An integrated optimization approach of high-speed railway line plan to network load-balancing

Hu, Xingzhu; Wang, Hongwei; Wu, Xingtang; Zhou, Min; Dong, Hairong

doi:10.1109/cac51589.2020.9327177

Cited by 1 publication

(6 citation statements)

References 13 publications

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“…For the scope of these studies, some studies focused on a railway line and some studies As mentioned above, train 2 and train 5 share the same train sets. If train 2 is delayed for a short period of time, considering the rest turnback time is enough to finish necessary operations, train 5 will not be influenced by the delay, as shown in Figure 3. But if train 2 is delayed for a long period of time, considering the rest of the connecting time is not enough to finish necessary operations, the operator will have to dispatch a standby train set to undertake the operation plan of train 5 , or train 5 will also be delayed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…If train 2 is delayed for a short period of time, considering the rest turnback time is enough to finish necessary operations, train 5 will not be influenced by the delay, as shown in Figure 3. But if train 2 is delayed for a long period of time, considering the rest of the connecting time is not enough to finish necessary operations, the operator will have to dispatch a standby train set to undertake the operation plan of train 5 , or train 5 will also be delayed. So, we can see that the train timetable and train set circulation plan are closely related to the rescheduling problems, and it is necessary to reschedule these two aspects together.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Train operation rescheduling problems on a railway network are also of vital importance. Hu XZ et al [5] present a mixed-integer nonlinear optimization model of a high-speed railway line planning problem, integrating the operation route plan, train stop plan, and passenger distribution plan. Moreover, a two-stage hybrid heuristic search algorithm is designed to solve this model.…”

Section: Literature Reviewmentioning

confidence: 99%

“…From the perspective of the algorithm, many algorithms are widely applied to solve the rescheduling problem, such as [20] branch and price algorithm, [25] ant colony algorithm, [26] heuristic simulated-annealing algorithm, etc. [1,5]. Both use particle swarm optimization algorithms; the difference is that [1] used particle swarm with quantuminspired algorithms, and [5] used particle swarm optimization algorithms with genetic algorithm (GA).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Different colorful lines represent different trains. There are five trains in the figure: Train 1 departs from station A to station I, train 2 departs from station A to station L, train 3 departs from station A to station F, train 4 departs from station A to station D, and train 5 departs from station L to station F. Trains 1 , 2 , and 5 are cross-line trains, and train 3 only departs on the main line. Train 2 and train 5 are connected by a black line segment, meaning that train 5 and train 2 share the same train sets.…”

Section: Introductionmentioning

confidence: 99%

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An Integrated Optimization Method of High-Speed Railway Rescheduling Problem at the Network Level

Zhao,

Zhou,

Guo

et al. 2023

Applied Sciences

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For high-speed railway operations at the network level, unforeseen events that lead to operation interruptions are inevitable, which should be handled within a short period of time to reduce the influence of the events as much as possible. This paper introduces an integrated optimization method to deal with rescheduling problems at the railway network level under emergencies, rescheduling the train timetable, and utilizing the train sets. train set A three-objective optimization model is proposed with the aim of minimizing additional operation costs, total delay, and the number of transfer passengers. Then, an algorithm based on NSGA-III is proposed to solve the model. Computational experiments on real data are conducted to show the adaptability of the model and algorithm. The average optimization rate of the three objectives is 12.12%, 14.12%, and 10.57%, indicating the effectiveness of the method. Moreover, more experiments on a railway network in China are being conducted to analyze which section and which time have the greatest impact on the railway network when emergencies occur. According to the experiment, the bottleneck section is section 15, and the bottleneck time is 11:00 am. In addition, the importance of all the depots is discussed, and depot II is selected as the most important depot.

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Section: Literature Reviewmentioning

confidence: 99%