城市轨道交通乘务计划优化编制研究综述

doi:10.13229/j.cnki.jdxbgxb.20231138

Abstract

Abstract:

The crew schedule is a crucial component of urban rail transit operation plannings， and its optimization methods have become a hot research topic in the field of transportation. To promote the research progress in crew planning optimization for urban rail transit， reviewing relevant literatures from both domestic and international sources. Regarding mathematical models， systematically summarizing two typical modeling approaches which are respectively based on "set relations" and "network graph construction"； Concerning solution algorithms， provideing detailed assessments of traditional integer programming algorithm， mathematical programming method based on column generation， graph and network-related algorithm， and intelligent heuristic algorithm. Finally， addressing current research deficiencies and challenges to propose future research directions.

Key words: urban rail transit, crew planning problem, mathematical model, optimization algorithm

CLC Number:

U292.8

Zuo-an HU,Shu ZHOU,Yu-ang ZHANG. Review on crew planning optimization for urban rail transit[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(2): 401-418.

Figures/Tables 7

Fig.1

Fig.2

Table 1

Table 2

Fig.3

Fig.4

Table 3

References 96

1	Kang L, Zhu X, Sun H, et al. Modeling the first train timetabling problem with minimal missed trains and synchronization time differences in subway networks[J]. Transportation Research Part B: Methodological, 2016, 93: 17-36.
2	中国城市轨道交通协会.城市轨道交通2022年度统计和分析报告[J]. 城市轨道交通, 2023(4): 13-15.
	China Association of Metros. 2022 Statistical and analysis report on urban rail transit[J]. China Metros, 2023(4): 13-15.
3	Yang X, Chen A, Ning B, et al. Bi-objective programming approach for solving the metro timetable optimization problem with dwell time uncertainty[J]. Transportation Research Part E: Logistics and Transportation Review, 2017, 97: 22-37.
4	Qi J, Yang L, Di Z, et al. Integrated optimization for train operation zone and stop plan with passenger distributions[J]. Transportation Research Part E: Logistics and Transportation Review, 2018, 109: 151-173.
5	Corman F, D´Ariano A, Marra A D, et al. Integrating train scheduling and delay management in real-time railway traffic control[J]. Transportation Research Part E: Logistics and Transportation Review, 2017, 105: 213-239.
6	Yue Y, Han J, Wang S, et al. Integrated train timetabling and rolling stock scheduling model based on time-dependent demand for urban rail transit[J]. Computer-Aided Civil and Infrastructure Engineering, 2017, 32(10): 856-873.
7	Lusby R M, Larsen J, Ehrgott M, et al. Railway track allocation: models and methods[J]. OR Spectrum, 2011, 33(4): 843-883.
8	Yang L, Qi J, Li S, et al. Collaborative optimization for train scheduling and train stop planning on high-speed railways[J]. Omega, 2016, 64: 57-76.
9	Feng T, Lusby R M, Zhang Y, et al. An ADMM-based dual decomposition mechanism for integrating crew scheduling and rostering in an urban rail transit line[J]. Transportation Research Part C: Emerging Technologies, 2023, 149: 104081.
10	Zhou H, Qi J, Yang L, et al. Joint optimization of train scheduling and rolling stock circulation planning with passenger flow control on tidal overcrowded metro lines[J]. Transportation Research Part C: Emerging Technologies, 2022, 140: 103708.
11	Wang Y, Zhao K, D´Ariano A, et al. Real-time integrated train rescheduling and rolling stock circulation planning for a metro line under disruptions[J]. Transportation Research Part B: Methodological, 2021, 152: 87-117.
12	Yuan J, Gao Y, Li S, et al. Integrated optimization of train timetable, rolling stock assignment and short-turning strategy for a metro line[J]. European Journal of Operational Research, 2022, 301(3): 855-874.
13	Zhao Y, Li D, Yin Y, et al. Integrated optimization of demand-driven timetable, train formation plan and rolling stock circulation with variable running times and dwell times[J]. Transportation Research Part E: Logistics and Transportation Review, 2023, 171: 103035.
14	Pan H, Yang L, Liang Z. Demand-oriented integration optimization of train timetabling and rolling stock circulation planning with flexible train compositions: a column-generation-based approach[J]. European Journal of Operational Research, 2023, 305(1): 184-206.
