Biobjective optimization for railway alignment fine‐grained designs with parallel existing railways
Yan Gao, Tianlong Zhang, Caiyiyi Zhu, Shusheng Yang, Paul Schonfeld, Kai Zou, Jialing Zhang, Ying Zhu, Ping Wang, Qing He
Abstract
Urban high-speed railway construction is complex due to limited land resources, high population density, and potential construction risks, especially when new tracks are parallelly aligned to operational railways. Addressing a gap in current literature on fine optimization of manual alignment in such scenarios, this paper introduces a biobjective approximate fine-grained optimization model for railway alignments (BA-FORA). Utilizing an approximate dynamic programming (ADP) method, BA-FORA effectively searches the feasible region to approach a global optimum, overcoming the dimensionality challenges inherent in standard dynamic programming (DP). This paper presents a biobjective optimization framework that takes into account both construction cost and construction risk adjacent to existing operating railways (CRAEOR), offering a method for the fine-grained design of new railways adjacent to existing railways. Finally, the proposed BA-FORA framework is applied to practical cases, demonstrating its superior optimization performance. The findings indicate that the BA-FORA model can autonomously investigate and enhance railway alignment. It generates cost-effective and low-risk solutions exceeding manual efforts, ensuring alignment constraint compliance.