Evolutionary Algorithms and Scheduling

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

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Traditional Jobshop
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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

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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

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Future

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