15	Nishi T, Sugiyama T, Inuiguchi M. Two-level decomposition algorithm for crew rostering problems with fair working condition[J]. European Journal of Operational Research, 2014, 237(2): 465-473.
16	Heil J, Hoffmann K, Buscher U. Railway crew scheduling: models, methods and applications[J]. European Journal of Operational Research, 2020, 283(2): 405-425.
17	Kasirzadeh A, Saddoune M, Soumis F. Airline crew scheduling: models, algorithms, and data sets[J]. Euro Journal on Transportation and Logistics, 2017, 6(2): 111-137.
18	Deveci M, Demirel N Ç. A survey of the literature on airline crew scheduling[J]. Engineering Applications of Artificial Intelligence, 2018, 74: 54-69.
19	Wen X, Sun X, Sun Y, et al. Airline crew scheduling: models and algorithms[J]. Transportation Research Part E: Logistics and Transportation Review, 2021, 149: 102304.
20	沈吟东, 钱壮, 李媛媛. 公共交通驾驶员调度研究综述[J]. 运筹学学报, 2021, 25(1): 1-16.
	Shen Yin-dong, Qian Zhuang, Li Yuan-yuan. A survey on driver scheduling in public transportation[J]. Operations Research Transactions, 2021, 25(1): 1-16.
21	林枫. 固定区段轮乘制乘务交路计划编制模型及算法[J]. 铁道学报, 2019, 41(12): 10-18.
	Lin Feng. Bilevel scale model and multi-objective multi-stage heuristic algorithm for crew scheduling based on fixed-section pooling mode[J]. Journal of the China Railway Society, 2019, 41(12): 10-18.
22	张哲铭, 王莹, 廖正文, 等. 基于时空状态网络的高速铁路乘务交路计划优化研究[J]. 铁道学报, 2019, 41(12): 1-11.
	Zhang Zhe-ming, Wang Ying, Liao Zheng-wen, et al. Research on optimization of crew scheduling of high-speed railway based on time space state network[J]. Journal of the China Railway Society, 2019, 41(12): 1-11.
23	符卓, 袁雪莹, 车瑶. 高速动车组司机乘务交路优化编制方法[J]. 铁道学报, 2020, 42(7): 24-33.
	Fu Zhuo, Yuan Xue-ying, Che Yao. Optimization method for high-speed eletric multiple unit crew scheduling[J]. Journal of the China Railway Society, 2020, 42(7): 24-33.
24	张增勇. 城市轨道交通乘务计划编制方法研究[D]. 北京:北京交通大学交通运输学院, 2014.
	Zhang Zeng-yong. Modeling crew planning for urban rail transit systems[D]. Beijing: School of Traffic and Transportation,Beijing Jiaotong University, 2014.
25	许仲豪. 城市轨道交通乘务计划优化编制方法研究[D]. 北京: 北京交通大学交通运输学院, 2019.
	Xu Zhong-hao. Study of the optimization algorithm of crew scheduling for urban rail transit[D]. Beijing: School of Traffic and Transportation,Beijing Jiaotong University, 2019.
26	罗红双. 网络化条件下城市轨道交通乘务计划编制方法研究[D]. 成都:西南交通大学交通运输与物流学院, 2019.
	Luo Hong-shuang. Study on mothed of making crew planning of urban rail transit on network operation[D]. Chengdu: School of Transportation and Logistics, Southwest Jiaotong University, 2019.
27	Caprara A, Fischetti M, Toth P, et al. Algorithms for railway crew management[J]. Mathematical Programming, 1997, 79(1-3): 125-141.
28	Morgado E M, Martins J P. Scheduling and managing crew in the Portuguese railways[J]. Expert Systems with Applications, 1992, 5(3/4): 301-321.
29	Ball M, Bodin L, Dial R. A matching based heuristic for scheduling mass transit crews and vehicles[J]. Transportation Science, 1983, 17(1): 4-31.
30	Chu S C K, Chan E C H. Crew scheduling of light rail transit in Hong Kong: from modeling to implementation[J]. Computers & Operations Research, 1998, 25(11): 887-894.
31	Chew K L, Pang J, Liu Q, et al. An optimization based approach to the train operator scheduling problem at Singapore MRT[J]. Annals of Operations Research, 2001, 108(1): 111-122.
32	Sodhi M S, Norris S. A flexible, fast, and optimal modeling approach applied to crew rostering at London underground[J]. Annals of Operations Research, 2004, 127(1-4): 259-281.
33	李献忠, 徐瑞华. 基于时间耗费的城市轨道交通乘务排班优化[J]. 铁道学报, 2007, 2007(1): 21-25.
	Li Xian-zhong, Xu Rui-hua. Optimization of crew scheduling for urban rail transportation based on time costs[J]. Journal of the China Railway Society, 2007, 2007(1): 21-25.
34	李献忠, 徐瑞华. 基于乘务广义费用的城市轨道交通排班[J]. 同济大学学报: 自然科学版, 2007(6): 750-754.
	Li Xian-zhong, Xu Rui-hua. An optimal wide crew-related costs-based scheduling for crew of urban rail transportation[J]. Journal of Tongji University (Natural Science), 2007(6): 750-754.
35	Park T, Ryu K R. Crew pairing optimization by a genetic algorithm with unexpressed genes[J]. Journal of Intelligent Manufacturing, 2006, 17(4): 375-383.
