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| Publication | Scenario | Type of model | Objective | Solution approach |
|
| [4] | Line, disruption, and huge passenger demand | Nonlinear programming | Train operation returns to original timetable and waiting time of passengers outside stations | Iterative metaheuristic approach |
| [5] | Line, single-point disturbance, overloaded passengers, and train skip-stopping rescheduling | Linear bilevel programming | Total train delay (upper-level model) and number of passengers served (lower-level model) | Sensitivity analysis-based algorithm |
| [32] | Rush hours, platform jam, inbound passenger flow control, and timetable regulation at transfer station | Nonlinear programming | Average waiting time of both inbound and transfer passengers | Improved genetic algorithm |
| [33] | Line, single-point disturbance, and crowded situations | Markov decision process | Total delay of all the disturbed trains with minimal impact on both operation costs and service quality | Approximation dynamic programming |
| [34] | High-frequency line, single-point disturbance, and overloaded passengers | Quadratic programming | Total train delay | Model predictive control and MATLAB optimization tool box |
| [35] | Network, single-point disturbance, and high demand | Mixed-integer nonlinear programming | Punctuality and regularity in train operations, the passenger waiting time, the passenger flow burden of platforms, and passenger flow control costs | Iterative nonlinear programming approach and Gurobi solver |
| This paper | Line, high demand, and train speed limit (multisection) | Mixed-integer quadratic programming | Total train delay and number of passengers served | Two-stage approach and Gurobi solver |
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