考虑资源转移的特长铁路隧道工程鲁棒项目调度优化

Robust Project Scheduling Optimization for Extra-long Railway Tunnel Engineering Considering Resource Transfer

摘要

为保证特长铁路隧道工程在不确定环境下，既能按基准调度计划稳定执行，又能维持较低的施工成本，在截止工期下以鲁棒值最大和总成本最小为目标，构建一个鲁棒项目调度优化模型。模型考虑了资源转移对时间缓冲的影响，同时增加围岩等级变化下优先关系为“开始−开始”型工序的时间缓冲计算方法。设计了一种针对模型特点的TS-NSGA-Ⅱ算法，以自适应遗传操作的NSGA-Ⅱ为主框架，混合禁忌搜索构造邻域解的精英策略强化全局和局部搜索能力。案例仿真结果表明，所提模型和算法可以有效求解得到稳定执行和较低施工成本的调度计划方案，相较不考虑资源转移的鲁棒项目调度优化模型，本模型求得的鲁棒值提升了11.33%；算例测试对比显示，TS-NSGA-Ⅱ在求解质量和求解效率上更优，能够满足不确定环境下特长铁路隧道工程项目调度优化。

Abstract

To ensure reliable execution of construction schedules and maintain low costs for extra-long railway tunnel engineering under uncertainty, a robust project scheduling optimization model is established with objectives of maximizing schedule robustness and minimizing total cost under deadline constraints. The model incorporates the impact of resource transfer on time buffers and introduces a time buffer calculation method for start-to-start precedence relationships considering rock grade variations. A TS-NSGA-Ⅱ algorithm tailored to model characteristics is designed, employing NSGA-Ⅱ with adaptive genetic operations as the main framework, and integrating a tabu search-based elite strategy for generating neighborhood solutions to enhance both global and local search capabilities. Simulation results demonstrate that the proposed model and algorithm can effectively generate scheduling plans with reliable execution and low construction costs, yielding an 11.33% improvement in robustness over the model without resource transfer. Comparative case studies show that TS-NSGA-Ⅱ achieves superior solution quality and computational efficiency, and can satisfy the scheduling optimization requirements of extra-long railway tunnel engineering under uncertainty.

关键词

不确定环境 / 隧道工程 / 鲁棒项目调度 / 资源转移 / TS-NSGA-Ⅱ算法

Key words

uncertain environments / tunnel engineering / robust project scheduling / resource transfer / TS-NSGA-Ⅱ(tabu search-baed non-dominated sorting genetic algorithm)

参考文献

[1] 国务院 . 国务院关于印发“十四五”现代综合交通运输体系发展规划的通知 (国发[2021]27号)[J]. 中华人民共和国国务院公报, 2022(4): 8-28.

[2] The State Council. Circular of the state council on printing and issuing the development plan for the modern comprehensive transport system during the 14th Five-Year Plan period[J]. Gazette of the State Council of the People’s Republic of China, 2022(4): 8-28.

[3] 魏亚瑞, 阎震. 铁路建设单位数字化转型评价指标体系及应用[J]. 建筑经济, 2025, 46(2): 71-78.

[4] Wei Yarui, Yan Zhen. Evaluation index system and application of digital transformation for railway construction units[J]. Construction Economy, 2025, 46(2): 71-78.

[5] 巩江峰, 王伟, 黎旭, 等 . 截至2024年底中国铁路隧道情况统计及2024年新开通项目隧道情况介绍[J]. 隧道建设(中英文), 2025, 45(3): 636-653.

[6] Gong Jiangfeng, Wang Wei, Li Xu, et al. Statistics of China’s railway tunnels by the end of 2024 and overview of tunnels of new projects in 2024[J]. Tunnel Construction, 2025, 45(3): 636-653.

[7] Paraskevopoulou C, Dallavalle M, Konstantis S, et al. Assessing the failure potential of tunnels and the impacts on cost overruns and project delays[J]. Tunnelling and Underground Space Technology, 2022, 123: 104443.

[8] Tang Y J, Liu R K, Wang F T, et al. Scheduling optimization of linear schedule with constraint programming[J]. Computer-Aided Civil and Infrastructure Engineering, 2018, 33(2): 124-151.

[9] 刘利强. 基于LSM的铁路工程施工进度计划优化方法研究[D]. 北京: 北京交通大学, 2019.

