垂直线形多点准网络流模型Optimization
Multi-Haul Quasi Network Flow Model for Vertical Alignment Optimization
摘要：
道路竖向线形最优化问题的目标是在满足安全性和设计约束的前提下，以最小的成本生成一条新道路的竖向线形。本文提出了一种新的多程准网络流（MH-QNF）模型，改进了以往混合整数线性规划模型的精度和可靠性。我们评估的新模型的性能相比，两个国家的最先进的模型在该领域：完全运输图（CTG）和准网络流（QNF）模型。数值结果表明，在1%的相对误差内，该模型是鲁棒的，并解决了超过93%的测试问题相比，82%的CTG和没有QNF。此外，MH-QNF模型解决问题的速度比CTG模型快约8倍。
英文摘要：
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.
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