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Multi-haul quasi network flow model for vertical alignment optimization Beiranvand, Vahid
Abstract
The vertical alignment optimization problem for road design aims to generate a vertical alignment of a new road with a minimum cost, while satisfying safety and design constraints. We present a new model called multi-haul quasi network flow (MH-QNF) for vertical alignment optimization that improves the accuracy and reliability of previous mixed integer linear programming models. We evaluate the performance of the new model compared to two state-of-the-art models in the field: the complete transportation graph (CTG) and quasi network flow (QNF) models. The numerical results show that, within 1% relative error, the proposed model is robust and solves more than 93% of test problems. Whereas, the CTG only solves about 82% of test problems and QNF fails to solve any problem within 1% relative error. Moreover, in terms of computational time, on average the MH-QNF model solves the problems approximately 8 times faster than the CTG model.
Item Metadata
Title |
Multi-haul quasi network flow model for vertical alignment optimization
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Creator | |
Publisher |
University of British Columbia
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Date Issued |
2016
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Description |
The vertical alignment optimization problem for road design aims to generate a vertical alignment of a new road with a minimum cost, while satisfying safety and design constraints. We present a new model called multi-haul quasi network flow (MH-QNF) for vertical alignment optimization that improves the accuracy and reliability of previous mixed integer linear programming models. We evaluate the performance of the new model compared to two state-of-the-art models in the field: the complete transportation graph (CTG) and quasi network flow (QNF) models. The numerical results show that, within 1% relative error, the proposed model is robust and solves more than 93% of test problems. Whereas, the CTG only solves about 82% of test problems and QNF fails to solve any problem within 1% relative error. Moreover, in terms of computational time, on average the MH-QNF model solves the problems approximately 8 times faster than the CTG model.
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Genre | |
Type | |
Language |
eng
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Date Available |
2016-07-15
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Provider |
Vancouver : University of British Columbia Library
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Rights |
Attribution-NonCommercial-NoDerivs 2.5 Canada
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DOI |
10.14288/1.0228128
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URI | |
Degree | |
Program | |
Affiliation | |
Degree Grantor |
University of British Columbia
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Graduation Date |
2016-02
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Campus | |
Scholarly Level |
Graduate
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DSpace
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Rights
Attribution-NonCommercial-NoDerivs 2.5 Canada