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2023 | OriginalPaper | Chapter

A Scheduling Plan Model for Metro Crew Incorporating Fatigue and Biological Rhythms

Authors : Yueyuan Chen, Weining Fang, Si Li, Jianxin Wang

Published in: Green Transportation and Low Carbon Mobility Safety

Publisher: Springer Nature Singapore

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Abstract

Crew scheduling is one of the critical planning decisions in railway transportation. The existing scheduling and rostering methods usually take the lowest cost as the objective, ignoring the metrzzo crew members’ fatigue and biological rhythms. This paper proposed an optimization approach considering fatigue's impact on solving real-world metro crew scheduling and rostering problems. The shift work characteristics of the metro crew were analyzed firstly. The usability of the Ikeda formula for fatigue evaluation was verified and applied to the metro crew. Then the metro crew scheduling and rostering model were described, and the process of incorporating fatigue factors into the model was demonstrated. Moreover, using the genetic algorithm to solve the problems. Finally, this model was applied to the Beijing Metro Yanfang Line. The results illustrated that the method could significantly reduce the metro crew members’ fatigue value with optimized operating costs.

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Arendt J (2010) Shift work: coping with the biological clock. Occup Med 60(1):10–20. https://doi.org/10.1093/occmed/kqp162CrossRef

Rogers AS, Spencer MB, Stone BM (1999) Validation and development of a method for assessing the risks arising from mental fatigue. Health Safety Executive, Sudbury

Bowden ZE, Ragsdale CT (2018) The truck driver scheduling problem with fatigue monitoring. Decis Support Syst 110:20–31. https://doi.org/10.1016/j.dss.2018.03.002CrossRef

Bowler N, Gibson H (2015) Fatigue and its contributions to railway incidents. Rail Safety and Standards Board, London

Caprara A, Fischetti M, Toth P, Vigo D, Guida PL (1997) Algorithms for railway crew management. Math Prog 79(1–3):125–141. https://doi.org/10.1007/BF02614314MathSciNetCrossRefMATH

Caprara A, Monaci M, Toth P (2001) A global method for crew planning in railway applications. Comput Aided Scheduling Public Transport, Springer 17–36. https://doi.org/10.1007/978-3-642-56423-9_2

Ceder AA, Hassold S (2015) Applied analysis for improving rail-network operations. J Rail Trans Planning Manag 5(2):50–63. https://doi.org/10.1016/j.jrtpm.2015.06.001CrossRef

Chu SC (2007) Generating, scheduling and rostering of shift crew-duties: applications at the Hong Kong International Airport. Eur J Oper Res 177(3):1764–1778. https://doi.org/10.1016/j.ejor.2005.10.008CrossRefMATH

Clement R, Wren A (1995) Greedy genetic algorithms, optimizing mutations and bus driver scheduling. Comput Aided Transit Scheduling, Springer 213–235. https://doi.org/10.1007/978-3-642-57762-8_14

10.

Crum MR, Morrow PC (2002) The influence of carrier scheduling practices on truck driver fatigue. Transp J 42(1):20–41. https://doi.org/10.2307/20713513CrossRef

11.

Dorrian J, Roach GD, Fletcher A, Dawson D (2007) Simulated train driving: fatigue, self-awareness and cognitive disengagement. Appl Ergon 38(2):155–166. https://doi.org/10.1016/j.apergo.2006.03.006CrossRef

12.

Dos Santos AG, Mateus GR (2009) General hybrid column generation algorithm for crew scheduling problems using genetic algorithm. In: Proceedings of IEEE congress on evolutionary computation. IEEE 1799–1806. https://doi.org/10.1109/cec.2009.4983159

13.

Elizondo R, Parada V, Pradenas L, Artigues C (2010) An evolutionary and constructive approach to a crew scheduling problem in underground passenger transport. J Heuristics 16(4):575–591. https://doi.org/10.1007/s10732-009-9102-xCrossRefMATH

14.

Ernst A, Jiang H, Krishnamoorthy M, Nott H, Sier D (2001) Rail crew scheduling and rostering optimization algorithms. Computer-Aided Scheduling of Public Transport, Springer 53–71. https://doi.org/10.1007/978-3-642-56423-9_4

15.

Ernst AT, Jiang H, Krishnamoorthy M, Sier D (2004) Staff scheduling and rostering: a review of applications, methods and models. Eur J Oper Res 153(1):3–27. https://doi.org/10.1016/s0377-2217(03)00095-xMathSciNetCrossRefMATH

16.

Filtness AJ, Naweed A (2017) Causes, consequences and countermeasures to driver fatigue in the rail industry: the train driver perspective. Appl Ergon 60:12–21. https://doi.org/10.1016/j.apergo.2016.10.009CrossRef

17.

Freling R, Huisman D, Wagelmans AP (2001) Applying an integrated approach to vehicle and crew scheduling in practice. Comput Aided Scheduling Public Transport, Springer 73–90. https://doi.org/10.1007/978-3-642-56423-9_5

18.

