Dynamic optimization of high-speed railway structure based on Non-dominated Sorting Genetic Algorithm-II algorithm

2021 ◽  
Author(s):  
Donglin Yao ◽  
Yongxiao Liu ◽  
Yuanbo Zhang ◽  
Xianghui Fan
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Optimization ◽  
High Speed ◽  
High Speed Railway

scholarly journals Optimizing Train-Set Circulation Plan in High-Speed Railway Networks Using Genetic Algorithm

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Yun Wang ◽  
Yu Zhou ◽  
Xuedong Yan
Keyword(s):  
Genetic Algorithm ◽  
High Speed ◽  
Sustainable Transportation ◽  
Utilization Efficiency ◽  
Practical Significance ◽  
Railway Transportation ◽  
High Speed Railway ◽  
Utilization Scheme ◽  
The One ◽  
New Research

As a sustainable transportation mode, high-speed railway (HSR) has been developing rapidly during the past decade in China. With the formation of dense HSR network, how to improve the utilization efficiency of train-sets (the carrying tools of HSR) has been a new research hotspot. Moreover, the emergence of railway transportation hubs has brought great challenges to the traditional train-sets’ utilization mode. Thus, in this paper, we address the issue of train-sets’ utilization problem with the consideration of railway transportation hubs, which consists of finding an optimal Train-set Circulation Plan (TCP) to complete trip tasks in a given Train Diagram (TD). An integer programming TCP model is established to optimize the train-set utilization scheme, aiming to obtain the one-to-one correspondence relationship among sets of train-sets, trip tasks, and maintenances. A genetic algorithm (GA) is designed to solve the model. A case study based on Nanjing and Shanghai HSR transportation hubs is made to demonstrate the practical significance of the proposed method. The results show that a more efficient TCP can be formulated by introducing train-sets being dispatched among different stations in the same hub.

scholarly journals Application of Multiple-Population Genetic Algorithm in Optimizing the Train-Set Circulation Plan Problem

Complexity ◽  
2017 ◽  
Vol 2017 ◽  
pp. 1-14 ◽  
Author(s):  
Yu Zhou ◽  
Leishan Zhou ◽  
Yun Wang ◽  
Zhuo Yang ◽  
Jiawei Wu
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing Problem ◽  
Population Genetic ◽  
High Speed ◽  
Rolling Stock ◽  
High Speed Railway ◽  
Routing Problem ◽  
Multiple Population ◽  
Population Genetic Algorithm ◽  
Maintenance Mode

The train-set circulation plan problem (TCPP) belongs to the rolling stock scheduling (RSS) problem and is similar to the aircraft routing problem (ARP) in airline operations and the vehicle routing problem (VRP) in the logistics field. However, TCPP involves additional complexity due to the maintenance constraint of train-sets: train-sets must conduct maintenance tasks after running for a certain time and distance. The TCPP is nondeterministic polynomial hard (NP-hard). There is no available algorithm that can obtain the optimal global solution, and many factors such as the utilization mode and the maintenance mode impact the solution of the TCPP. This paper proposes a train-set circulation optimization model to minimize the total connection time and maintenance costs and describes the design of an efficient multiple-population genetic algorithm (MPGA) to solve this model. A realistic high-speed railway (HSR) case is selected to verify our model and algorithm, and, then, a comparison of different algorithms is carried out. Furthermore, a new maintenance mode is proposed, and related implementation requirements are discussed.

scholarly journals Genetic Algorithm-Based Particle Swarm Optimization Approach to Reschedule High-Speed Railway Timetables: A Case Study in China

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Mingming Wang ◽  
Li Wang ◽  
Xinyue Xu ◽  
Yong Qin ◽  
Lingqiao Qin
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
High Speed ◽  
Particle Swarm ◽  
Mixed Integer ◽  
Swarm Optimization ◽  
High Speed Railway ◽  
Service Strategy ◽  
Delay Propagation

