Railway Door-to-Door Transportation Routing Design |
title_sort |
multi-objective cluster intelligent algorithms for railway door-to-door transportation routing design |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
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. |
topic |
Intelligent water drops algorithms multi-objective cluster intelligent algorithms random frog-leaping algorithm random multi-neighborhood structure routing design |
url |
https://ieeexplore.ieee.org/document/8859358/ |
work_keys_str_mv |
AT daqinggong multiobjectiveclusterintelligentalgorithmsforrailwaydoortodoortransportationroutingdesign AT mincongtang multiobjectiveclusterintelligentalgorithmsforrailwaydoortodoortransportationroutingdesign AT gangxue multiobjectiveclusterintelligentalgorithmsforrailwaydoortodoortransportationroutingdesign AT hankunzhang multiobjectiveclusterintelligentalgorithmsforrailwaydoortodoortransportationroutingdesign AT borutbuchmeister multiobjectiveclusterintelligentalgorithmsforrailwaydoortodoortransportationroutingdesign |
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1724188895279579136 |