| dc.description.abstract | Vehicle fleet routing and timetable setting are essential to inter-city bus carriers’
operating cost, profit, level of service and competitive capability in the market. With
the growing scales of carriers and the increasing requests of operation efficiency, an
optimal scheduling model was recently developed to improve the traditionally manual
way in fleeting routing and timetable setting. However, the model was established
based on an average passenger demand as input to produce the final timetable and
fleet routes, neglecting the stochastic characteristics of daily passenger demands in
actual operations, which could affect the optimal fleet routes and timetables. If the
demand is wildly changed in daily operations, then the original schedule could be
disturbed to lose its optimality. In the past, the effect of the stochastic disturbance
on the optimally planned schedule was rarely researched in either practices or
academics. Considering the stochastic characteristics of daily passenger demands in
actual operations, in this research, on the basis of the carrier’s perspective, we
developed a deterministic-demand and a stochastic-demand scheduling models, with
the objective of maximizing the carrier profit, subject to the real operating constraints.
The models are expected to be useful planning tools for inter-city bus carriers to solve
their optimal vehicle fleet routes and timetables in their short-term operations.
We employed network flow techniques to construct the deterministic-demand
scheduling model, which included multiple passenger-flow and fleet-flow time-space
networks in order to formulate the flows of passengers and vehicle fleet in the
dimensions of time and space. The deterministic-demand scheduling model was
formulated as a multiple commodity network flow problem that is characterized as an
NP-hard problem. We further established the stochastic-demand scheduling model
by modifying the fixed demand parameters in the deterministic-demand scheduling
model. Furthermore, we developed a simulation-based heuristic with link-based and
path-based algorithms to solve the stochastic-demand scheduling model. We
employed a mathematical programming solver, coupled with computer programs, to
solve the deterministic-demand scheduling model. To compare the performance of
the two models and the solution methods under stochastic demands in actual
operations, we developed a simulation-based evaluation method. Finally, to evaluate
the models, the solution methods and the evaluation method in practice, we performed
a case study using real operating data from a major Taiwan inter-city bus carrier, with
the assistance of C computer programs and a mathematical programming solver.
Then show the conclusions and suggestions. | en_US |