36	张增勇, 毛保华, 杜鹏, 等. 基于惩罚费用的城市轨道交通乘务排班优化模型与算法[J]. 交通运输系统工程与信息, 2014, 14(2): 113-120.
	Zhang Zeng-yong, Mao Bao-hua, Du Peng, et al. Urban rail transit crew scheduling model and algorithm based on punishment costs[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(2): 113-120.
37	Elizondo R, Parada V, Pradenas L, et al. An evolutionary and constructive approach to a crew scheduling problem in underground passenger transport[J]. Journal of Heuristics, 2010, 16(4): 575-591.
38	丰富, 陈绍宽, 杜鹏. 考虑时间均衡度的城市轨道交通乘务排班计划优化方法[J]. 交通运输系统工程与信息, 2014, 14(6): 164-170.
	Feng Fu, Chen Shao-kuan, Du Peng. Time equitability-based crew scheduling optimization for urban rail transit[J]. Journal of Transportation Systems Engineering and Information Technology, 2014, 14(6): 164-170.
39	Chen S, Shen Y, Su X, et al. A crew scheduling with Chinese meal break rules[J]. Journal of Transportation Systems Engineering and Information Technology, 2013, 13(2): 90-95.
40	石俊刚, 史宏杰, 徐瑞华. 城市轨道交通乘务任务划分模型及算法研究[J]. 铁道学报, 2014, 36(5): 1-7.
	Shi Jun-gang, Shi Hong-jie, Xu Rui-hua. Modeling and solving urban rail transit crew pairing problems[J]. Journal of the China Railway Society, 2014, 36(5): 1-7.
41	Shen Y, Peng K, Chen K, et al. Evolutionary crew scheduling with adaptive chromosomes[J]. Transportation Research Part B: Methodological, 2013, 56: 174-185.
42	石俊刚, 周峰, 徐瑞华. 城轨交通乘务任务配对的集合分割模型及算法[J]. 同济大学学报: 自然科学版, 2015, 43(2): 232-238.
	Shi Jun-gang, Zhou Feng, Xu Rui-hua. A set segmentation model and algorithm for crew task pairing in urban rail transit[J]. Journal of Tongji University(Natural Science), 2015, 43(2): 232-238.
43	Han A F, Li E C. A constraint programming-based approach to the crew scheduling problem of the Taipei mass rapid transit system[J]. Annals of Operations Research, 2014, 223(1): 173-193.
44	石俊刚, 杨静, 周峰, 等. 基于任务均衡的城市轨道交通乘务任务轮转模型及算法[J]. 铁道学报, 2017, 39(9): 17-24.
	Shi Jun-gang, Yang Jing, Zhou Feng, et al. Crew rostering model and algorithm based on balanced workload in urban rail transit[J]. Journal of the China Railway Society, 2017, 39(9): 17-24.
45	Peng K, Shen Y. A variable iterated greedy algorithm based on grey relational analysis for crew scheduling[J]. Scientia Iranica, 2018, 25(2): 831-840.
46	黄志远, 周峰, 张在龙. 基于可预知事件的城市轨道交通乘务轮转计划优化调整[J]. 交通运输系统工程与信息, 2017, 17(4): 111-117.
	Huang Zhi-yuan, Zhou Feng, Zhang Zai-long. Optimizing adjustment of urban rail transit crew rostering plan based on predictable events[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(4): 111-117.
47	Shen Y, Li J, Peng K. An estimation of distribution algorithm for public transport driver scheduling[J]. International Journal of Operational Research, 2017, 28(2): 245-262.
48	陈绍宽, 马卓然, 彭小波, 等. 基于工作强度均衡的地铁乘务轮班计划优化模型[J]. 交通运输系统工程与信息, 2018, 18(6): 81-87.
	Chen Shao-kuan, Ma Zhuo-ran, Peng Xiao-bo, et al. Workload equitability-based optimum modeling for metro crew rostering[J]. Journal of Transportation Systems Engineering and Information Technology, 2018, 18(6): 81-87.
49	Janacek J, Kohani M, Koniorczyk M, et al. Optimization of periodic crew schedules with application of column generation method[J]. Transportation Research Part C: Emerging Technologies, 2017, 83: 165-178.
50	许仲豪, 杜鹏. 基于列生成的城市轨道交通乘务计划优化编制方法研究[J]. 铁道学报, 2019, 41(3): 25-32.
	Xu Zhong-hao, Du Peng. A column generation-based crew scheduling optimization method for urban rail transit[J]. Journal of the China Railway Society, 2019, 41(3): 25-32.
51	Zhou W, Yang X, Deng L, et al. Crew scheduling considering both crew duty time difference and cost on urban rail system[J]. Promet-Traffic&Transportation, 2016, 28(5): 449-460.
52	金华, 陈绍宽, 刘爽, 等. 基于固定班制的地铁乘务计划一体化优化方法[J]. 西南交通大学学报, 2020, 55(5): 955-962.
	Jin Hua, Chen Shao-kuan, Liu Shuang, et al.Integrated optimum crew planning in fixed shift system for subways[J]. Journal of Southwest Jiaotong University, 2020, 55(5): 955-962.
53	Zhou J, Xu X, Long J, et al. Integrated optimization approach to metro crew scheduling and rostering[J]. Transportation Research Part C: Emerging Technologies, 2021, 123: 102975.
54	金华, 陈绍宽, 王志美, 等. 基于地铁乘务资源共享的排班计划优化方法[J]. 交通运输系统工程与信息, 2021, 21(2): 126-132.
