This study explores the mathematical programming aspects of the air cargo fleet
assignment problem for one international air cargo carrier - Korean Air - under
given origin-destination (O-D) pairs, departure and arrival times, and frequencies.
A pure cargo service is taken as the basis for this study, since such a service is not
constrained by passenger route determinants and the schedule of a combination air
The objectives of the study include: to identify the pure air cargo network
representation of the combination air carriers; to develop and solve a conventional
branch-and-bound mathematical programming model for optimising the
assignment of aircraft to flight routes given a set of constraints, including aircraft
fleet size, schedule balance, and `required through' constraints; to develop and
solve the fleet assignment problem using a novel neural network optimisation
modelling approach; to investigate methods of implementing the neural network
model, and to analyse the performance of the model when compared with
conventional solution methods; and finally to analyse the utility of the neural
network model and identify how it may be used in the design and development of
air cargo networks for combination air carriers like Korean Air.
There are four main parts to the thesis: the first part outlines the schedule design
process of an airline and some details of the fleet assignment problem are
reviewed. The air cargo flight network is represented and the fleet assignment
problem is formulated as a mixed integer programming problem of cost
minimisation with various constraints. The complexity of the problem is discussed;
the second part outlines the various techniques available to solve optimisation
problems and neural network models are presented and discussed as a promising
alternative solution method. Neural network applications in the transport field are
reviewed and the neural network process for optimisation and for solving the
general assignment problem are studied and presented; the third part incorporates
the practical application of both the conventional fleet assignment problem solving
method and the proposed neural network method to a combination airline's case -
Korean Air. The detailed process of constructing a time line network and
formulating a mathematical programming model are described and equivalent
neural network models are formulated. The results from the two solution
approaches are compared and evaluated; and the final part summarises the main
findings, presents the significant conclusions, the contribution of the research is
discussed and some recommendations for further research are presented.
Overall, the conventional branch-and-bound optimisation model yielded
plausible results which were demonstrably superior to those produced using the
novel neural network optimisation models.
A Doctoral Thesis. Submitted in partial fulfillment of the requirements for the award of Doctor of Philosophy of Loughborough University.