Energy-Efficient Train Operation Using Nature-Inspired Algorithms

A train operation optimization by minimizing its traction energy subject to various constraints is carried out using nature-inspired evolutionary algorithms. The optimization process results in switching points that initiate cruising and coasting phases of the driving. Due to nonlinear optimization...

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Published inJournal of advanced transportation Vol. 2017; no. 2017; pp. 1 - 12
Main Authors Keskin, Kemal, Karamancioglu, Abdurrahman
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2017
Hindawi
John Wiley & Sons, Inc
Wiley
Subjects
Algorithms
Case studies
Coasting
Computer simulation
Driving ability
Dynamic programming
Energy consumption
Energy efficiency
Energy use
Evolutionary algorithms
Genetic algorithms
Heuristic
Infrastructure
Integer programming
Light rail transit
Management
Mathematical optimization
Methods
Objective function
Optimization
Railroads
Simulated annealing
Studies
Trains
Transportation
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Abstract A train operation optimization by minimizing its traction energy subject to various constraints is carried out using nature-inspired evolutionary algorithms. The optimization process results in switching points that initiate cruising and coasting phases of the driving. Due to nonlinear optimization formulation of the problem, nature-inspired evolutionary search methods, Genetic Simulated Annealing, Firefly, and Big Bang-Big Crunch algorithms were employed in this study. As a case study a real-like train and test track from a part of Eskisehir light rail network were modeled. Speed limitations, various track alignments, maximum allowable trip time, and changes in train mass were considered, and punctuality was put into objective function as a penalty factor. Results have shown that all three evolutionary methods generated effective and consistent solutions. However, it has also been shown that each one has different accuracy and convergence characteristics.
AbstractList A train operation optimization by minimizing its traction energy subject to various constraints is carried out using nature-inspired evolutionary algorithms. The optimization process results in switching points that initiate cruising and coasting phases of the driving. Due to nonlinear optimization formulation of the problem, nature-inspired evolutionary search methods, Genetic Simulated Annealing, Firefly, and Big Bang-Big Crunch algorithms were employed in this study. As a case study a real-like train and test track from a part of Eskisehir light rail network were modeled. Speed limitations, various track alignments, maximum allowable trip time, and changes in train mass were considered, and punctuality was put into objective function as a penalty factor. Results have shown that all three evolutionary methods generated effective and consistent solutions. However, it has also been shown that each one has different accuracy and convergence characteristics.
A train operation optimization by minimizing its traction energy subject to various constraints is carried out using nature-inspired evolutionary algorithms. The optimization process results in switching points that initiate cruising and coasting phases of the driving. Due to nonlinear optimization formulation of the problem, nature-inspired evolutionary search methods, Genetic Simulated Annealing, Firefly, and Big Bang-Big Crunch algorithms were employed in this study. As a case study a real-like train and test track from a part of Eskisehir light rail network were mo deled. Speed limitations, various track alignments, maximum allowable trip time, and changes in train mass were considered, and punctuality was put into objective function as a penalty factor. Results have shown that all three evolutionary methods generated effective and consistent solutions. However, it has also been shown that each one has different accuracy and convergence characteristics.
Audience Academic
Author Karamancioglu, Abdurrahman
Keskin, Kemal
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ContentType Journal Article
Copyright Copyright © 2017 Kemal Keskin and Abdurrahman Karamancioglu.
COPYRIGHT 2017 John Wiley & Sons, Inc.
Copyright © 2017 Kemal Keskin and Abdurrahman Karamancioglu. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: Copyright © 2017 Kemal Keskin and Abdurrahman Karamancioglu.
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– notice: Copyright © 2017 Kemal Keskin and Abdurrahman Karamancioglu. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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SubjectTerms Algorithms
Case studies
Coasting
Computer simulation
Driving ability
Dynamic programming
Energy consumption
Energy efficiency
Energy use
Evolutionary algorithms
Genetic algorithms
Heuristic
Infrastructure
Integer programming
Light rail transit
Management
Mathematical optimization
Methods
Objective function
Optimization
Railroads
Simulated annealing
Studies
Trains
Transportation
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Title Energy-Efficient Train Operation Using Nature-Inspired Algorithms
URI https://search.emarefa.net/detail/BIM-1170841
https://dx.doi.org/10.1155/2017/6173795
https://www.proquest.com/docview/2407638520
https://doaj.org/article/072cd13a57714786b5d8b946a35178e2
Volume 2017
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