Application of Multiple-Population Genetic Algorithm in Optimizing the Train-Set Circulation Plan Problem
The train-set circulation plan problem (TCPP) belongs to the rolling stock scheduling (RSS) problem and is similar to the aircraft routing problem (ARP) in airline operations and the vehicle routing problem (VRP) in the logistics field. However, TCPP involves additional complexity due to the mainten...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/3717654 |
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| _version_ | 1850162486386360320 |
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| author | Yu Zhou Leishan Zhou Yun Wang Zhuo Yang Jiawei Wu |
| author_facet | Yu Zhou Leishan Zhou Yun Wang Zhuo Yang Jiawei Wu |
| author_sort | Yu Zhou |
| collection | DOAJ |
| description | The train-set circulation plan problem (TCPP) belongs to the rolling stock scheduling (RSS) problem and is similar to the aircraft routing problem (ARP) in airline operations and the vehicle routing problem (VRP) in the logistics field. However, TCPP involves additional complexity due to the maintenance constraint of train-sets: train-sets must conduct maintenance tasks after running for a certain time and distance. The TCPP is nondeterministic polynomial hard (NP-hard). There is no available algorithm that can obtain the optimal global solution, and many factors such as the utilization mode and the maintenance mode impact the solution of the TCPP. This paper proposes a train-set circulation optimization model to minimize the total connection time and maintenance costs and describes the design of an efficient multiple-population genetic algorithm (MPGA) to solve this model. A realistic high-speed railway (HSR) case is selected to verify our model and algorithm, and, then, a comparison of different algorithms is carried out. Furthermore, a new maintenance mode is proposed, and related implementation requirements are discussed. |
| format | Article |
| id | doaj-art-fdba3e95c6ba4687b386ee1c46f6cf14 |
| institution | OA Journals |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-fdba3e95c6ba4687b386ee1c46f6cf142025-08-20T02:22:33ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/37176543717654Application of Multiple-Population Genetic Algorithm in Optimizing the Train-Set Circulation Plan ProblemYu Zhou0Leishan Zhou1Yun Wang2Zhuo Yang3Jiawei Wu4Department of Transportation Management Engineering, School of Traffic and Transportation, Beijing Jiaotong University, Beijing, ChinaDepartment of Transportation Management Engineering, School of Traffic and Transportation, Beijing Jiaotong University, Beijing, ChinaMinistry of Education (MOE) Key Laboratory for Urban Transportation Complex System Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing, ChinaDepartment of Civil, Environmental, and Infrastructure Engineering, Volgenau School of Engineering, George Mason University, Fairfax, VA, USACenter for Advanced Transportation System Simulation, Department of Civil Environment Construction Engineering, University of Central Florida, Orlando, FL, USAThe train-set circulation plan problem (TCPP) belongs to the rolling stock scheduling (RSS) problem and is similar to the aircraft routing problem (ARP) in airline operations and the vehicle routing problem (VRP) in the logistics field. However, TCPP involves additional complexity due to the maintenance constraint of train-sets: train-sets must conduct maintenance tasks after running for a certain time and distance. The TCPP is nondeterministic polynomial hard (NP-hard). There is no available algorithm that can obtain the optimal global solution, and many factors such as the utilization mode and the maintenance mode impact the solution of the TCPP. This paper proposes a train-set circulation optimization model to minimize the total connection time and maintenance costs and describes the design of an efficient multiple-population genetic algorithm (MPGA) to solve this model. A realistic high-speed railway (HSR) case is selected to verify our model and algorithm, and, then, a comparison of different algorithms is carried out. Furthermore, a new maintenance mode is proposed, and related implementation requirements are discussed.http://dx.doi.org/10.1155/2017/3717654 |
| spellingShingle | Yu Zhou Leishan Zhou Yun Wang Zhuo Yang Jiawei Wu Application of Multiple-Population Genetic Algorithm in Optimizing the Train-Set Circulation Plan Problem Complexity |
| title | Application of Multiple-Population Genetic Algorithm in Optimizing the Train-Set Circulation Plan Problem |
| title_full | Application of Multiple-Population Genetic Algorithm in Optimizing the Train-Set Circulation Plan Problem |
| title_fullStr | Application of Multiple-Population Genetic Algorithm in Optimizing the Train-Set Circulation Plan Problem |
| title_full_unstemmed | Application of Multiple-Population Genetic Algorithm in Optimizing the Train-Set Circulation Plan Problem |
| title_short | Application of Multiple-Population Genetic Algorithm in Optimizing the Train-Set Circulation Plan Problem |
| title_sort | application of multiple population genetic algorithm in optimizing the train set circulation plan problem |
| url | http://dx.doi.org/10.1155/2017/3717654 |
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