	Jin Hua, Chen Shao-kuan, Wang Zhi-mei, et al. Crew schedule optimization for urban rail transit with crew resources sharing[J]. Journal of Transportation Systems Engineering and Information Technology, 2021, 21(2): 126-132.
55	Zhou J, Xu X, Long J, et al. Metro crew planning with day-off pattern, duty type, and rostering scheme considerations[J]. Transportation Research Part C: Emerging Technologies, 2022, 143: 103832.
56	马亮, 徐晓英. 考虑便乘的地铁乘务任务配对的约束优化模型研究[J]. 铁道学报, 2021, 43(3): 25-33.
	Ma Liang, Xu Xiao-ying. Research on constraint optimization model for metro crew pairing problem with passenger tasks[J]. Journal of the China Railway Society, 2021, 43(3): 25-33.
57	Fuentes M, Cadarso L, Marín Á. A new approach to crew scheduling in rapid transit networks[J]. Transportation Research Procedia, 2015, 10: 554-563.
58	陈霁岩, 潘寒川, 刘志钢, 等. 基于任务均衡的乘务排班计划优化编制方法研究[J]. 铁道运输与经济, 2021, 43(9): 101-107.
	Chen Ji-yan, Pan Han-chuan, Liu Zhi-gang, et al. Research on optimization of crew scheduling based on task balance[J]. Railway Transport and Economy, 2021, 43(9): 101-107.
59	Fuentes M, Cadarso L, Marín Á. A hybrid model for crew scheduling in rail rapid transit networks[J]. Transportation Research Part B: Methodological, 2019, 125: 248-265.
60	薛锋, 李海, 梁鹏, 等. 基于遗传禁忌算法的城市轨道交通乘务任务配对研究[J]. 铁道运输与经济, 2022, 44(7): 103-109.
	Xue Feng, LI Hai, Liang Peng, et al. Research on urban rail transit crew task pairing based on genetic tabu algorithm[J]. Railway Transport and Economy, 2022, 44(7): 103-109.
61	Jin H, Chen S, Ran X, et al. Column generation-based optimum crew scheduling incorporating network representation for urban rail transit systems[J]. Computers & Industrial Engineering, 2022, 169: 108155.
62	薛锋, 梁鹏, 李海, 等. 地铁乘务排班计划优化的最短路快速算法[J]. 铁道科学与工程学报, 2022, 19(9): 2532-2540.
	Xue Feng, Liang Peng, Li Hai, et al. Optimization of subway crew scheduling based on shortest path faster algorithm[J]. Journal of Railway Science and Engineering, 2022, 19(9): 2532-2540.
63	Pan H, Liu Z, Yang L, et al. A column generation-based approach for integrated vehicle and crew scheduling on a single metro line with the fully automatic operation system by partial supervision[J]. Transportation Research Part E: Logistics and Transportation Review, 2021, 152: 102406.
64	苏铭, 刘兰芬, 杨信丰, 等. 基于TSP问题思想的城市轨道交通乘务排班计划研究[J]. 铁道运输与经济, 2022, 44(2): 131-138.
	Su Ming, Liu Lan-fen, Yang Xin-feng, et al. Research on TSP-based crew scheduling plan for urban rail transit[J]. Railway Transport and Economy, 2022, 44(2): 131-138.
65	Neufeld J S, Scheffler M, Tamke F, et al. An efficient column generation approach for practical railway crew scheduling with attendance rates[J]. European Journal of Operational Research, 2021, 293(3): 1113-1130.
66	戚博洋, 潘寒川, 刘志钢, 等. 考虑司机偏好的城市轨道交通乘务轮班模型[J]. 深圳大学学报: 理工版, 2023, 40(2): 203-209.
	Qi Bo-yang, Pan Han-chuan, Liu Zhi-gang, et al. Preference-oriented crew rostering optimization model for urban railway transit[J]. Journal of Shenzhen University (Science and Engineering), 2023, 40(2): 203-209.
67	Xue F, Zhang X, Hu P, et al. Metro crew planning with heterogeneous duty paths and period-cycle pattern considerations[J]. Computers & Industrial Engineering, 2023, 182: 109354.
68	Şahin G, Yüceoğlu B. Tactical crew planning in railways[J]. Transportation Research Part E: Logistics and Transportation Review, 2011, 47(6): 1221-1243.
69	潘寒川, 刘志钢, 吴强, 等. 考虑用餐约束的城市轨道交通乘务计划编制优化研究[J]. 铁道运输与经济, 2019, 41(1): 121-126.
	Pan Han-chuan, Liu Zhi-qiang, Wu Qiang, et al. A study on the optimization of crew scheduling in urban rail transit with the meal constraint[J]. Railway Transport and Economy, 2019, 41(1): 121-126.
70	金华. 乘务资源共享条件下的城市轨道交通乘务计划优化研究[D]. 北京:北京交通大学交通运输学院, 2022.
	Jin Hua. Crew scheduling optimization for urban rail transit system with crew resources sharing[D]. Beijing: School of Traffic and Transportationo,Beijing Jiaotong University, 2022.
71	Dauzère P S, Almeida D, Guyon O, et al. A lagrangian heuristic framework for a real-life integrated planning problem of railway transportation resources[J]. Transportation Research Part B: Methodological, 2015, 74: 138-150.
72	Huang S H, Yang T H, Wang R T. Ant colony optimization for railway driver crew scheduling: from modeling to implementation[J]. Journal of the Chinese Institute of Industrial Engineers, 2011, 28(6): 437-449.
