Venkata Pilla

Graduation Semester and Year

2006

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Industrial Engineering

Department

Industrial and Manufacturing Systems Engineering

First Advisor

Victoria Chen

Abstract

The fleet assignment model allocates the fleet of aircraft types to the scheduled flight legs in the airline timetable published 90 days prior to the departure of the aircraft. The objective is to maximize profit. While costs associated with assigning a particular fleet type to a leg are easy to estimate, the revenues are based upon demand, which is realized close to departure. The uncertainty in demand makes it challenging to assign the right type of aircraft to each flight leg based on the forecasts taken 90 days prior to departure. Therefore, in this dissertation a two-stage stochastic programming framework has been utilized to model the uncertainty in demand, along with the Boeing concept of demand driven dispatch to reallocate aircraft close to departure of the aircraft. In this method, crew-compatible families are allocated in the first stage (90 days prior to departure of aircraft), and in the second stage (two weeks prior to departure), when most of the demand is realized, specific fleet types within the families are assigned. The stochasticity of the demand is incorporated by considering different demand scenarios in the second stage, and an average over the scenarios is used to calculate the expected profit. Traditionally, the two-stage stochastic programming framework problems are solved using a Benders' approach. Due to the slow convergence of the Benders' approach a novel statistics-based approach using design and analysis of computer experiments has been developed. The results obtained with our approach are compared to that of a Benders' approach. Finally future research is discussed.

Disciplines

Engineering | Operations Research, Systems Engineering and Industrial Engineering

Comments

Degree granted by The University of Texas at Arlington

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