A Stochastic Optimization Approach to Energy-Efficient Underground Timetabling Under Uncertain Dwell and Running Times

Published Online:https://doi.org/10.1287/trsc.2022.0269

References

  • Achterberg T (2007) Constraint integer programming. Doctoral thesis, Technische Universität Berlin, Fakultät II - Mathematik und Naturwissenschaften, Berlin.Google Scholar
  • Albrecht T (2010) Reducing power peaks and energy consumption in rail transit systems by simultaneous train running time control. WIT Trans. on State of the Art in Science and Engineering 74:1–10.CrossrefGoogle Scholar
  • Bärmann A, Gemander P, Merkert M (2020) The clique problem with multiple-choice constraints under a cycle-free dependency graph. Discrete Appl. Math. 283:59–77.CrossrefGoogle Scholar
  • Bärmann A, Martin A, Schneider O (2017) A comparison of performance metrics for balancing the power consumption of trains in a railway network by slight timetable adaptation. Public Transport 9(1–2):95–113.CrossrefGoogle Scholar
  • Bärmann A, Martin A, Schneider O (2021) Efficient formulations and decomposition approaches for power peak reduction in railway traffic via timetabling. Transportation Sci. 55(3):747–767.LinkGoogle Scholar
  • Bärmann A, Gellermann T, Merkert M, Schneider O (2018) Staircase compatibility and its applications in scheduling and piecewise linearization. Discrete Optim. 29:111–132.CrossrefGoogle Scholar
  • Bärmann A, Gemander P, Hager L, Nöth F, Schneider O (2022) EETTlib – Energy-efficient train timetabling library. Accessed June 8, 2023, http://www.optimization-online.org/DB_HTML/2021/09/8597.html.Google Scholar
  • Cacchiani V, Toth P (2012) Nominal and robust train timetabling problems. European J. Oper. Res. 219(3):727–737.CrossrefGoogle Scholar
  • Cacchiani V, Huisman D, Kidd M, Kroon L, Toth P, Veelenturf L, Wagenaar J (2014) An overview of recovery models and algorithms for real-time railway rescheduling. Transportation Res. Part B 63:15–37.CrossrefGoogle Scholar
  • Chen JF, Lin RL, Liu YC (2005) Optimization of an mrt train schedule: Reducing maximum traction power by using genetic algorithms. IEEE Trans. 20(3):1366–1372.Google Scholar
  • Feng X, Zhang H, Ding Y, Liu Z, Peng H, Xu B, (2013) A review study on traction energy saving of rail transport. Discrete Dynam. Nature Soc. 2013:1–9.CrossrefGoogle Scholar
  • Fournier D, Mulard D, Fages F (2012) Energy optimization of metro timetables: A hybrid approach. The 18th International Conference on Principles and Practice of Constraint Programming (Springer, Berlin), 7–12.Google Scholar
  • Glomb L, Liers F, Rösel F, (2022) A rolling-horizon approach for multi-period optimization. European J. Oper. Res. 300(1):189–206.CrossrefGoogle Scholar
  • Gong C, Zhang S, Zhang F, Jiang J, Wang X (2014) An integrated energy-efficient operation methodology for metro systems based on a real case of shanghai metro line one. Energies 7(11):7305–7329.CrossrefGoogle Scholar
  • Gurobi Optimization (2022) Gurobi optimizer reference manual. Accessed June 8, 2023, http://www.gurobi.com.Google Scholar
  • Hasegawa D, Nicholson GL, Roberts C, Schmid F (2014) The impact of different maximum speeds on journey times, energy use, headway times and the number of trains required for phase one of Britain’s high speed two line. WIT Transactions on the Built Environment 135:485–496.Google Scholar
  • Kall P, Wallace SW (1994) Stochastic Programming. Wiley-Interscience Series in Systems and Optimization (John Wiley & Sons, Ltd., Chichester).Google Scholar
  • Kim KM, Kim KT, Han M (2011) A model and approaches for synchronized energy saving in timetable. Proc. 9th World Congress Railway Res. (WCRR) (Lille, France, May 2011), 1–8.Google Scholar