73	Vaidyanathan B, Jha K C, Ahuja R K. Multicommodity network flow approach to the railroad crew-scheduling problem[J]. IBM Journal of Research and Development, 2007, 51(3): 325-344.
74	Fischetti M, Lodi A, Martello S, et al. A polyhedral approach to simplified crew scheduling and vehicle scheduling problems[J]. Management Science, 2001, 47(6): 833-850.
75	Suyabatmaz A Ç, Şahin G. Railway crew capacity planning problem with connectivity of schedules[J]. Transportation Research Part E: Logistics and Transportation Review, 2015, 84: 88-100.
76	Zhou F, Xu R H. Study on model and algorithm for urban rail transit crew scheduling system[C]∥International Conference On Computer Design and Applications, Qinhuangdao, China, 2010: 213-216.
77	袁仁杰. 基于遗传算法的地铁司乘排班计划管理研究[D]. 成都:西南交通大学交通运输与物流学院, 2018.
	Yuan Ren-jie. Research on metro driver crew management planning based on genetic algorithm[D]. Chengdu: School of Transportation and Logistics,Southwest Jiaotong University, 2018.
78	李思. 基于生物节律的地铁乘务计划编制研究[D]. 北京:北京交通大学交通运输学院, 2019.
	Li Si. Study on the subway crew planning based on the biological rhythm[D]. Beijing: School of Traffic and Transportation,Beijing Jiaotong University, 2019.
79	Lusby R M, Larsen J, Bull S. A survey on robustness in railway planning[J]. European Journal of Operational Research, 2018, 266(1): 1-15.
80	Cacchiani V, Caprara A, Galli L, et al. Railway rolling stock planning: robustness against large disruptions[J]. Transportation Science, 2012, 46(2): 217-232.
81	Zhou L, Yang X, Wang H, et al. A robust train timetable optimization approach for reducing the number of waiting passengers in metro systems[J]. Physica A: Statistical Mechanics and its Applications, 2020, 558: 124927.
82	Qu Y, Wang H, Wu J, et al. Robust optimization of train timetable and energy efficiency in urban rail transit: a two-stage approach[J]. Computers & Industrial Engineering, 2020, 146: 106594.
83	Jütte S, Müller D, Thonemann U W. Optimizing railway crew schedules with fairness preferences[J]. Journal of Scheduling, 2017, 20(1): 43-55.
84	Smith B M, Wren A. A bus crew scheduling system using a set covering formulation[J]. Transportation Research Part A: General, 1988, 22(2): 97-108.
85	Smith B M. Impacs-A bus crew scheduling system using integer programming[J]. Mathematical Programming, 1988, 42(1-3): 181-187.
86	Kwan R S K. Case studies of successful train crew scheduling optimisation[J]. Journal of Scheduling, 2011, 14(5): 423-434.
87	Lübbecke M E, Desrosiers J. Selected topics in column generation[J]. Operations Research, 2005, 53(6): 1007-1023.
88	Desrochers M, Soumis F. A column generation approach to the urban transit crew scheduling problem[J]. Transportation Science, 1989, 23(1): 1-13.
89	Lusby R, Dohn A, Range T M, et al. A column generation-based heuristic for rostering with work patterns[J]. Journal of the Operational Research Society, 2012, 63(2): 261-277.
90	Wren A, Wren D O. A genetic algorithm for public transport driver scheduling[J]. Computers & Operations Research, 1995, 22(1): 101-110.
91	Kwan R S K, Kwan A S K, Wren A. Evolutionary driver scheduling with relief chains[J]. Evolutionary Computation, 2001, 9(4): 445-460.
92	Hanafi R, Kozan E. A hybrid constructive heuristic and simulated annealing for railway crew scheduling[J]. Computers & Industrial Engineering, 2014, 70: 11-19.
93	Borndörfer R, Schulz C, Seidl S, et al. Integration of duty scheduling and rostering to increase driver satisfaction[J]. Public Transport, 2017, 9(1/2): 177-191.
94	王莹. 动车组运用计划和乘务计划的优化方法研究[D]. 北京: 北京交通大学交通运输学院, 2009.
	Wang Ying. Study on the optimization of rolling stock planning and crew planning[D]. Beijing: School of Traffic and Transportation, Beijing Jiaotong University, 2009.
95	Bach L, Dollevoet T, Huisman D. Integrating timetabling and crew scheduling at a freight railway operator[J]. Transportation Science, 2016, 50(3): 878-891.
96	戚建国, 周厚盛, 杨立兴, 等. 灵活编组条件下轨道交通客货协同运输方案优化[J]. 交通运输系统工程与信息, 2022, 22(2): 197-205.
	Qi Jian-guo, Zhou Hou-sheng, Yang Li-xing, et al. Optimization methods of combined passenger and freight transportation based on flexible train composition mode[J]. Journal of Transportation Systems Engineering and Information Technology, 2022, 22(2): 197-205.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

