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Optimized Schedule Verified by Simulation
(the second half)
Collects detailed performance metrics
- Feasibility assurance
- Continuous time execution of transit model based on integer time steps
- Inspection & analysis of track logs from individual passengers and vehicles
State persistence
- Evolve system state with all known data
- Reformulate and re-optimize schedule as scenario progresses and new input data is introduced
- Eventually allow rolling horizon scheduling
SimPy: discrete event simulation framework
LP_solve: MIP Optimization
Notes:
Simulation to execute the aggregate schedule using and tracking individual entities