Multi-Haul Quasi Network Flow Model for Vertical Alignment Optimization
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 the quasi network flow (QNF) models. The numerical results show that, within a 1% relative error, the proposed model is robust and solves more than 93% of test problems compared to 82% for the CTG and none for the QNF. Moreover, the MH-QNF model solves the problems approximately 8 times faster than the CTG model.
Keywords
Cite
@article{arxiv.1701.01768,
title = {Multi-Haul Quasi Network Flow Model for Vertical Alignment Optimization},
author = {Vahid Beiranvand and Warren Hare and Yves Lucet and Shahadat Hossain},
journal= {arXiv preprint arXiv:1701.01768},
year = {2017}
}
Comments
23 pages, 4 figures The Version of Record of this manuscript has been published and is available in Engineering Optimization (GENO); Estimated publication date: Jan 12, 2017 (online); http://dx.doi.org/10.1080/0305215X.2016.1271880