Journal of Computational Physics
Modern heuristic techniques for combinatorial problems
The evolution and analysis of potential antibody library for use in job-shop scheduling
New ideas in optimization
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Proceedings of the 5th International Conference on Genetic Algorithms
The Evolution of Emergent Organization in Immune System Gene Libraries
Proceedings of the 6th International Conference on Genetic Algorithms
General Cooling Schedules for a Simulated Annealing Based Timetabling System
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
Off-the-Peg or Made-to-Measure? Timetabling and Scheduling with SA and TS
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
A new approach to solve hybrid flow shop scheduling problems by artificial immune system
Future Generation Computer Systems - Special issue: Computational science of lattice Boltzmann modelling
On the contribution of gene libraries to artificial immune systems
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Scheduling Algorithms
Solving job-shop scheduling problems by a novel artificial immune system
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
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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.