Abstract:
Aviation industry is one of the most sophisticated and commercially challenging
industries, providing both passenger and freight services. In a highly competitive
environment, it is essential to dynamically control their operating costs by managing their
flights, selecting aircraft, and scheduling crews effectively. This is a continuous process
of fleet planning and route planning.
In aircraft fleet planning problem, large number of parameters or input variables needs to
be considered, which are often conflicting in nature. Thus, this complex decision making
process requires the use of analytical as well as optimization techniques for a solution
with high degree of optimality. Among many such parameters, some are design and
capacity (in terms of seating, flying, mileage) of the aircraft, demand, cost, carbon
emission, issues related to customer satisfaction, number of crews, flying distance, etc.
The probable outputs are also multi-fold, such as selection of aircraft type, capacity (size)
and number of aircrafts, fleet assignment and schedule, fleet routing, crew schedule, etc.
This dissertation formulates and analyzes six different models, each of which examines
a composition of certain pertinent airline fleet planning problems. First one is the profit
maximization model. Second one is fuzzy customer satisfaction model, third one is the
carbon emission model, fourth one is the integration of all three models using Artificial
Neural Network. Fifth and sixth models are used to verify trip cost using Genetic
algorithm and Particle Swarm Optimization respectively.
In this dissertation, cognitive computing fuzzy logic has been used to rank a set of
selected aircraft according to the level of satisfaction on the basis of the reviews made by
the passengers in a linguistic pattern. This research applied artificial neural network to
solve optimization sub-problems, such as profit maximization, satisfaction maximization
and carbon emission minimization.
Analyses involving many optimization requirements, taking into account so many input
variables/parameters, and with targets of so many outputs make the complete problem
NP-hard. Additionally, while analytical methods might suffer from slow convergence and
the curse of dimensionality, heuristics-based swarm intelligence can be an efficient
alternative. This necessitates the use of meta-heuristics. Particle Swarm Optimization
(PSO), part of the swarm intelligence family, is known to effectively solve large-scale
nonlinear optimization problems. This dissertation used Genetic Algorithm (GA) and
Particle Swarm Optimization (PSO) techniques for optimizing trip cost for short haul and
long haul operation. A comparative analysis has also been made for trip costs, obtained
from different methodologies.
The proposed models, methods of implementation of those models and proposed
analytical techniques can be of tremendous help for not only aviation industry, but also
for other similar situations in logistics management in general.
