| Summary: | 碩士 === 國立中央大學 === 土木工程研究所 === 90 === The setting of a good flight schedule for an airline not only has to consider its fleet and related supply, but also has to take into account of passenger reactions on its service. Although little research of medium/long-term flight scheduling in the past has ever dealt with variable passenger demands considering market competitions, almost all past short-term flight scheduling models assumed passenger demands as fixed and used a draft timetable as input to produce the final timetable and schedule, neglecting passenger choice behaviors among different airlines in practice. As a result, the schedule and fleet route offered may not reflect the real demands, decreasing the system performance. Considering both fleet supply and market demands, in this research, we developed a short-term flight scheduling model with variable demands, in order to help an airline solve optimal fleet routes and timetables. We employed network flow techniques to construct the model which includes multiple passengers and fleet flow network. In the passenger flow networks, we introduced a passenger choice model to formulate passenger flows. Considering the loss of waiting passengers in practice, we used generalized networks to formulate passenger flows in terms of time and space. In the fleet flow network, we used integer flow networks to formulate the aircraft routes in terms of time and space. Some side constraints were sat between the passenger and fleet flow network according to the real operating requirements. The model is expected to be a useful planning tool for airlines to determine their short-term fleet routes and timetables.
We used mathematical programming techniques to formulate the model as a nonlinear mixed integer program that is characterized as a NP-hard problem and is more difficult to solve than traditional flight scheduling problems that are often formulated as integer linear programs. To efficiently solve the model with practical size problems, we developed an iterative solution framework, in which we repeatedly modify the target airline market share in each iteration and solve a fixed-demand flight scheduling problem with the assistance of the mathematical programming solver, CPLEX. To evaluate the model and the solution framework, we performed a case study using real operating data of domestic passenger transportation from a major Taiwan airline.
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