Genetic Programming for Production Scheduling : An Evolutionary Learning Approach / by Fangfang Zhang, Su Nguyen, Yi Mei, Mengjie Zhang.

Format:

Book

Author/Creator:

Zhang, Fangfang, Author.

Nguyen, Su., Author.

Mei, Yi, Author.

Zhang, Mengjie, Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Machine Learning: Foundations, Methodologies, and Applications, 2730-9916

Language:

English

Subjects (All):

Machine learning.

Expert systems (Computer science).

Industrial engineering.

Production engineering.

Operations research.

Machine Learning.

Knowledge Based Systems.

Industrial and Production Engineering.

Operations Research and Decision Theory.

Local Subjects:

Machine Learning.

Knowledge Based Systems.

Industrial and Production Engineering.

Operations Research and Decision Theory.

Physical Description:

1 online resource (XXXIII, 336 pages) : 154 illustrations, 105 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2021.

System Details:

text file PDF

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.

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.

Other Format:

Printed edition:

ISBN:

978-981-16-4859-5

9789811648595

Access Restriction:

Restricted for use by site license.