Freling R, Lentink RM, Odijk MA (2001) Scheduling train crews: a case study for the Dutch railways. Comput Aided Scheduling Public Transport, Springer 153–165. https://doi.org/10.1007/978-3-642-56423-9_9

19.

Fuentes M, Cadarso L, Marín Á (2019) A hybrid model for crew scheduling in rail rapid transit networks. Transport Res Part B Methodol 125:248–265. https://doi.org/10.1016/j.trb.2019.05.007CrossRef

20.

García J, Altimiras F, Peña A, Astorga G, Peredo O (2018) A binary cuckoo search big data algorithm applied to large-scale crew scheduling problems. Complexity. https://doi.org/10.1155/2018/8395193

21.

Han AF, Li EC (2014) A constraint programming-based approach to the crew scheduling problem of the Taipei mass rapid transit system. Ann Oper Res 223(1):173–193. https://doi.org/10.1007/s10479-014-1619-1MathSciNetCrossRefMATH

22.

Van Dongen HP, Dinges DF (2005) Circadian rhythms in fatigue, alertness, and performance. In: Meir TR, Kryger R, William R, Dement W (eds) pp 435–443. Principles and Practice of Sleep Medicine (Fourth Edition). https://doi.org/10.1016/B0-72-160797-7/50042-2

23.

Hartley L, Horberry T, Mabbott N, Krueger GP (2000) Review of fatigue detection and prediction technologies. National Road Transport Commission Virginia

24.

Heil J, Hoffmann K, Buscher U (2019) Railway crew scheduling: models, methods and applications. Eur J Oper Res. https://doi.org/10.1016/j.ejor.2019.06.016CrossRefMATH

25.

Hoffmann K, Buscher U (2019) Valid inequalities for the arc flow formulation of the railway crew scheduling problem with attendance rates. Comput Ind Eng 127:1143–1152. https://doi.org/10.1016/j.cie.2018.05.031CrossRef

26.

Jütte S, Müller D, Thonemann UW (2017) Optimizing railway crew schedules with fairness preferences. J Sched 20(1):43–55. https://doi.org/10.1007/s10951-016-0499-4MathSciNetCrossRefMATH

27.

Kazuhiro S (2002) Revised work of the Japanese society for occupational health industry fatigue study group “Subjective symptoms” 2002. (Japanese). Digest of science of labour 57(5):295–298

28.

Khmeleva E, Hopgood AA, Tipi L, Shahidan M (2018) Fuzzy-logic controlled genetic algorithm for the rail-freight crew-scheduling problem. KI-Künstliche Intelligenz 32(1):61–75. https://doi.org/10.1007/s13218-017-0516-6CrossRef

29.

Korf RE (1998) A complete anytime algorithm for number partitioning. Artif Intell 106(2):181–203. https://doi.org/10.1016/s0004-3702(98)00086-1MathSciNetCrossRefMATH

30.

Lee C-K (2004) The integrated scheduling and rostering problem of train driver using Genetic algorithm. In: Proceedings of 9th international conference on computer-aided scheduling of public transport (CASPT), San Diego–California, Citeseer

31.

Lee KS, Geem ZW (2005) A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice. Comput Methods Appl Mech Eng 194(36–38):3902–3933. https://doi.org/10.1016/j.cma.2004.09.007CrossRefMATH

32.

Li Si FW, Wu, Jie, Bin W (2019) A comparison between two methods of evaluating shift subway drivers fatigue (Chinese). China Safety Sci J 029(002):7–12

33.

Ma J, Ceder A, Yang Y, Liu T, Guan W (2016) A case study of Beijing bus crew scheduling: a variable neighborhood-based approach. J Adv Transp 50(4):434–445. https://doi.org/10.1002/atr.1333CrossRef

34.

Masahiro O (1979) Fatigue research (Second Edition), Tong Wen College, Tokyo

35.

McGuffog A, Spencer MB, Stone BM, Turner C (2005) Guidelines for the management and reduction of fatigue in train drivers. Report T059. London: RSSB.

36.

McGuffog A, Spencer MB, Stone BM, Turner C (2004) Working patterns of train drivers: implications for fatigue and safety. QinetiQ Centre for Human Sciences Report. KI/CHS/CR043098

37.

Mingozzi A, Boschetti MA, Ricciardelli S, Bianco L (1999) A set partitioning approach to the crew scheduling problem. Oper Res 47(6):873–888. https://doi.org/10.1287/opre.47.6.873CrossRefMATH

38.

Monk TH, Kennedy KS, Rose LR, Linenger JM (2001) Decreased human circadian pacemaker influence after 100 days in space: a case study. Psychosom Med 63(6):881–885. https://doi.org/10.1097/00006842-200111000-00005CrossRef

39.

Naweed A (2014) Investigations into the skills of modern and traditional train driving. Appl Ergon 45(3):462–470. https://doi.org/10.1016/j.apergo.2013.06.006CrossRef

40.

Neri DF, Oyung RL, Colletti LM, Mallis MM, Tam PY, Dinges DF (2002) Controlled breaks as a fatigue countermeasure on the flight deck. Aviat Space Environ Med 73(7):654–664. https://doi.org/10.1016/S1352-2310(02)00273-XCrossRef

41.

