A feasible timetable generator simulation modelling framework and simulation integrated genetic and hybrid genetic algorithms for train scheduling problem

An important problem in management of railway systems is train scheduling problem (TrnSchPrb). This is the problem of determining a timetable for a set of trains that does not violate track capacities and satisfies operational constraints. In this thesis, a feasible timetable generator stochastic simulation modelling framework is developed. The objective is to obtain a feasible train timetable for all trains in the system. The feasible train timetable includes train arrival and departure times at all visited stations with calculated average train travel time. In addition to obtaining a feasible timetable, hybrid algorithms are developed with the objective of minimizing the average train travel time. The first hybrid is obtained by integrating simulation and genetic algorithm (GA), and the other three hybrids are obtained by embedding each of three local search algorithms in simulation integrated GA. The simulation modelling framework developed in this thesis is implemented for a TrnSchPrb based on an infrastructure which was inspired by a real railway line system with single track corridor. The set of feasible timetables found by simulation forms the initial solution space of the developed hybrid GAs. These hybrid GAs are run for getting a feasible train timetable with optimum average train travel time. The optimum average train travel times found by the hybrid GAs are compared, and the results are discussed. Although this thesis focuses on train scheduling/timetabling problem, the developed simulation integrated framework can also be used for train rescheduling/dispatching problem if this framework can be fed by real time data.