Titlebook: Genetic Programming for Production Scheduling; An Evolutionary Lear Fangfang Zhang,Su Nguyen,Mengjie Zhang Book 2021 The Editor(s) (if appl

Stagger

978-981-16-4861-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor

Notorious

Genetic Programming for Production Scheduling978-981-16-4859-5Series ISSN 2730-9908 Series E-ISSN 2730-9916

aphasia

Fangfang Zhang,Su Nguyen,Mengjie ZhangPresents theoretical aspects and applications of genetic programming for production scheduling.Explores the modern and unique interfaces between operations research and machine learning.Offers an intr

機(jī)械

Salivary-Gland

含糊其辭

Learning Schedule Construction Heuristicseduling algorithms. Details about attributes extracted from production data and representations of scheduling construction heuristics are provided in this chapter. The advantages and disadvantages of each representation are analysed, and the generalisation of evolved heuristics is examined by using

CLAMP

Learning Schedule Improvement Heuristicss presented in this book and other meta-heuristics in the literature. Extended attribute sets and several evaluation mechanisms are introduced in this chapter to allow GP to evolve scheduling improvement heuristics. Experiment results show that the evolved scheduling improvement heuristics outperfor

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Learning to Augment Operations Research Algorithmsing. A simple genetic programming algorithm is introduced to evolve variable selectors for optimisation solvers to reduce the computational efforts required to obtain high-quality or optimal solutions for production scheduling. The optimisation solver enhanced by the evolved variable selectors can f

Blazon

Representations with Multi-tree and Cooperative Coevolutionexible job shop scheduling. Two strategies are introduced, one is the genetic programming with cooperative coevolution, the other is the genetic programming with multi-tree representation. The results show the advantages and disadvantages of these two strategies over learning two rules simultaneousl

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