The train schedule usually includes train stop schedule,routing scheme and formation scheme.It is the basis of subway transportation.Combining the practical experience of transport organizations and the principle of t...The train schedule usually includes train stop schedule,routing scheme and formation scheme.It is the basis of subway transportation.Combining the practical experience of transport organizations and the principle of the best match between transport capacity and passenger flow demand,taking the minimum value of passenger travel costs and corporation operating costs as the goal,considering the constraints of the maximum rail capacity,the minimum departure frequency and the maximum available electric multiple unit,an optimization model for city subway Y-type operation mode is constructed to determine the operation section of mainline as well as branch line and the train frequency of the Y-type operation mode.The particle swarm optimization(PSO)algorithm based on classification learning is used to solve the model,and the effectiveness of the model and algorithm is verified by a practical case.The results show that the length of branch line in Y-type operation affects the cost of waiting time of passengers significantly.展开更多
This paper presents the formulation and solution of a train routing and makeupmodel.This formulation results in a large scale 0-1 integer programmingproblem with nonlinear objective function, and linear and nonlinearc...This paper presents the formulation and solution of a train routing and makeupmodel.This formulation results in a large scale 0-1 integer programmingproblem with nonlinear objective function, and linear and nonlinearcoastraints. We know this problem is NP-complete;hence,it is diffieult to solvefor a globelly optimal solution. In this paper we propose a simulated annealingalgorithm for the train routing and makeup problem. This method avoidsentrapment in the local minima of the objective function. The effectiveness ofthis approach has been demonstrasted by results from test casee,展开更多
Meta-heuristic evolutionary algorithms have become widely used for solving complex optimization problems.However,their effectiveness in real-world applications is often limited by the need for many evaluations,which c...Meta-heuristic evolutionary algorithms have become widely used for solving complex optimization problems.However,their effectiveness in real-world applications is often limited by the need for many evaluations,which can be both costly and time-consuming.This is especially true for large-scale transportation networks,where the size of the problem and the high computational cost can hinder the algorithm’s performance.To address these challenges,recent research has focused on using surrogate-assisted models.These models aim to reduce the number of expensive evaluations and improve the efficiency of solving time-consuming optimization problems.This paper presents a new two-layer Surrogate-Assisted Fish Migration Optimization(SA-FMO)algorithm designed to tackle high-dimensional and computationally heavy problems.The global surrogate model offers a good approximation of the entire problem space,while the local surrogate model focuses on refining the solution near the current best option,improving local optimization.To test the effectiveness of the SA-FMO algorithm,we first conduct experiments using six benchmark functions in a 50-dimensional space.We then apply the algorithm to optimize urban rail transit routes,focusing on the Train Routing Optimization problem.This aims to improve operational efficiency and vehicle turnover in situations with uneven passenger flow during transit disruptions.The results show that SA-FMO can effectively improve optimization outcomes in complex transportation scenarios.展开更多
A station carrying capacity calculation method based on the station blocking time method is proposed.According to the station track circuit groups,signal system and station topological structure,the station blocks are...A station carrying capacity calculation method based on the station blocking time method is proposed.According to the station track circuit groups,signal system and station topological structure,the station blocks are divided and the division principle is proposed.Then the train routes model is built based on the station blocking time method.The calculation methods of the train arrival headway and departure headway at the station are redefined.The optimal capacity calculation model and algorithm based on a given train operation plan are studied by analyzing the optimal operation sequence of trains with different train speeds and operation types.With the case study of Jinan West Railway Station of Beijing-Shanghai high-speed railway in China,the minimum arrival headway between two trains with the speeds of 300 and 250 km/h can be reduced to 3.0 and 2.7 min,respectively.The maximum calculation results of the calculation methods can be increased from 13 train/h to 16 train/h.This method can increase the number of trains within a period of time in a station while meeting the transport organization and passenger service requirements.展开更多
文摘The train schedule usually includes train stop schedule,routing scheme and formation scheme.It is the basis of subway transportation.Combining the practical experience of transport organizations and the principle of the best match between transport capacity and passenger flow demand,taking the minimum value of passenger travel costs and corporation operating costs as the goal,considering the constraints of the maximum rail capacity,the minimum departure frequency and the maximum available electric multiple unit,an optimization model for city subway Y-type operation mode is constructed to determine the operation section of mainline as well as branch line and the train frequency of the Y-type operation mode.The particle swarm optimization(PSO)algorithm based on classification learning is used to solve the model,and the effectiveness of the model and algorithm is verified by a practical case.The results show that the length of branch line in Y-type operation affects the cost of waiting time of passengers significantly.
文摘This paper presents the formulation and solution of a train routing and makeupmodel.This formulation results in a large scale 0-1 integer programmingproblem with nonlinear objective function, and linear and nonlinearcoastraints. We know this problem is NP-complete;hence,it is diffieult to solvefor a globelly optimal solution. In this paper we propose a simulated annealingalgorithm for the train routing and makeup problem. This method avoidsentrapment in the local minima of the objective function. The effectiveness ofthis approach has been demonstrasted by results from test casee,
基金supported by the National Natural Science Foundation of China(Project No.52172321,52102391)Sichuan Province Science and Technology Innovation Talent Project(2024JDRC0020)+1 种基金China Shenhua Energy Company Limited Technology Project(GJNY-22-7/2300-K1220053)Key science and technology projects in the transportation industry of the Ministry of Transport(2022-ZD7-132).
文摘Meta-heuristic evolutionary algorithms have become widely used for solving complex optimization problems.However,their effectiveness in real-world applications is often limited by the need for many evaluations,which can be both costly and time-consuming.This is especially true for large-scale transportation networks,where the size of the problem and the high computational cost can hinder the algorithm’s performance.To address these challenges,recent research has focused on using surrogate-assisted models.These models aim to reduce the number of expensive evaluations and improve the efficiency of solving time-consuming optimization problems.This paper presents a new two-layer Surrogate-Assisted Fish Migration Optimization(SA-FMO)algorithm designed to tackle high-dimensional and computationally heavy problems.The global surrogate model offers a good approximation of the entire problem space,while the local surrogate model focuses on refining the solution near the current best option,improving local optimization.To test the effectiveness of the SA-FMO algorithm,we first conduct experiments using six benchmark functions in a 50-dimensional space.We then apply the algorithm to optimize urban rail transit routes,focusing on the Train Routing Optimization problem.This aims to improve operational efficiency and vehicle turnover in situations with uneven passenger flow during transit disruptions.The results show that SA-FMO can effectively improve optimization outcomes in complex transportation scenarios.
基金The National Natural Science Foundation of China(No.51668048)the Natural Science Foundation of Inner M ongolia(No.2017BS0501)
文摘A station carrying capacity calculation method based on the station blocking time method is proposed.According to the station track circuit groups,signal system and station topological structure,the station blocks are divided and the division principle is proposed.Then the train routes model is built based on the station blocking time method.The calculation methods of the train arrival headway and departure headway at the station are redefined.The optimal capacity calculation model and algorithm based on a given train operation plan are studied by analyzing the optimal operation sequence of trains with different train speeds and operation types.With the case study of Jinan West Railway Station of Beijing-Shanghai high-speed railway in China,the minimum arrival headway between two trains with the speeds of 300 and 250 km/h can be reduced to 3.0 and 2.7 min,respectively.The maximum calculation results of the calculation methods can be increased from 13 train/h to 16 train/h.This method can increase the number of trains within a period of time in a station while meeting the transport organization and passenger service requirements.
