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Genetic Programming for Production Scheduling : An Evolutionary Learning Approach /

This book introduces readers to an evolutionary learning approach, specifically genetic programming (GP), for production scheduling. The book is divided into six parts. In Part I, it provides an introduction to production scheduling, existing solution methods, and the GP approach to production sched...

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Bibliographic Details
Main Authors: Zhang, Fangfang (Author), Nguyen, Su (Author), Mei, Yi (Author), Zhang, Mengjie (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Singapore : Springer Singapore : Imprint: Springer, 2021.
Edition:1st ed. 2021.
Series:Machine Learning: Foundations, Methodologies, and Applications,
Subjects:
Machine learning.
Knowledge representation (Information theory) .
Industrial engineering.
Production engineering.
Operations research.
Decision making.
Machine Learning.
Knowledge based Systems.
Industrial and Production Engineering.
Operations Research/Decision Theory.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Part I Introduction
  • 1 Introduction
  • 2 Preliminaries
  • Part II Genetic Programming for Static Production Scheduling Problems
  • 3 Learning Schedule Construction Heuristics
  • 4 Learning Schedule Improvement Heuristics
  • 5 Learning to Augment Operations Research Algorithms
  • Part III Genetic Programming for Dynamic Production Scheduling Problems
  • 6 Representations with Multi-tree and Cooperative Coevolution
  • 7 Efficiency Improvement with Multi-fidelity Surrogates
  • 8 Search Space Reduction with Feature Selection
  • 9 Search Mechanism with Specialised Genetic Operators
  • Part IV Genetic Programming for Multi-objective Production Scheduling Problems
  • 10 Learning Heuristics for Multi-objective Dynamic Production Scheduling Problems
  • 11 Cooperative Coevolutionary for Multi-objective Production Scheduling Problems
  • 12 Learning Scheduling Heuristics for Multi-objective Dynamic Flexible Job Shop Scheduling
  • Part V Multitask Genetic Programming for Production Scheduling Problems
  • 13 Multitask Learning in Hyper-heuristic Domain with Dynamic Production Scheduling
  • 14 Adaptive Multitask Genetic Programming for Dynamic Job Shop Scheduling
  • 15 Surrogate-Assisted Multitask Genetic Programming for Learning Scheduling Heuristics
  • Part VI Conclusions and Prospects
  • 16 Conclusions and Prospects.