[10] 周国华, 马依婷. 基于改进NSGA-Ⅱ的铁路项目进度计划多目标优化[J]. 工业工程, 2023, 26(4): 85-95.

[11] Zhou Guohua, Ma Yiting. Multi-objective optimization of railway project schedules based on modified NSGA-II[J]. Industrial Engineering Journal, 2023, 26(4): 85-95.

[12] Zhao X, Zhou G H. Dynamic schedule model and algorithm optimization method for linear engineering projects[J]. Engineering Optimization, 2024, 56(7): 1021-1044.

[13] Hu Z Y, Wang F T, Tang Y J. Scenario-oriented repetitive project scheduling optimization[J]. Computer-Aided Civil and Infrastructure Engineering, 2023, 38(10): 1239-1273.

[14] Katsuragawa C M, Lucko G, Isaac S, et al. Fuzzy linear and repetitive scheduling for construction projects[J]. Journal of Construction Engineering and Management, 2021, 147(3): 04021002.

[15] Bakry I, Moselhi O, Zayed T. Optimized scheduling and buffering of repetitive construction projects under uncertainty[J]. Engineering, Construction and Architectural Management, 2016, 23(6): 782-800.

[16] Cao Q N, Zou X, Zhang L H. Multiobjective robust optimization model for generating stable and makespan-protective repetitive schedules[J]. Journal of Construction Engineering and Management, 2022, 148(9): 04022099.

[17] Roofigari-Esfahan N, Razavi S. Uncertainty-aware linear schedule optimization: a space-time constraint-satisfaction approach[J]. Journal of Construction Engineering and Management, 2017, 143(5): 04016132.

[18] 周国华, 张华珂. 基于多工作面的铁路特长隧道施工进度计划优化[J]. 运筹与管理, 2023, 32(9): 21-27.

[19] Zhou Guohua, Zhang Huake. Optimization of construction schedule of the railway extra-long tunnel based on multiple working faces[J]. Operations Research and Management Science, 2023, 32(9): 21-27.

[20] 胡雪君, 王建江, 崔南方, 等 . 资源转移视角下的RCPSP鲁棒资源分配方法[J]. 系统工程学报, 2020, 35(2): 173-187.

[21] Hu Xuejun, Wang Jianjiang, Cui Nanfang, et al. Robust resource allocation method for the RCPSP from a resource transferring perspective[J]. Journal of Systems Engineering, 2020, 35(2): 173-187.

[22] 胡雪君, 梁盛, 王建江, 等 . 带转移时间的资源受限项目鲁棒调度优化[J]. 计算机集成制造系统, 2023, 29(12): 4191-4205.

[23] Hu Xuejun, Liang Sheng, Wang Jianjiang, et al. Robust scheduling and optimization for resource-constrained project scheduling problem with transfer times[J]. Computer Integrated Manufacturing Systems, 2023, 29(12): 4191-4205.

[24] Zhang H K, Ma R, He Z W. Project scheduling cost optimization based on resource transfer costs and robustness[J]. Computers & Operations Research, 2024, 161: 106445.

[25] Altanany M Y, Badawy M, Ebrahim G A, et al. Modeling and optimizing linear projects using LSM and non-dominated Sorting Genetic Algorithm (NSGA-II)[J]. Automation in Construction, 2024, 165: 105567.

[26] Lambrechts O, Demeulemeester E, Herroelen W. A tabu search procedure for developing robust predictive project schedules[J]. International Journal of Production Economics, 2008, 111(2): 493-508.

[27] 邹鑫, 陈丹昊, 张立辉. 考虑模糊工序工期的重复性项目时间费用权衡优化[J]. 工业工程, 2024, 27(4): 150-160,170.

[28] Zou Xin, Chen Danhao, Zhang Lihui. Time-cost tradeoff optimization for scheduling repetitive projects with fuzzy activity durations[J]. Industrial Engineering Journal, 2024, 27(4): 150-160,170.

[29] Deb K, Pratap A, Agarwal S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.

[30] Glover F. Tabu search: a tutorial[J]. Interfaces, 1990, 20(4): 74-94.

[31] Liang Z H, Zhao K G, He K Y, et al. Improved dynamic programming method for solving multi-objective and multi-stage decision-making problems[J]. Scientific Reports, 2025, 15: 1668.

基金资助

中国国家铁路集团有限公司科技研究开发计划重大课题(K2022G002)

中国铁道科学研究院集团有限公司资助项目(2023YJ144)