Download Evolutionary Algorithms and Scheduling

Evolutionary Algorithms and
Scheduling
Colin Reeves
School of Mathematical and Information
Sciences
Coventry University
Evolutionary Algorithms
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Population-based heuristic search
Genotype-phenotype mapping
Fitness evaluation
‘Genetic’ operators: recombination,
mutation
• Selection mechanism
Scheduling
• Single/multiple machines
• Flow shop/job shop/open shop
Traditional Jobshop
Variations
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Simple flows/Precedence constraints
Serial/parallel machines
Intermediate storage
Ready times
Independent/dependent processing times
Due dates/earliness penalties
EA Techniques
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Encoding methods: direct/indirect
Sequence/schedule (permutations)
Operator design
Performance measures
Despatching rules
Fitness landscapes
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Different operators = different landscapes
Measuring ‘ruggedness’
‘Big valley’ phenomenon
‘Path tracing’ EAs
EA Strengths/Weaknesses
• Population: good for uncertainty/multiobjective aspects
• Parallel implementation
• Hybridisation
• May be inefficient (fitness evaluations)
• Need for tuning
• Not an excuse for lack of thought
Future
• Uncertainty modelling: probability vs.
fuzziness
• Multiple objectives
• Rescheduling: robustness measures
(Artificial Immune Systems?)