In this dissertation, I explored using several months worth of historical bus journey data to feed a search algorithm on three main optimisation criteria, with the goal that the program could be used by a bus operator to aid with improving their timetables during a review period. More data than ever before is being recorded about buses within the UK and this provided an exciting opportunity to encourage greater usage of buses, for the benefits they provide.
The first optimisation criteria was minimising unneeded slack time and travel times while balancing the percentage of buses predicted to be on time. The second optimisation criteria was to maximise cohesion between services that share a common route segment, by ensuring they are more spread out at shared stops. A shared route segment is defined as "N" consecutive stops shared by two or more services, where "N" is the minimum segment length. The third optimisation target is to minimise change as much as possible, as too many changes at once make it very difficult to predict how it is likely to perform in the real world. The search algorithm used is tabu-search coupled with squeaky wheel optimisation for a more targeted and informed search. I have demonstrated that tabu-search and squeaky wheel optimisation can be used to optimise a buses timetable to great effect.