A feasible timetable generator simulation modelling framework for train scheduling problem

Yalçınkaya, Özgür; Bayhan, G. Miraç

doi:10.1016/j.simpat.2011.09.005

Cited by 20 publications

(8 citation statements)

References 20 publications

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“…[57] In 2012 Bayhan with one other proposed simulation model approach for a feasible timetable generator framework for railway scheduling problem. [30] In 2013 Corman with other three proposed iterative optimization framework for railway schedule using alternative graph method. [29] In 2014 Ariano with three other proposed MILP based AGLIBRARY advanced technique for practical real time scheduling and evaluate applicability in railway.…”

Section: Related Workmentioning

confidence: 99%

Towards Self Sustainable Railway Transportation System!

Upadhyay¹,

Joshi²

2015

CAE

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This paper is focused on numerous challenges holding by Railway Transportation systems and various techniques to solve problems. Scheduling is one of the challenges in Railway transportation system which depends on numerous constraints. This paper consists of discussion of deadlock free schedule using Agent based system and expert system for the railway transportation system to minimize overall system delay by considering other constrain depend on this. Despite having Persist enhancement in approaches still have limitations which require improvement to meet the objective of the entire railway transportation system.

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Section: Related Workmentioning

confidence: 99%

Towards Self Sustainable Railway Transportation System!

Upadhyay¹,

Joshi²

2015

CAE

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“…Constraint (11) enforces that every event must use exactly one track per relevant segment. Constraints (12) and (13) ensure that the entering and departing trains at segment 1 must select track 1 and track 2, respectively. Constraint (14) ensures that if a train enters segment 3, it must select the same track ID of the foregoing event.…”

Section: Objective Functionmentioning

confidence: 99%

“…Yet, the train scheduling problem in a rail transit system is quite complex [6]. Numerous proposed scheduling models and algorithms have addressed different but related optimization objectives, including capacity, route service, minimal delay, passenger demand, operating cost, and transit unit (TU) circulation [7][8][9][10][11][12]. However, although the parameters of minimum headway and layover time at terminals were considered in these studies, other important factors, such as the tail track allocation strategy and delay recovery time distribution, were not included in these studies.…”

Section: Introductionmentioning

confidence: 99%

Turnback Capacity Assessment and Delay Management at a Rail Transit Terminal with Two-Tail Tracks

Jiang¹,

Tan

Wang³

et al. 2015

Mathematical Problems in Engineering

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Terminal capacity and performance have become a major concern for rail transit agencies in China due to the ever increasing passenger demand. This paper develops a mixed integer programming (MIP) optimization model to estimate the turnback capacity and performance of a rail transit terminal with two-tail tracks. The capacity evaluation and delay propagation are described and assessed as anN-track integrated model with minimal time span and train delay. Operations and design parameters such as tail track allocation strategies, maximum layover time, headway pattern, buffer time distribution scheme, and primary delay are also considered in this model. The effectiveness of the model is tested by a case study with computation results drawn from one terminal station in Shanghai, China. The case study results show that unfixed platform time and flexible tail track allocation strategies can improve the capacity of turnback operation, and the strategy of allowing swapping of the tail tracks has a significantly positive impact on delay absorption.

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“…Yang et al [12] explored the fuzzy and stochastic programming for freight transportation and utilized genetic algorithm to solve the chance constraint programming model. Yalçinkaya and Bayhan [13] put up a random simulation approach to produce feasible timetables. Krasemann [14] proposed a greedy algorithm using depth-first searching and evaluation function ordering to find the satisfactory solution of a train scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

Models and Algorithms of Conflict Detection and Scheduling Optimization for High-Speed Train Operations Based on MPC

Wang

Zhou

Deng

2018

Journal of Control Science and Engineering

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The high-speed train with high transport speed and punctuality rate has become a leading tool of daily traveling. Compared with the general-speed railway transportation, the high-speed railway transportation requires a timely and automatic adjustment capability in the centralized traffic control (CTC) system. In order to improve the capability, this paper mainly explores the models and algorithms of conflict detection and scheduling optimization of high-speed train operations. They are implemented under the mechanism of model predictive control (MPC) over the rolling prediction horizon. A train movement prediction model with conflict resolution is established. The mechanism of conflict detection and scheduling optimization is implemented through a case study. The simulation results demonstrate the effectiveness of the proposed mechanism.

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A feasible timetable generator simulation modelling framework for train scheduling problem

Cited by 20 publications

References 20 publications

Towards Self Sustainable Railway Transportation System!

Towards Self Sustainable Railway Transportation System!

Turnback Capacity Assessment and Delay Management at a Rail Transit Terminal with Two-Tail Tracks

Models and Algorithms of Conflict Detection and Scheduling Optimization for High-Speed Train Operations Based on MPC

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