Nesthus T, Cruz C, Boquet A, Detwiler C, Holcomb K, Della Rocco P (2001) Circadian temperature rhythms in clockwise and counter-clockwise rapidly rotating shift schedules. J Human Ergol 30(1–2), 245–249. https://doi.org/10.11183/jhe1972.30.245

42.

Parasuraman R, Sheridan TB, Wickens CD (2008) Situation awareness, mental workload, and trust in automation: viable, empirically supported cognitive engineering constructs. J Cogn Eng Decision Making 2(2):140–160. https://doi.org/10.1518/155534308x284417CrossRef

43.

Shen J, Botly LC, Chung SA, Gibbs AL, Sabanadzovic S, Shapiro CM (2006) Fatigue and shift work. J Sleep Res 15(1):1–5. https://doi.org/10.1111/j.1365-2869.2006.00493.xCrossRef

44.

Shen Y, Peng K, Chen K, Li J (2013) Evolutionary crew scheduling with adaptive chromosomes. Trans Res Part B Methodol 56:174–185. https://doi.org/10.1016/j.trb.2013.08.003CrossRef

45.

Shiffer D, Minonzio M, Dipaola F, Bertola M, Zamuner AR, Dalla Vecchia LA, Solbiati M, Costantino G, Furlan R, Barbic F (2018) Effects of clockwise and counterclockwise job shift work rotation on sleep and work-life balance on hospital nurses. Int J Environ Res Public Health 15(9):2038. https://doi.org/10.3390/ijerph15092038CrossRef

46.

Sodhi MS, Norris S (2004) A flexible, fast, and optimal modeling approach applied to crew rostering at London Underground. Ann Oper Res 127(1–4):259–281. https://doi.org/10.1023/b:anor.0000019092.76669.a1CrossRefMATH

47.

Souai N, Teghem J (2009) Genetic algorithm based approach for the integrated airline crew-pairing and rostering problem. Eur J Oper Res 199(3):674–683. https://doi.org/10.1016/j.ejor.2007.10.065MathSciNetCrossRefMATH

48.

Spencer M, Robertson K, Folkard S (2006) The development of a fatigue/risk index for shiftworkers. Health and Safety Executive, Sudbury

49.

Suyabatmaz AÇ, Şahin G (2015) Railway crew capacity planning problem with connectivity of schedules. Transport Res Part E Logistics Transport Rev 84:88–100. https://doi.org/10.1016/j.tre.2015.10.003CrossRef

50.

Toshiaki T, Shinzo Y (1991) The validity of the workload evaluation by driver’s subjective symptoms. Jpn J Ergonom 27(Suppl):248–249. https://doi.org/10.5100/jje.27.Supplement_248CrossRef

51.

Toshihisa I, Hisae O (1986) Quantification of workload in power car operation (Japanese). Jpn J Ergonom 22(Supplement):182–183

52.

Tsukasaki K, Kido T, Makimoto K, Naganuma R, Sunaga K (2006) The impact of sleep interruptions on vital measurements and chronic fatigue of female caregivers providing home care in Japan. Nurs Health Sci 8(1):2–9. https://doi.org/10.1111/j.1442-2018.2006.00261.xCrossRef

53.

Vazirani VV (2013) Approximation algorithms, Springer Science & Business Media

54.

Wang Q, Yang J, Ren M, Zheng Y (2006) Driver fatigue detection: a survey. In: Proceedings of 6th world congress on intelligent control and automation. IEEE 8587–8591. https://doi.org/10.1109/wcica.2006.1713656

55.

Wilson J, Farrington-Darby T, Cox G, Bye R, Hockey GRJ (2007) The railway as a socio-technical system: human factors at the heart of successful rail engineering. In: Proceedings of the institution of mechanical engineers, Part F: J Rail Rapid Transit 221(1):101–115. https://doi.org/10.1243/09544097jrrt78CrossRef

56.

Zhang Y, Fang W, Er M-C, Wang J, Guo B-Y (2010) Analysis on fatigue risk of subway divers on shift (Chinese). Railway Trans Econ 32(4):90–94. https://doi.org/10.3969/j.issn.1003-1421.2010.04.023CrossRef

57.

Zhao Y, Yue WJ (2017) Cognitive radio networks with multiple secondary users under two kinds of priority schemes: performance comparison and optimization. J Industr Manag Optimizat 13(3):1449–1466. https://doi.org/10.3934/jimo.2017001MathSciNetCrossRefMATH

Title: A Scheduling Plan Model for Metro Crew Incorporating Fatigue and Biological Rhythms
Authors: Yueyuan Chen
Weining Fang
Si Li
Jianxin Wang
Publisher: Springer Nature Singapore
Book: Green Transportation and Low Carbon Mobility Safety
Print ISBN: 978-981-19-5614-0

Electronic ISBN: 978-981-19-5615-7

Copyright Year: 2023
DOI: https://doi.org/10.1007/978-981-19-5615-7_1

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