A column-generation-based matheuristic for periodic and symmetric train timetabling with integrated passenger routing

Martin-Iradi, Bernardo; Røpke, Stefan

doi:10.1016/j.ejor.2021.04.041

Cited by 17 publications

(3 citation statements)

References 38 publications

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“…Several other works apply column generation for heuristic search without explicitly mentioning Dantzig–Wolfe (DW) decomposition. The addressed range of problems is wide, including technician routing (Dupin et al., 2021), train timetabling (Martin‐Iradi and Ropke, 2022), electric vehicle routing (Echeverri et al., 2019), city logistics (Boschetti and Maniezzo, 2015) and planning in bulk ports (de Andrade and Menezes, 2023) among others.…”

Section: Methods Based On Decompositionmentioning

confidence: 99%

Matheuristics: survey and synthesis

Boschetti

Letchford

Maniezzo

2023

Int Trans Operational Res

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In integer programming and combinatorial optimisation, people use the term matheuristics to refer to methods that are heuristic in nature but draw on concepts from the literature on exact methods. We survey the literature on this topic, with a particular emphasis on matheuristics that yield both primal and dual bounds (i.e., upper and lower bounds in the case of a minimisation problem). We also make some comments about possible future developments.

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Section: Methods Based On Decompositionmentioning

confidence: 99%

Matheuristics: survey and synthesis

Boschetti

Letchford

Maniezzo

2023

Int Trans Operational Res

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“…Since TTR was proposed in the 1970s (Szpigel, 1973), most researchers usually establish the integer linear programming model based on train operation constraints in block sections and stations. The main optimization approaches of TTR comprise operational research (Zhu & Goverde, 2021;Zhan et al, 2021;Luan & Corman, 2022;Martin-Iradi & Ropke, 2022), evolutionary algorithms (Han et al, 2021;Tang et al, 2021;Pascariu et al, 2022;Wang et al, 2022b) and reinforcement learning ( Semrov, Marseti c, Zura, Todorovski, & Srdic, 2016;Khadilkar, 2018;Wang, Zhou, Li, Zhang, & Dong, 2019). Operational research can obtain an optimal rescheduling solution with branch and bound rules.…”

Section: Introductionmentioning

confidence: 99%

“…The main optimization approaches of TTR comprise operational research (Zhu & Goverde, 2021; Zhan et al. , 2021; Luan & Corman, 2022; Martin-Iradi & Ropke, 2022), evolutionary algorithms (Han et al. , 2021; Tang et al.…”

Section: Introductionmentioning

confidence: 99%

A train timetable rescheduling approach based on multi-train tracking optimization of high-speed railways

Wang,

Zhang,

Yuan

et al. 2023

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PurposeThis paper aims to propose a train timetable rescheduling (TTR) approach from the perspective of multi-train tracking optimization based on the mutual spatiotemporal information in the high-speed railway signaling system.Design/methodology/approachFirstly, a single-train trajectory optimization (STTO) model is constructed based on train dynamics and operating conditions. The train kinematics parameters, including acceleration, speed and time at each position, are calculated to predict the arrival times in the train timetable. A STTO algorithm is developed to optimize a single-train time-efficient driving strategy. Then, a TTR approach based on multi-train tracking optimization (TTR-MTTO) is proposed with mutual information. The constraints of temporary speed restriction (TSR) and end of authority are decoupled to calculate the tracking trajectory of the backward tracking train. The multi-train trajectories at each position are optimized to generate a time-efficient train timetable.FindingsThe numerical experiment is performed on the Beijing-Tianjin high-speed railway line and CR400AF. The STTO algorithm predicts the train’s planned arrival time to calculate the total train delay (TTD). As for the TSR scenario, the proposed TTR-MTTO can reduce TTD by 60.60% compared with the traditional TTR approach with dispatchers’ experience. Moreover, TTR-MTTO can optimize a time-efficient train timetable to help dispatchers reschedule trains more reasonably.Originality/valueWith the cooperative relationship and mutual information between train rescheduling and control, the proposed TTR-MTTO approach can automatically generate a time-efficient train timetable to reduce the total train delay and the work intensity of dispatchers.

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