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Evolutionary Algorithms and Scheduling Colin Reeves School of Mathematical and Information Sciences Coventry University.

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Presentation on theme: "Evolutionary Algorithms and Scheduling Colin Reeves School of Mathematical and Information Sciences Coventry University."— Presentation transcript:

1 Evolutionary Algorithms and Scheduling Colin Reeves School of Mathematical and Information Sciences Coventry University

2 Evolutionary Algorithms Population-based heuristic search Genotype-phenotype mapping Fitness evaluation ‘Genetic’ operators: recombination, mutation Selection mechanism

3 Scheduling Single/multiple machines Flow shop/job shop/open shop

4 Traditional Jobshop

5 Variations Simple flows/Precedence constraints Serial/parallel machines Intermediate storage Ready times Independent/dependent processing times Due dates/earliness penalties

6 EA Techniques Encoding methods: direct/indirect Sequence/schedule (permutations) Operator design Performance measures Despatching rules

7 Fitness landscapes Different operators = different landscapes Measuring ‘ruggedness’ ‘Big valley’ phenomenon ‘Path tracing’ EAs

8 EA Strengths/Weaknesses Population: good for uncertainty/multi- objective aspects Parallel implementation Hybridisation May be inefficient (fitness evaluations) Need for tuning Not an excuse for lack of thought

9 Future Uncertainty modelling: probability vs. fuzziness Multiple objectives Rescheduling: robustness measures (Artificial Immune Systems?)

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