Integrated Backup Rolling Stock Allocation and Timetable Rescheduling with Uncertain Time-Variant Passenger Demand Under Disruptive Events

References

• Alderson D, Brown G, Carlyle W (2014) Assessing and improving operational resilience of critical infrastructures and other systems. Newman A, Leung J, eds. Bridging Data and Decisions, Tutorials in Operations Research (INFORMS, Catonsville, MD), 180–215.Link, Google Scholar
• Angulo G, Ahmed S, Dey S (2016) Improving the integer L-shaped method. INFORMS J. Comput. 38(3):483–499.Link, Google Scholar
• Barrena E, Canca D, Coelho L, Laporte G (2014) Exact formulations and algorithm for the train timetabling problem with dynamic demand. Comput. Oper. Res. 44(April):66–74.Crossref, Google Scholar
• Birge J, Louveaux F (2011) Introduction to Stochastic Programming (Springer Science & Business Media, New York).Crossref, Google Scholar
• Cacchiani V, Caprara A, Fischetti M (2012a) A Lagrangian heuristic for robustness, with an application to train timetabling. Transportation Sci. 46(1):124–133.Link, Google Scholar
• Cacchiani V, Qi J, Yang L (2020) Robust optimization models for integrated train stop planning and timetabling with passenger demand uncertainty. Transportation Res. Part B 136(June):1–29.Crossref, Google Scholar
• Cacchiani V, Caprara A, Galli L, Kroon L, Maroti G, Toth P (2012b) Railway rolling stock planning: Robustness against large disruptions. Transportation Sci. 46(2):217–232.Link, Google 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(May):15–37.Crossref, Google Scholar
• Caimi G, Chudak F, Fuchsberger M, Laumanns M, Zenklusen R (2011) A new resource-constrained multicommodity flow model for conflict-free train routing and scheduling. Transportation Sci. 45(2):212–227.Link, Google Scholar
• Caprara A, Fischetti M, Toth P (2002) Modeling and solving the train timetabling problem. Oper. Res. 50(5):851–861.Link, Google Scholar
• Carøe C, Tind J (1998) L-shaped decomposition of two-stage stochastic programs with integer recourse. Math. Programming 83(1–3):451–464.Crossref, Google Scholar
• Chen L, Miller-Hooks E (2012) Resilience: An indicator of recovery capability in intermodal freight transport. Transportation Sci. 46(1):109–123.Link, Google Scholar
• Cooper K, Hunter S, Nagaraj K (2020) Biobjective simulation optimization on integer lattices using the epsilon-constraint method in a retrospective approximation framework. INFORMS J. Comput. 32(4):1080–1100.Abstract, Google Scholar
• Cordeau J, Toth P, Vigo D (1998) A survey of optimization models for train routing and scheduling. Transportation Sci. 32(4):380–404.Link, Google Scholar
• Corman F, D’Ariano A, Pacciarelli D (2010) A tabu search algorithm for rerouting trains during rail operations. Transportation Res. Part B 44(1):175–192.Crossref, Google Scholar
• Cox T, Houdmont J, Griffiths A (2006) Rail passenger crowding, stress, health and safety in Britain. Transportation Res. Part A 40(3):244–258.Google Scholar
• D’Ariano A, Pacciarelli D, Pranzo M (2007) A branch and bound algorithm for scheduling trains in a railway network. Eur. J. Oper. Res. 183(2):646–657.Google Scholar
• D’Ariano A, Corman F, Pacciarelli D, Pranzo M (2008) Reordering and local rerouting strategies to manage train traffic in real time. Transportation Sci. 42(4):405–419.Link, Google Scholar
• Dollevoet T, Corman F, D’Ariano A, Huisman D (2014a) An iterative optimization framework for delay management and train scheduling. Flexible Services Manufacturing J. 26(4):490–515.Crossref, Google Scholar
• Dollevoet T, Huisman D, Kroon L, Schmidt M, Schöbel A (2014b) Delay management including capacities of stations. Transportation Sci. 49(2):185–203.Link, Google Scholar
• Gade D, Küçükyavuz S, Sen S (2014) Decomposition algorithms with parametric Gomory cuts for two-stage stochastic integer programs. Math. Programming 144(1–2):39–64.Crossref, Google Scholar
• Ganin A, Kitsak M, Marchese D, Keisler J, Seager T, Linkov I (2017) Resilience and efficiency in transportation networks. Sci. Adv. 3(12):1–8.Crossref, Google Scholar
