In order to meet the fast-growing demand, airlines have applied much more compact air-fleet operation schedules which directly lead to airport congestion. One result is the flight delay, which appears more frequently and seriously; the flight delay can also significantly damage airline's profitability and reputation The aim of this project is to enhance the dispatch reliability of Australian X Airline's fleet through a newly developed approach to reliability modeling, which employs computer-aided numerical simulation of the departure delay distribution and related cost to achieve the flight schedule optimization. The reliability modeling approach developed in this project is based on the probability distributions and Monte Carlo Simulation (MCS) techniques. Initial (type I) delay and propagated (type II) delay are adopted as the criterion for data classification and analysis. The randomicity of type I delay occurrence and the internal relationship between type II delay and changed flight schedule are considered as the core factors in this new approach of reliability modeling, which compared to the conventional assessment methodologies, is proved to be more accurate on the departure delay and cost evaluation modeling. The Flight Delay and Cost Simulation Program (FDCSP) has been developed (Visual Basic 6.0) to perform the complicated numerical calculations through significant amount of pseudo-samples. FDCSP is also designed to provide convenience for varied applications in dispatch reliability modeling. The end-users can be airlines, airports and aviation authorities, etc. As a result, through this project, a 16.87% reduction in departure delay is estimated to be achieved by Australian X Airline. The air-fleet dispatch reliability has been enhanced to a higher level - 78.94% compared to initial 65.25%. Thus, 13.35% of system cost can be saved. At last, this project also achieves to set a more practical guideline for air-fleet database and management upon overall dispatch reliability optimization.

Post deregulation, many U.S. airlines created hubs with banked schedules, however, in the past decade these same airlines began to experiment with depeaking their schedules to reduce costs and improve operational performance. To date there has been little research that has investigated revenue and operational shifts associated with depeaked schedules; yet understanding the trade-offs among revenue, costs, and operational performance at a network level is critical before airlines will consider future depeaking and related congestion-management strategies. This study develops data cleaning and analysis methodologies based on publicly available data that are used to quantify airport-level and network-level revenue and operational changes associated with schedule depeaking. These methodologies are applied to six case studies of airline depeaking over the past decade. Results show that depeaking is associated with revenue per available seat mile (RASM) increasing slower than the rest of the network and the industry as a whole. Depeaking is associated with improved operations for both the depeaking airlines and competitors. Airports benefit from increases in non-aeronautical sales associated with connecting passengers spending more time in the terminal. The underlying reasons driving airlines' scheduling decisions during depeaking vary greatly by case. Results from the study provide insights for airlines that are considering depeaking and the airports which are affected. The results suggest that losses in RASM and no improvement in operations could potentially lead an airline to repeak, and that RASM is prone to fall when a strong competitive threat exists.