铁道运输与经济2026，Vol.48Issue(6)：71-81,11.DOI:10.16668/j.cnki.issn.1003-1421.20251030002
基于两阶段鲁棒优化的铁路隧道应急资源调度研究
Research on Emergency Resource Scheduling of Railway Tunnels Based on Two-Stage Robust Optimization
摘要
Abstract
Railway tunnels have a closed environment and complex geological conditions.Emergencies such as fires and collapses are prone to triggering cascading disasters,posing severe challenges to the robustness of emergency resource scheduling.To address the shortcomings of existing rescue plans in addressing demand fluctuation and transportation time uncertainty,this paper proposed an emergency resource scheduling model based on two-stage robust optimization.The model divided the decision-making process into two stages:pre-disaster preventive resource allocation and post-disaster emergency dynamic scheduling.By introducing robustness coefficients and uncertainty sets,it systematically characterized the impact of multiple uncertain parameters and used the column and constraint generation algorithm for efficient solution.Simulation experiments were conducted by combining typical railway tunnel emergency scenarios.The robust scheduling scheme expands the path network to 8～9 routes with an average cost premium of 18.9%,significantly improving the risk resistance of the system,which verifies that the proposed method can ensure rescue efficiency while balancing economy and reliability.This paper not only provides a scientific decision support tool for emergency management of railway tunnels but also its method system can be extended to complex application scenarios such as multi-department collaborative rescue,which has high theoretical value and practical significance.关键词
铁路隧道/应急资源调度/鲁棒优化/两阶段模型/列与约束生成算法Key words
Railway Tunnel/Emergency Resource Scheduling/Robust Optimization/Two-Stage Model/Column and Constraint Generation Algorithm分类
交通工程引用本文复制引用
曾昕涛,郭阳,何佳艺,贾祖祥,李力..基于两阶段鲁棒优化的铁路隧道应急资源调度研究[J].铁道运输与经济,2026,48(6):71-81,11.基金项目
西南交通大学重点实验室向本科生开放工程实践项目(ZD202505003) （ZD202505003）
国家重点研发计划项目(2022YFB4300502) （2022YFB4300502）
