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Graduate work from Kennesaw State University | …
(1972) Analysis of the airline systems performance by varying inputs, viz. jet aircraft, routes and scheduling. Engineer's thesis, California Institute of Technology.
This is a systems analysis study of the commercial airlines. In essence, it provides a systematic procedure for determining a combination of decisions regarding the aircraft, routes and scheduling which maximizes overall effectiveness.
The model treats the aircraft, route and the scheduling as basic inputs, passenger-mile or ton-mile as basic outputs, revenue and cost as the monetary output and input respectively, to the airline system. Out of the numerous and diverse management objectives, the profit is chosen as a performance parameter, however noting that the return on investment would be a more realistic parameter. As a simplification, a multiple relationship between the direct and the indirect operating cost is used to eliminate indirect operating cost, and the attention is focused on the direct operating cost. The core idea of the profit computation is the deterministic approach to the costs and the probabilistic approach to the revenues.
Four major steps can be observed, namely:
1. Collection and systematic presentation of the relevant data about the aircraft and routes, taking into account the operational and technical constraints. This system reduces the number of possible alternatives, thus simplifying decision analysis. The pictorial representation of this system appears on the next sheet.
2. A set of equations for the direct operating cost computation is developed, employing a technique of multiple regression analysis, by observing U. S. domestic experience of major airlines using B707-320B, B727-100 C/QC, DC9-30 aircraft. (All data is obtained from CAB-Wash.) DOC is further divided into flying, maintenance and depreciation. With the help of these equations, the direct operating cost could be predicted knowing the characteristics of the aircraft (number of engines, operating empty weight, fuel consumption), routes (average stage length) and scheduling (utilization).
3. The probabilistic revenue calculations are carried out using a simplified Monte Carlo simulation and expected value approach. The impact of alternative modes of transportation and the competition on the route has been given proper weightage. Even though calculations are done for three station problems, an extension is possible for complicated route networks.
4. By judging the profit performance, a feedback system is proposed.
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