对比特点		普速铁路	高速铁路	城市轨道交通（以地铁为例）
线路条件	乘务基地	两端和中间	线路两端	线路两端
线路条件	站间距离	很长	较长	很短
运营情况	时刻表安排	较灵活	固定	固定
	运行交路	长	较长	短
	开行频率	较低	较高	高
乘务管理	人员类型	工种多	工种多	主要为司机
	乘务片段长度	长	较长	短
	班次所含片段数	少	较少	多
	冗余时间	很长	较长	较短
	跨夜值乘	常有	较少	一般没有

文献	计划阶段	建模方法	求解算法					文献	计划阶段	建模方法	求解算法
文献	计划阶段	建模方法	IPM	GNA	H	CG	Other	文献	计划阶段	建模方法	IPM	GNA	H	CG	Other
［33］	CS	NFP		√				［32］	CR	NFP	√
［34］	CS	SPP			√			［35］	CS	other			√
［36］	CS	SPP		√				［37］	CS	-			√
［38］	CS	SPP		√				［39］	CS	SCP			√
［40］	CS	SCP				√		［41］	CS	SCP			√
［42］	CS	SPP				√		［43］	CS	SCP				√
［44］	CR	SPP				√		［45］	CS	SCP			√
［46］	CR	SPP			√			［47］	CS	SCP			√
［48］	CR	SPP			√			［49］	CS	NFP				√
［50］	CS	SPP， NFP						［51］	CS	SPP			√
［52］	CS⊕CR	SCP， NFP	√			√		［53］	CS⊕CR	NFP			√
［54］	CS	SCP， NFP	√			√		［55］	CS⊕CR	NFP		√		√
［56］	CS	other			√		CP	［57］	CS	other			√
［58］	CS	SPP				√		［59］	CS	NFP	√
［60］	CS	SPP			√			［61］	CS	SCP，NFP		√		√
［62］	CS	other		√				［63］	CS⊕VS	SCP，SPP， NFP	√			√
［64］	CS	NFP			√			［65］	CS	SCP，NFP	√		√	√
［66］	CR	SPP， NFP	√					［67］	CS∪CR	NFP			√		ADMM
［30］	CS	NFP			√			［9］	CS⊕CR	NFP					ADMM
［31］	CS	SPP， other		√	√

