Genetic programming for production scheduling : an evolutionary learning approach / Fangfang Zhang, Su Nguyen, Yi Mei, Mengjie Zhang.
Zhang, Fangfang, author.; Nguyen, Su, author.; Mei, Yi, author.; Zhang, Mengjie, author.
2021
TS157.5
Available Online

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Title Genetic programming for production scheduling : an evolutionary learning approach / Fangfang Zhang, Su Nguyen, Yi Mei, Mengjie Zhang.
Author Zhang, Fangfang, author.
ISBN 9789811648595 (electronic bk.)
981164859X (electronic bk.)
9789811648588 (print)
9811648581
DOI https://doi.org/10.1007/978-981-16-4859-5
Published Singapore : Springer, 2021.
Language English
Description 1 online resource (xxxiii, 336 pages) : illustrations (some color)
Other Standard Identifiers 10.1007/978-981-16-4859-5 doi
Call Number TS157.5
Dewey Decimal Classification 004/.35
Summary 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 scheduling. Characteristics of production environments, problem formulations, an abstract GP framework for production scheduling, and evaluation criteria are also presented. Part II shows various ways that GP can be employed to solve static production scheduling problems and their connections with conventional operation research methods. In turn, Part III shows how to design GP algorithms for dynamic production scheduling problems and describes advanced techniques for enhancing GP's performance, including feature selection, surrogate modeling, and specialized genetic operators. In Part IV, the book addresses how to use heuristics to deal with multiple, potentially conflicting objectives in production scheduling problems, and presents an advanced multi-objective approach with cooperative coevolution techniques or multi-tree representations. Part V demonstrates how to use multitask learning techniques in the hyper-heuristics space for production scheduling. It also shows how surrogate techniques and assisted task selection strategies can benefit multitask learning with GP for learning heuristics in the context of production scheduling. Part VI rounds out the text with an outlook on the future. Given its scope, the book benefits scientists, engineers, researchers, practitioners, postgraduates, and undergraduates in the areas of machine learning, artificial intelligence, evolutionary computation, operations research, and industrial engineering.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from PDF title page (SpringerLink, viewed November 19, 2021).
Added Author Nguyen, Su, author.
Mei, Yi, author.
Zhang, Mengjie, author.
Series Machine learning, 2730-9916
Available in Other Form Genetic programming for production scheduling.
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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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.

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