The multi-objective railway timetable rescheduling problem

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.

Section: Headway Constraintsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Wang

et al. 2019

“…Binder et al [16] proposed an ILP model with three objectives: the passenger satisfaction, the operational costs, and the deviation from the scheduled timetable. Strategies include canceling, delaying, rerouting the trains, and scheduling emergency trains.…”

Section: Timetablementioning

confidence: 99%

A Practical Method for Timetable Rescheduling in Subway Networks during the End-of-Service Period

Zhao

Ning

2018

This study proposes a biobjective optimization method for timetable rescheduling during the end-of-service period of a subway network, taking all stakeholders' interests into consideration. We seek to minimize the total transfer waiting time for all transfer passengers, meanwhile minimizing the deviation to the scheduled timetable. The -constraint method and linearization techniques are utilized to obtain the approximate Pareto optimal solutions within limited seconds, allowing for figuring out the trade-off between the two objectives. The method is validated by numerical experiments for different delay scenarios based on a real-world case: the Beijing subway network.

“…Disturbances in metro networks such as temporary platform blockages make o ine schedules suboptimal for use. Hence, various methods of Train Timetable Rescheduling (TTR) [2][3][4][5][6] have been proposed to handle unforeseen events which may disturb timetable. Prevailing methods for timetable rescheduling can be classi ed into two categories: passenger-oriented and train-oriented.…”

Section: Introductionmentioning

confidence: 99%

“…: conversion e ciency of the train braking system (from mechanical energy to electrical energy) 3. : feedback coe cient on braking energy.…”

mentioning

confidence: 99%

A Real-Time Timetable Rescheduling Method for Metro System Energy Optimization under Dwell-Time Disturbances

Yang

Wang

Zhang

et al. 2019

Automatic Train Systems (ATSs) have attracted much attention in recent years. A reliable ATS can reschedule timetables adaptively and rapidly whenever a possible disturbance breaks the original timetable. Most research focuses the timetable rescheduling problem on minimizing the overall delay for trains or passengers. Few have been focusing on how to minimize the energy consumption when disturbances happen. In this paper, a real-time timetable rescheduling method (RTTRM) for energy optimization of metro systems has been proposed. e proposed method takes little time to recalculate a new schedule and gives proper solutions for all trains in the network immediately a er a random disturbance happens, which avoids possible chain reactions that would attenuate the reuse of regenerative energy. e real-time feature and self-adaptability of the method are attributed to the combinational use of Genetic Algorithm (GA) and Deep Neural Network (DNN). e decision system for proposing solutions, which contains multiple DNN cells with same structures, is trained by GA results. RTTRM is upon the foundation of three models for metro networks: a control model, a timetable model and an energy model. Several numerical examples tested on Shanghai Metro Line 1 (SML1) validate the energy saving e ects and real-time features of the proposed method.