In this study, a mixed integer programming model is proposed to address timetable rescheduling problem under primary delays. The model considers timetable rescheduling strategies such as retiming, reordering, and adjusting stop pattern. A genetic algorithm-based particle swarm optimization algorithm is developed where position vector and genetic evolution operators are reconstructed based on departure and arrival time of each train at stations. Finally, a numerical experiment of Beijing-Shanghai high-speed railway corridor is implemented to test the proposed model and algorithm. The results show that the objective value of proposed method is decreased by 15.6%, 48.8%, and 25.7% compared with the first-come-first-service strategy, the first-schedule-first-service strategy, and the particle swarm optimization, respectively. The gap between the best solution obtained by the proposed method and the optimum solution computed by CPLEX solver is around 19.6%. All delay cases are addressed within acceptable time (within 1.5 min). Moreover, the case study gives insight into the correlation between delay propagation and headway. The primary delays occur in high-density period (scheduled headway closes to the minimum headway), which results in a great delay propagation.

Railway Capacity Allocation Modeling Using a Genetic Algorithm

2017 ◽  
Vol 2608 (1) ◽  
pp. 115-124
Author(s):  
Hyunseung Kim ◽  
In-Jae Jeong ◽  
Dongjoo Park
Keyword(s):  
Genetic Algorithm ◽  
South Korea ◽  
High Speed ◽  
Rational Solution ◽  
Capacity Allocation ◽  
High Speed Railway ◽  
South Korean ◽  
Transport Services ◽  
Proposed Model ◽  
Railway Capacity

The South Korean government has established guidelines for railway capacity allocation. Railway transport services are provided by a monopoly company, which together with the guidelines, has hampered research into railway capacity allocation in South Korea. Recently, a new high-speed railway company has been established. Therefore, there is a pressing need for a fair and objective railway capacity allocation procedure. A model was developed to be applicable to South Korean high-speed railway capacity allocation, which is optimized by viewing the railway network as a location–time network. Because railway capacity allocation in South Korea is an administered process, various requirements must be followed; the model uses a genetic algorithm for such requirements. Two test scenarios were used to validate the proposed model, the solution to which resolves more than 70% of conflicts within 20 iterations (148 min). When an attempt is made to schedule infeasible trains compulsively, it is impossible to do so without relaxing one or more constraints. The average headway among real operating trains is very close to the results of the analysis. The proposed model with a genetic algorithm is a rational solution.

Layout scheme of high-speed railway transfer hubs: bi-level modeling and hybrid genetic algorithm approach

2018 ◽  
Vol 22 (S5) ◽  
pp. 12551-12566 ◽  
Author(s):  
Lu Tong ◽  
Lei Nie ◽  
Gen-cai Guo ◽  
Nuan-nuan Leng ◽  
Ruo-xi Xu
Keyword(s):  
Genetic Algorithm ◽  
High Speed ◽  
Hybrid Genetic Algorithm ◽  
High Speed Railway ◽  
Genetic Algorithm Approach

Crew Planning Optimization Model of High-Speed Railway

2013 ◽  
Vol 869-870 ◽  
pp. 298-304 ◽  
Author(s):  
Jin Mei Li ◽  
Lei Nie
Keyword(s):  
Genetic Algorithm ◽  
High Speed ◽  
Simulated Annealing Algorithm ◽  
Set Covering ◽  
High Speed Railway ◽  
Planning Optimization ◽  
Proposed Model ◽  
Phase Setting ◽  
Crew Planning ◽  
Covering Model

Crew planning with complicated constraints is decomposed into two sequential phases: crew scheduling phase, crew rostering phase. Setting a dynamic model based on set covering model, Genetic Algorithm is adopted based on feasible solution range in search of optimal scheduling set with minimum time. Constructing a node-arc TSP network, it adopts Genetic Algorithm and Simulated Annealing Algorithm to create a work roster. Based on Wuhan-Guangzhou High-Speed Railway in China, the balance degree of crew planning is measured by crew working time entropy. The proposed model proves strong practical application.

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