Real-time energy-saving metro train rescheduling with primary delay identification

This paper aims to reschedule online metro trains in delay scenarios. A graph representation and a mixed integer programming model are proposed to formulate the optimization problem. The solution approach is a two-stage optimization method. In the first stage, based on a proposed train state graph a...

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Bibliographic Details
Published in:PloS one 2018-02, Vol.13 (2), p.e0192792-e0192792
Main Authors: Huang, Hangfei, Li, Keping, Schonfeld, Paul
Format: Article
Language:English
Subjects:
Algorithms
Beijing
Biology and Life Sciences
Case studies
China
Computer and Information Sciences
Conservation of Energy Resources - economics
Decision making
Decision Making, Organizational
Delay
Energy conservation
Energy consumption
Energy management
Energy use
Genetic algorithms
Graphical representations
Integer programming
Internet
Laboratories
Medicine and Health Sciences
Methods
Mixed integer
Optimization
Physical Sciences
Problems
Railroads
Railway networks
Rescheduling
Research and Analysis Methods
Scheduling
Sensitivity analysis
Social Sciences
Subway cars
Subways
Systems analysis
Traffic congestion
Traffic control
Travel
United States
Urbanization
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Summary:This paper aims to reschedule online metro trains in delay scenarios. A graph representation and a mixed integer programming model are proposed to formulate the optimization problem. The solution approach is a two-stage optimization method. In the first stage, based on a proposed train state graph and system analysis, the primary and flow-on delays are specifically analyzed and identified with a critical path algorithm. For the second stage a hybrid genetic algorithm is designed to optimize the schedule, with the delay identification results as input. Then, based on the infrastructure data of Beijing Subway Line 4 of China, case studies are presented to demonstrate the effectiveness and efficiency of the solution approach. The results show that the algorithm can quickly and accurately identify primary delays among different types of delays. The economic cost of energy consumption and total delay is considerably reduced (by more than 10% in each case). The computation time of the Hybrid-GA is low enough for rescheduling online. Sensitivity analyses further demonstrate that the proposed approach can be used as a decision-making support tool for operators.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0192792