文献	智能启发式算法（H）					（最大）求解规模	算法特点
文献	GA	TS	PSO	ACO	Other	（最大）求解规模	算法特点
［35］	√					814个乘务片段	基于贪婪机制的交叉和变异算子；染色体分为表达和未表达两部分
［41］	√					830个乘务片段	染色体长度可自适应改变
［38］	√					求解得到20个乘务班次	遗传算法中嵌套双向匹配法
［60］	√	√				686个乘务作业段	遗传禁忌混合搜索算法
［31］		√				高峰时段开行列车77列非高峰时段开行列车41列	夜班编制采用二分匹配算法；日班编制采用禁忌搜索算法
［34］		√				138个乘务作业段（日班）	夜班编制采用Hungarian算法日班编制采用禁忌搜索算法
［37］		√			EM；GM	900个乘务片段	对比了3种启发式算法
［46］		√				每日早班10个，白班15个，夜班10个轮转周期：1周，可用司机：48名	初始方案通过贪婪算法生成
［36］			√			9列车底 182个乘务片段	下层模型采用改进Dijkstra算法；上层模型采用离散粒子群算法
［48］			√		SE	每日100个值乘班次，20个休息班次轮转模式：六班五运转	粒子群与模拟进化的混合算法
［67］			√			686个乘务作业段轮转周期：7天轮转模式：四班三运转	排班阶段采用基于ADMM的对偶分解算法；轮班阶段采用改进粒子群算法
［64］				√		248个乘务作业段	将问题转换为类TSP问题
［51］				√		724个乘务作业段	基于蚁群路径选择策略的优化算法
［39］					√	830个乘务片段	考虑中国就餐和休息制度，基于启发式思想的班次生成与选择方式
［53］					LR	2030个乘务作业段（1周）	拉格朗日松弛启发式算法
［45］					GM	830个乘务片段	灰色关联分析融入可变迭代贪婪算法
［59］					F&R	日开行车次超300趟	进行聚类操作简化模型

