A parallel algorithm for train rescheduling

Josyula, Sai Prashanth; Krasemann, Johanna Törnquist; Lundberg, Lars

doi:10.1016/j.trc.2018.07.003

Cited by 26 publications

(27 citation statements)

References 18 publications

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“…In spite of this, limited research has been conducted in designing parallel algorithms that employ such hardware to better solve the train rescheduling problem. Recently, Josyula et al [8] report significant speedup in train rescheduling as a result of parallel exploration of multiple branches of the search tree. Further research [9] in parallel algorithms explores the potential of GPUs in train rescheduling.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…Research studies that employ concepts of parallel computing in railway research have recently been reviewed in [24]. Though the use of parallel computing for railway rescheduling has been investigated in recent years, e.g., in [1,8,9], research on parallel computing for multi-objective train rescheduling is rather scarce. In a recent work, Nitisiri et al [13] present a parallel multi-objective genetic algorithm for scheduling trains.…”

Section: Related Workmentioning

confidence: 99%

“…Based on the review of related work, some of the observed weaknesses and challenges are addressed in this paper by: (i) investigating the effectiveness of a parallel search strategy, (ii) considering up to 6 relevant objectives, and (iii) quickly reaching completion for real-world input, while train rescheduling. This paper extends an existing single-objective train rescheduling algorithm [8] for the multi-objective case, using a few concepts from an algorithmic framework [17]. The redesigned algorithm is parallelized using a parallel DFS strategy.…”

Section: Related Workmentioning

confidence: 99%

“…The sequential and the parallel heuristic algorithms for single-objective train rescheduling problems have been designed and introduced in [8]. The following provides a concise summary of the two algorithms.…”

Section: Algorithmic Design Choicesmentioning

confidence: 99%

“…The best solution obtained by the parallel algorithm is the same as that obtained by the sequential algorithm. See [8] for further details about the two algorithms.…”

Section: Algorithmic Design Choicesmentioning

confidence: 99%

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Parallel Computing for Multi-Objective Train Rescheduling

Josyula

Krasemann

Lundberg

2021

IEEE Trans. Emerg. Topics Comput.

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In railway traffic systems, it is essential to achieve a high punctuality to satisfy the goals of the involved stakeholders. Thus, whenever disturbances occur, it is important to effectively reschedule trains while considering the perspectives of various stakeholders. This typically involves solving a multi-objective train rescheduling problem, which is much more complex than its single-objective counterpart. Solving such a problem in realtime for practically relevant problem sizes is computationally challenging. The reason is that the rescheduling solution(s) of interest are dispersed across a large search tree. The tree needs to be navigated fast while pruning off branches leading to undesirable solutions and exploring branches leading to potentially desirable solutions. The use of parallel computing enables such a fast navigation of the tree. This paper presents a heuristic parallel algorithm to solve the multi-objective train rescheduling problem. The parallel algorithm combines a depth-first search with simultaneous breadth-wise tree exploration while searching the tree for solutions. An existing parallel algorithm for singleobjective train rescheduling has been redesigned, primarily, by (i) pruning based on multiple metrics, and (ii) maintaining a set of upper bounds. The redesign improved the quality of the obtained rescheduling solutions and showed better speedups for several disturbance scenarios.

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Section: Problem Descriptionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Algorithmic Design Choicesmentioning

confidence: 99%

“…The best solution obtained by the parallel algorithm is the same as that obtained by the sequential algorithm. See [8] for further details about the two algorithms.…”

Section: Algorithmic Design Choicesmentioning

confidence: 99%

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Parallel Computing for Multi-Objective Train Rescheduling

Josyula

Krasemann

Lundberg

2021

IEEE Trans. Emerg. Topics Comput.

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Multi-stage timetable rescheduling for high-speed railways: a dynamic programming approach with adaptive state generation

Feng

Cui

et al. 2021

Complex Intell. Syst.

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A dynamic programming (DP) approach with adaptive state generation and conflicts resolution is developed to address the timetable-rescheduling problem (TRP) at relatively lower computation costs. A multi-stage decision-making model is first developed to represent the timetable-rescheduling procedure in high-speed railways. Then, an adaptive state generation method by reordering the trains at each station is proposed to dynamically create the possible states according to the states of previous stages, such that the infeasible states can be removed and the search space is reduced. Then, conflicts are resolved by retiming the arrival and/or departure times of trains. Furthermore, the state transfer equation is built and Bellman equation is developed to derive the solution to minimize the total delay time (TT). A series of simulation experiments and a real-world case study are used to evaluate the performance of the proposed method. The simulation experiments indicate that the proposed method is able to find the optimal timetable with appropriate overtaking at right stations and reduce the total delay by 62.7% and 41.5% with respect to the First-Come-First-Serve (FCFS) and First-Schedule-First-Serve (FSFS) strategy that are widely used in practice. Comparing to the intelligent scheduling method (e.g., Ant Colony Optimization and Particle Swarm Optimization), similar objective performance can be achieved at a much lower cost of computation time, which make the proposed method more applicable to the TRP in daily operation of high-speed railway.

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A multi-objective and dictionary-based checking for efficient rescheduling trains

Kumar

Mishra

2021

Alexandria Engineering Journal

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A parallel algorithm for train rescheduling

Cited by 26 publications

References 18 publications

Parallel Computing for Multi-Objective Train Rescheduling

Parallel Computing for Multi-Objective Train Rescheduling

Multi-stage timetable rescheduling for high-speed railways: a dynamic programming approach with adaptive state generation

A multi-objective and dictionary-based checking for efficient rescheduling trains

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