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An intelligent simulation platform for train traffic control under disturbance

An intelligent simulation platform for train traffic control under disturbance Railway disturbance management is inherently a multi-objective optimization problem that concerns both the operators’ cost and passenger’s service level. This study proposes a multi-objective simulation-based optimization framework to effectively manage the train conflicts after the occurrences of a disturbance caused by a temporary line blockage. The simulation model enhanced with a dynamic priority dispatching rule in order to speed up the optimization procedure. A multi-objective variable neighborhood search meta-heuristic is proposed to solve the train rescheduling model. The obtained Pareto optimal solutions for disturbance management model support the decision maker to find a trade-off between both user and operator viewpoints. The proposed approach has been validated on a set of disruption scenarios covering a large part of the Iranian rail network. The computational results prove that the proposed model can generate good-quality timetables with the minimum passenger delay and deviation from the initial timetable. The outcomes indicate that the developed simulation-based optimization approach has substantial advantages in producing practical solution quickly when compared to currently accepted solutions.Abbreviation: MOVNS: multi-objective variable neighbourhood search; DES: discrete-event simulation; SO: simulation-optimization; AG: Alternative Graph; FCFS: First Come First Served; MIP: mixed integer programming; MILP: mixed-integer linear programming; B&B: branch and bound algorithm; VND: Variable Neighborhood Descent; NSGA-II: Non-dominated Sorting Genetic Algorithm–II; CD: crowding distance; DP: dynamic priority; EDD: earliest due date first; SRTT: shortest remaining traveling time; LST: least slack time first http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Modelling and Simulation Taylor & Francis

An intelligent simulation platform for train traffic control under disturbance

An intelligent simulation platform for train traffic control under disturbance

International Journal of Modelling and Simulation , Volume 39 (3): 22 – Jul 3, 2019

Abstract

Railway disturbance management is inherently a multi-objective optimization problem that concerns both the operators’ cost and passenger’s service level. This study proposes a multi-objective simulation-based optimization framework to effectively manage the train conflicts after the occurrences of a disturbance caused by a temporary line blockage. The simulation model enhanced with a dynamic priority dispatching rule in order to speed up the optimization procedure. A multi-objective variable neighborhood search meta-heuristic is proposed to solve the train rescheduling model. The obtained Pareto optimal solutions for disturbance management model support the decision maker to find a trade-off between both user and operator viewpoints. The proposed approach has been validated on a set of disruption scenarios covering a large part of the Iranian rail network. The computational results prove that the proposed model can generate good-quality timetables with the minimum passenger delay and deviation from the initial timetable. The outcomes indicate that the developed simulation-based optimization approach has substantial advantages in producing practical solution quickly when compared to currently accepted solutions.Abbreviation: MOVNS: multi-objective variable neighbourhood search; DES: discrete-event simulation; SO: simulation-optimization; AG: Alternative Graph; FCFS: First Come First Served; MIP: mixed integer programming; MILP: mixed-integer linear programming; B&B: branch and bound algorithm; VND: Variable Neighborhood Descent; NSGA-II: Non-dominated Sorting Genetic Algorithm–II; CD: crowding distance; DP: dynamic priority; EDD: earliest due date first; SRTT: shortest remaining traveling time; LST: least slack time first

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References (60)

Publisher
Taylor & Francis
Copyright
© 2018 Informa UK Limited, trading as Taylor & Francis Group
ISSN
1925-7082
eISSN
0228-6203
DOI
10.1080/02286203.2018.1488110
Publisher site
See Article on Publisher Site

Abstract

Railway disturbance management is inherently a multi-objective optimization problem that concerns both the operators’ cost and passenger’s service level. This study proposes a multi-objective simulation-based optimization framework to effectively manage the train conflicts after the occurrences of a disturbance caused by a temporary line blockage. The simulation model enhanced with a dynamic priority dispatching rule in order to speed up the optimization procedure. A multi-objective variable neighborhood search meta-heuristic is proposed to solve the train rescheduling model. The obtained Pareto optimal solutions for disturbance management model support the decision maker to find a trade-off between both user and operator viewpoints. The proposed approach has been validated on a set of disruption scenarios covering a large part of the Iranian rail network. The computational results prove that the proposed model can generate good-quality timetables with the minimum passenger delay and deviation from the initial timetable. The outcomes indicate that the developed simulation-based optimization approach has substantial advantages in producing practical solution quickly when compared to currently accepted solutions.Abbreviation: MOVNS: multi-objective variable neighbourhood search; DES: discrete-event simulation; SO: simulation-optimization; AG: Alternative Graph; FCFS: First Come First Served; MIP: mixed integer programming; MILP: mixed-integer linear programming; B&B: branch and bound algorithm; VND: Variable Neighborhood Descent; NSGA-II: Non-dominated Sorting Genetic Algorithm–II; CD: crowding distance; DP: dynamic priority; EDD: earliest due date first; SRTT: shortest remaining traveling time; LST: least slack time first

Journal

International Journal of Modelling and SimulationTaylor & Francis

Published: Jul 3, 2019

Keywords: Train rescheduling; multi-objective optimization; discrete-event simulation; disturbance management

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