Thio thesis investigates the use of genetic algorithms (GAs) for solving a range of
timetabling and scheduling problems. Such problems arc very hard in general, and
GAs offer a useful and successful alternative to existing techniques.A framework is presented for GAs to solve modular timetabling problems in edu¬
cational institutions. The approach involves three components: declaring problemspecific
constraints, constructing a problem specific evaluation function and using a
problem-independent GA to attempt to solve the problem. Successful results are
demonstrated and a general analysis of the reliability and robustness of the approach is
conducted. The basic approach can readily handle a wide variety of general timetabling
problem constraints, and is therefore likely to be of great practical usefulness (indeed,
an earlier version is already in use). The approach rclicG for its success on the use of
specially designed mutation operators which greatly improve upon the performance of
a GA with standard operators.A framework for GAs in job shop and open shop scheduling is also presented. One
of the key aspects of this approach is the use of specially designed representations
for such scheduling problems. The representations implicitly encode a schedule by
encoding instructions for a schedule builder. The general robustness of this approach
is demonstrated with respect to experiments on a range of widely-used benchmark
problems involving many different schedule quality criteria. When compared against
a variety of common heuristic search approaches, the GA approach is clearly the most
successful method overall. An extension to the representation, in which choices of
heuristic for the schedule builder arc also incorporated in the chromosome, iG found to
lead to new best results on the makespan for some well known benchmark open shop
scheduling problems. The general approach is also shown to be readily extendable to
rescheduling and dynamic scheduling