• Huang Y, Mannino C, Yang L, Tang T (2020) Coupling time-indexed and big-M formulations for real-time train scheduling during metro service disruptions. Transportation Res. Part B 133(March):38–61.Crossref, Google Scholar
• Ibarra-Rojas O, Giesen R, Rios-Solis Y (2014) An integrated approach for timetabling and vehicle scheduling problems to analyze the trade-off between level of service and operating costs of transit networks. Transportation Res. Part B 70(December):35–46.Crossref, Google Scholar
• Jianfei P, Yuan Y (2020) Changping Line and Batong Line open the minimum operating interval, and the congestion in the morning peak is relieved. (In Chinese.) Beijing News (March 24), https://www.bjnews.com.cn/detail/158503040714188.html.Google Scholar
• Jin JG, Teo KM, Odoni AR (2016) Optimizing bus bridging services in response to disruptions of urban transit rail networks. Transportation Sci. 50(3):790–804.Link, Google Scholar
• Jin JG, Tang L, Sun L, Lee D (2014) Enhancing metro network resilience via localized integration with bus services. Transportation Res. Part E 63(March):17–30.Crossref, Google Scholar
• Lai D, Leung JM (2018) Real-time rescheduling and disruption management for public transit. Transportmetrica B 6(1):17–33.Crossref, Google Scholar
• Lamorgese L, Mannino C (2015) An exact decomposition approach for the real-time train dispatching problem. Oper. Res. 63(1):48–64.Link, Google Scholar
• Lamorgese L, Mannino C (2019) A noncompact formulation for job-shop scheduling problems in traffic management. Oper. Res. 67(6):1586–1609.Link, Google Scholar
• Lamorgese L, Mannino C, Piacentini M (2016) Optimal train dispatching by Benders-like reformulation. Transportation Sci. 50(3):910–925.Link, Google Scholar
• Lamorgese L, Mannino C, Pacciarelli D, Krasemann JT (2018) Train dispatching. Borndörfer R, Klug T, Lamorgese L, Mannino C, Reuther M, Schlechte T, eds. Handbook of Optimization in the Railway Industry, International Series in Operations Research & Management Science, Vol. 268 (Springer, Cham, Switzerland), 265–283.Crossref, Google Scholar
• Laporte G, Louveaux F (1993) The integer L-shaped method for stochastic integer programs with complete recourse. Oper. Res. Lett. 13(3):133–142.Crossref, Google Scholar
• Laporte G, Louveaux F, Hamme L (2002) An integer L-shaped algorithm for the capacitated vehicle routing problem with stochastic demands. Oper. Res. 50(3):415–423.Link, Google Scholar
• Liu S, Kozan E (2011) Scheduling trains with priorities: A no-wait blocking parallel-machine job-shop scheduling model. Transportation Sci. 45(2):175–198.Link, Google Scholar
• Louwerse I, Huisman D (2014) Adjusting a railway timetable in case of partial or complete blockades. Eur. J. Oper. Res. 253(3):583–593.Crossref, Google Scholar
• Lu M, Chen Z, Shen S (2017) Optimizing the profitability and quality of service in carshare systems under demand uncertainty. Manufacturing Service Oper. Management 20(2):162–180.Link, Google Scholar
• Mannino C, Mascis A (2009) Optimal real-time traffic control in metro stations. Oper. Res. 57(4):1026–1039.Link, Google Scholar
• Martin-Iradi B, Ropke S (2022) A column-generation-based matheuristic for periodic and symmetric train timetabling with integrated passenger routing. Eur. J. Oper. Res. 297(2):511–531.Crossref, Google Scholar
• Mascis A, Pacciarelli D (2002) Job-shop scheduling with blocking and no-wait constraints. Eur. J. Oper. Res. 143(3):498–517.Crossref, Google Scholar
• Niu H, Zhou X (2013) Optimizing urban rail timetable under time-dependent demand and oversaturated conditions. Transportation Res. Part C 36(November):212–230.Crossref, Google Scholar
• Ortega F, Pozo M, Puerto J (2018) On-line timetable rescheduling in a transit line. Transportation Sci. 52(5):1106–1121.Link, Google Scholar
• Pender B, Currie G, Delbosc A, Shiwakoti N (2012) Planning for the unplanned: An international review of current approaches to service disruption management of railways. Proc. 35th Australasian Transport Res. Forum (Western Australia Department of Transport, Perth), 1–17.Google Scholar