[1]	Pei-ning TIAN,Rui-yong TONG,Hai-peng WANG,Bao-hua MAO,Hao-xiang ZHANG,Xia LU. Optimization of cross-line train operation across the urban rail transit loop line [J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(1): 132-140.
[2]	Na ZHANG,Feng CHEN,Jian-po WANG,Ya-di ZHU. Recognition of travel patterns for urban rail transit passengers based on spatiotemporal sequence similarity [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(9): 2588-2599.
[3]	Dan-hui LAI,Wei-feng LUO,Xu-dong YUAN,Zi-liang QIU. Key point feature extraction algorithms for multimodal gesture in complex environments [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(8): 2288-2294.
[4]	Jing-hua ZHAO,Shi-hao DU,Liang-wei LIU,Yun-feng HU,Yao SUN,Fang-xi XIE. Parameter identification for SCR systems based on improved chaos optimization algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(2): 550-557.
[5]	Bo-song FAN,Chun-fu SHAO. Urban rail transit emergency risk level identification method [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(2): 427-435.
[6]	Hui-jing DOU,Dong-xu XIE,Wei GUO,Lu-yang XING. Direction of arrival estimation based on improved orthogonal matching pursuit algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(12): 3568-3576.
[7]	Jian XIAO,Jing-wei LIU,Xin HU,Xiao-gang QI. TDOA⁃AOA location based on improved african vulture algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(12): 3558-3567.
[8]	Lei ZHANG,Zi-mu LI,Yong-yao YAN,Fei DOU,Hong-jie LIU. Brake control algorithm for virtually coupled trains based on multi vehicle cooperation [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(10): 3027-3036.
[9]	Guo-jin TAN,Qing-wen KONG,Xin HE,Pan ZHANG,Run-chao YANG,Yang-jun CHAO,Zhong YANG. Bridge scour depth identification based on dynamic characteristics and improved particle swarm optimization algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(6): 1592-1600.
[10]	Zhi-yong SHE,Tong-ming ZHU,Wang-kui LIU. Rapid trajectory programming for hypersonic umanned areial vehicle in ascent phase based on proximal policy optimization [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(3): 863-870.
[11]	Jing WANG,Feng WAN,Chun-jiao DONG,Chun-fu SHAO. Modelling on catchment area and attraction intensity of urban rail transit stations [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(2): 439-447.
[12]	Qing-yong WANG,Wei-qiang QU. Optimization algorithm of urban rail transit operation scheduling based on linear programming [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(12): 3446-3451.
[13]	Zheng ZHANG,Qi-dan ZHU,Xiao-long LYU,Xing FAN. Optimized method for solving inverse kinematics of redundant manipulator [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(12): 3379-3387.
[14]	Li-li BAI,Feng-guo JIANG,Yu-ming ZHOU,Xiao ZENG. Optimized design of structure reliability based on improved whale algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(11): 3160-3165.
[15]	Hui GUO,Jie-di FU,Zhen-dong LI,Yan YAN,Xiao LI. SVM parameters and feature selection optimization based on improved whale algorithm [J]. Journal of Jilin University(Engineering and Technology Edition), 2023, 53(10): 2952-2963.

Review on crew planning optimization for urban rail transit

RICH HTML

PDF (PC)

Abstract

Cite this article

share this article

Figures/Tables 7

References 96

Related Articles 15

Metrics

Comments

Recommended 0