Crew scheduling of light rail transit in Hong Kong: from modeling to implementation
Our work concerns the problem of crew scheduling for the Hong Kong Light Rail Transit, which together with Heavy Rail Transit, makes up the two divisions of KowloonCanton Railway Corporation. As the Corporation is rapidly expanding on routes, capacities and territorial coverage, there are pressing needs for timely constructions of crew schedules whenever passenger demand variations necessitate modifications in train timetables, typically every 34months. We aim at automating this complex schedule construction, adopting computer and operations research models. The implementation runs as a decision support tool, with an overwhelming reduction in human effort in crew schedule construction; as the entire crew schedule can be constructed iteratively in less than half an hour on a PC. This work concerns the problem of crew scheduling for the Hong Kong Light Rail Transit (LRT), which together with Heavy Rail Transit, makes up the two divisions of KowloonCanton Railway Corporation. As of early 1996, LRT operates eight routes, two train depots and 57 stations on its operational network. It is rapidly expanding on routes, capacities and territorial coverage, hence pressing needs for timely constructions of crew schedules whenever passenger demand variations necessitate modifications in train timetables, typically every 34months. Computer-assisted manual solutions from old software can take up to 1month of painstaking work. Our project aims at automating this complex schedule construction, adopting a novel optimization modeling approach amenable for decomposition into separate solution stages by network and heuristics algorithms. The entire crew schedule can be constructed iteratively in less than half an hour on a PC. The implementation runs as a decision support tool, with contributions of an overwhelming reduction in human effort in crew schedule construction and a feasible and better (higher productivity rate) schedule, with possible further manual improvements that can be made.
crew scheduling optimization modeling heuristics