• Pu S, Zhan S (2021) Two-stage robust railway line-planning approach with passenger demand uncertainty. Transportation Res. Part E 152(August):102372.Crossref, Google Scholar
• Rader B, Scarpino SV, Nande A, Hill AL, Adlam B, Reiner RC, Pigott DB, et al. (2020) Crowding and the epidemic intensity of COVID-19 transmission. Nature Medicine 26(December):1829–1834.Google Scholar
• Rahmaniani R, Crainic T, Gendreau M, Rei W (2017) The Benders decomposition algorithm: A literature review. Eur. J. Oper. Res. 259(3):801–817.Crossref, Google Scholar
• Riis M, Andersen K (2002) Capacitated network design with uncertain demand. INFORMS J. Comput. 14(3):247–260.Link, Google Scholar
• Sánchez-Martínez GE, Koutsopoulos H, Wilson NHM (2016) Real-time holding control for high-frequency transit with dynamics. Transportation Res. Part B 83(January):1–19.Crossref, Google Scholar
• Scheepmaker GM, Goverde RMP, Kroon L (2017) Review of energy-efficient train control and timetabling. Eur. J. Oper. Res. 257(2):355–376.Crossref, Google Scholar
• Sen S, Higle JL (2005) The C3 theorem and a D2 algorithm for large scale stochastic mixed-integer programming: Set convexification. Math. Programming 104(1):1–20.Crossref, Google Scholar
• Törnquist J, Persson J (2007) N-track railway traffic re-scheduling during disturbances. Transportation Res. Part B 41(3):342–362.Crossref, Google Scholar
• Van der Hurk E, Kroon L, Maróti G (2018) Passenger advice and rolling stock rescheduling under uncertainty for disruption management. Transportation Sci. 52(6):1391–1411.Link, Google Scholar
• Veelenturf LP, Martin PK, Cacchiani V, Kroon L, Toth P (2015) A railway timetable rescheduling approach for handling large-scale disruptions. Transportation Sci. 50(3):841–862.Link, Google Scholar
• Wang Y (2014) Optimal trajectory planning and train scheduling for railway systems. PhD thesis, Delft University of Technology, Delft, Netherlands.Google Scholar
• Wang K, Jacquillat A (2020) A stochastic integer programming approach to air traffic scheduling and operations. Oper. Res. 68(5):1375–1402.Link, Google Scholar
• Wong R, Yuen T, Fung K, Leung J (2008) Optimizing timetable synchronization for rail mass transit. Transportation Sci. 41(1):57–69.Link, Google Scholar
• Yin J, D’Ariano A, Wang Y, Yang L, Tang T (2021) Timetable coordination in a rail transit network with time-dependent passenger demand. Eur. J. Oper. Res. 295(1):183–202.Crossref, Google Scholar
• Yin J, Tang T, Yang L, Gao Z, Ran B (2016) Energy-efficient metro train rescheduling with uncertain time-variant passenger demands: An approximated dynamic programming approach. Transportation Res. Part B 91(September):178–210.Crossref, Google Scholar
• Yin J, Yang L, Tang T, Gao Z, Ran B (2017a) Dynamic passenger demand oriented metro train scheduling with energy-efficiency and waiting time minimization: Mixed-integer linear programming approaches. Transportation Res. Part B 97(March):182–213.Crossref, Google Scholar
• Yin J, Wang Y, Tang T, Xun J, Su S (2017b) Metro train rescheduling by adding backup trains under disrupted scenarios. Frontiers Engrg. Management 4(4):418–427.Crossref, Google Scholar
• Yin J, Tang T, Yang L, Xun J, Su S, Wang Y (2018) A two-stage stochastic optimization model for passenger-oriented metro rescheduling with backup trains. Proc. 21st IEEE Internat. Conf. Intelligent Transportation Systems (IEEE, Piscataway, NJ), 2315–2320.Google Scholar
• Zhan S, Kroon L, Veelenturf L, Wagenaar J (2015) Real-time high-speed train rescheduling in case of a complete blockage. Transportation Res. Part B 78(August):182–201.Crossref, Google Scholar
• Zhang W, Rahimian H, Bayraksan G (2016) Decomposition algorithms for risk-averse multistage stochastic programs with application to water allocation under uncertainty. INFORMS J. Comput. 28(3):385–404.Link, Google Scholar
• Zou J (2017) Large scale multistage stochastic integer programming with applications in electric power systems. PhD thesis, Georgia Institute of Technology, Atlanta.Google Scholar