Solving the train formation plan network problem of the single-block train and two-block train using a hybrid algorithm of genetic algorithm and tabu search
This paper presents a formulation and solution of the railway freight Train Formation Plan (TFP) network problem in China using both the single-block trains and the two-block trains. Firstly, the single-block TFP model is established under given shipment demands, classification capacity and track quantity at the yards. Then the benefits which can be achieved by replacing single-block trains with two-block trains are systematically analyzed and summarized. The comprehensive optimization model of the train formulation plan using both the single-block trains and two-block trains is established aiming at the minimization of the total car-hour consumption at all yards. A hybrid algorithm of genetic algorithm and tabu search is developed to solve the single-block TFP model and then a greedy algorithm is proposed to replace single-block trains with two-block trains. Finally, the model and the solution approach are tested in an actual 19-yard railway sub-network in China.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/0968090X
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Supplemental Notes:
- Abstract reprinted with permission of Elsevier.
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Authors:
- Xiao, Jie
- Lin, Boliang
- Wang, Jiaxi
- Publication Date: 2018-1
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: pp 124-146
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Serial:
- Transportation Research Part C: Emerging Technologies
- Volume: 86
- Issue Number: 0
- Publisher: Elsevier
- ISSN: 0968-090X
- Serial URL: http://www.sciencedirect.com/science/journal/0968090X
Subject/Index Terms
- TRT Terms: Block control systems; Genetic algorithms; Optimization; Tabu search; Train operations
- Geographic Terms: China
- Subject Areas: Operations and Traffic Management; Planning and Forecasting; Railroads; Vehicles and Equipment;
Filing Info
- Accession Number: 01660565
- Record Type: Publication
- Files: TRIS
- Created Date: Feb 20 2018 9:31AM