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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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2017/6173795 |
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| _version_ | 1832558839232200704 |
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| author | Kemal Keskin Abdurrahman Karamancioglu |
| author_facet | Kemal Keskin Abdurrahman Karamancioglu |
| author_sort | Kemal Keskin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-072cd13a57714786b5d8b946a35178e2 |
| institution | Kabale University |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-072cd13a57714786b5d8b946a35178e22025-02-03T01:31:25ZengWileyJournal of Advanced Transportation0197-67292042-31952017-01-01201710.1155/2017/61737956173795Energy-Efficient Train Operation Using Nature-Inspired AlgorithmsKemal Keskin0Abdurrahman Karamancioglu1Department of Electrical and Electronics Engineering, Eskisehir Osmangazi University, 26480 Eskisehir, TurkeyDepartment of Electrical and Electronics Engineering, Eskisehir Osmangazi University, 26480 Eskisehir, TurkeyA 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.http://dx.doi.org/10.1155/2017/6173795 |
| spellingShingle | Kemal Keskin Abdurrahman Karamancioglu Energy-Efficient Train Operation Using Nature-Inspired Algorithms Journal of Advanced Transportation |
| title | Energy-Efficient Train Operation Using Nature-Inspired Algorithms |
| title_full | Energy-Efficient Train Operation Using Nature-Inspired Algorithms |
| title_fullStr | Energy-Efficient Train Operation Using Nature-Inspired Algorithms |
| title_full_unstemmed | Energy-Efficient Train Operation Using Nature-Inspired Algorithms |
| title_short | Energy-Efficient Train Operation Using Nature-Inspired Algorithms |
| title_sort | energy efficient train operation using nature inspired algorithms |
| url | http://dx.doi.org/10.1155/2017/6173795 |
| work_keys_str_mv | AT kemalkeskin energyefficienttrainoperationusingnatureinspiredalgorithms AT abdurrahmankaramancioglu energyefficienttrainoperationusingnatureinspiredalgorithms |