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 in | Journal of advanced transportation Vol. 2017; no. 2017; pp. 1 - 12 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Cairo, Egypt
Hindawi Publishing Corporation
01.01.2017
Hindawi John Wiley & Sons, Inc Wiley |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0197-6729 2042-3195 2042-3195 |
| DOI | 10.1155/2017/6173795 |
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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. |
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| 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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| 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. |
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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 |
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