  Previous research has shown that artificial immune systems can be used to
produce robust schedules in a manufacturing environment. The main goal is to
develop building blocks (antibodies) of partial schedules that can be used to
construct backup solutions (antigens) when disturbances occur during
production. The building blocks are created based upon underpinning ideas from
artificial immune systems and evolved using a genetic algorithm (Phase I). Each
partial schedule (antibody) is assigned a fitness value and the best partial
schedules are selected to be converted into complete schedules (antigens). We
further investigate whether simulated annealing and the great deluge algorithm
can improve the results when hybridised with our artificial immune system
(Phase II). We use ten fixed solutions as our target and measure how well we
cover these specific scenarios.