  • Kim KM, Oh SM, Han M (2010) A mathematical approach for reducing the maximum traction energy: The case of Korean mrt trains. Proc. Internat. MultiConf. Engineers Comput. Scientists. Hong Kong, March 17 − 19, 2010, vol. III (International Association of Engineers), 2169–2173.Google Scholar
  • Kimura N, Miyatake M (2014) Strategy of speed restriction allowing extended running times to minimize energy consumption and passenger disutility. WIT Trans. on The Built Environment 135:733–743.Google Scholar
  • Kleywegt AJ, Shapiro A, Homem-de Mello T (2002) The sample average approximation method for stochastic discrete optimization. SIAM J. Optim. 12(2):479–502.CrossrefGoogle Scholar
  • Kliewer N, Suhl L (2011) A note on the online nature of the railway delay management problem. Networks 57(1):28–37.CrossrefGoogle Scholar
  • Kroon L, Maróti G, Helmrich MR, Vromans M, Dekker R (2008) Stochastic improvement of cyclic railway timetables. Transportation Res. Part B 42(6):553–570.CrossrefGoogle Scholar
  • Li X, Lo HK (2014) Energy minimization in dynamic train scheduling and control for metro rail operations. Transportation Res. Part B 70:269–284.CrossrefGoogle Scholar
  • Lusby RM, Larsen J, Bull S (2018) A survey on robustness in railway planning. European J. Oper. Res. 266(1):1–15.CrossrefGoogle Scholar
  • Peña-Alcaraz M, Fernández A, Cucala AP, Ramos A, Pecharromán RR (2012) Optimal underground timetable design based on power flow for maximizing the use of regenerative-braking energy. Proc. Institution Mech. Engineers, Part F: J. Rail Rapid Transit 226(4):397–408.CrossrefGoogle Scholar
  • Raghunathan AU, Wada T, Ueda K, Takahashi S (2014) Minimizing energy consumption in railways by voltage control on substations. WIT Trans. on the Built Environment 135:697–708.Google Scholar
  • Restel F, Wolniewicz Ł, Mikulčić M (2021) Method for designing robust and energy efficient railway schedules. Energies 14(24):8248.CrossrefGoogle Scholar
  • Sansó B, Girard P (1997) Instantaneous power peak reduction and train scheduling desynchronization in subway systems. Transportation Sci. 31(4):312–323.LinkGoogle Scholar
  • Scheepmaker GM, Goverde RMP, Kroon LG (2017) Review of energy-efficient train control and timetabling. European J. Oper. Res. 257(2):355–376.CrossrefGoogle Scholar
  • Su S, Li X, Tang T, Gao Z (2013) A subway train timetable optimization approach based on energy-efficient operation strategy. IEEE Trans. 14(2):883–893.Google Scholar
  • Van Rossum G, Drake FL (2009) Python 3 Reference Manual (CreateSpace, Scotts Valley, CA)Google Scholar
  • Wang P, Goverde RMP (2019) Multi-train trajectory optimization for energy-efficient timetabling. European J. Oper. Res. 272(2):621–635.CrossrefGoogle Scholar
  • Wang Y, Zhu S, D’Ariano A, Yin J, Miao J, Meng L (2021) Energy-efficient timetabling and rolling stock circulation planning based on automatic train operation levels for metro lines. Transportation Res. Part C: Emerging Technologies 129:103209.CrossrefGoogle Scholar
  • Yin J, Tang T, Yang L, Gao Z, Ran B (2016) Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximate dynamic programming approach. Transportation Res. Part B 91:178–210.CrossrefGoogle Scholar
  • Yin J, Tang T, Yang L, Xun J, Huang Y, Gao Z (2017) Research and development of automatic train operation for railway transportation systems: A survey. Transportation Res. Part C: Emerging Technologies 85:548–572.CrossrefGoogle Scholar
  • Zhou L, Tong LC, Chen J, Tang J, Zhou X (2017) Joint optimization of high-speed train timetables and speed profiles: A unified modeling approach using space-time-speed grid networks. Transportation Res. Part B 97:157–181.CrossrefGoogle Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.