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Effective Methods for Integrated Process Planning and Scheduling

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Cover of 'Effective Methods for Integrated Process Planning and Scheduling'

Table of Contents

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    Book Overview
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    Chapter 1 Introduction for Integrated Process Planning and Scheduling
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    Chapter 2 Review for Flexible Job Shop Scheduling
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    Chapter 3 Review for Integrated Process Planning and Scheduling
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    Chapter 4 Improved Genetic Programming for Process Planning
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    Chapter 5 An Efficient Modified Particle Swarm Optimization Algorithm for Process Planning
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    Chapter 6 A Hybrid Algorithm for Job Shop Scheduling Problem
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    Chapter 7 An Effective Genetic Algorithm for FJSP
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    Chapter 8 An Effective Collaborative Evolutionary Algorithm for FJSP
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    Chapter 9 Mathematical Modeling and Evolutionary Algorithm-Based Approach for IPPS
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    Chapter 10 An Agent-Based Approach for IPPS
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    Chapter 11 A Modified Genetic Algorithm Based Approach for IPPS
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    Chapter 12 An Effective Hybrid Algorithm for IPPS
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    Chapter 13 An Effective Hybrid Particle Swarm Optimization Algorithm for Multi-objective FJSP
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    Chapter 14 A Multi-objective GA Based on Immune and Entropy Principle for FJSP
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    Chapter 15 An Effective Genetic Algorithm for Multi-objective IPPS with Various Flexibilities in Process Planning
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    Chapter 16 Application of Game Theory-Based Hybrid Algorithm for Multi-objective IPPS
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    Chapter 17 A Hybrid Intelligent Algorithm and Rescheduling Technique for Dynamic JSP
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    Chapter 18 A Hybrid Genetic Algorithm and Tabu Search for Multi-objective Dynamic JSP
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    Chapter 19 GEP-Based Reactive Scheduling Policies for Dynamic FJSP with Job Release Dates
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    Chapter 20 A Hybrid Genetic Algorithm with Variable Neighborhood Search for Dynamic IPPS
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    Chapter 21 IPPS Simulation Prototype System
Attention for Chapter 1: Introduction for Integrated Process Planning and Scheduling
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Chapter title
Introduction for Integrated Process Planning and Scheduling
Chapter number 1
Book title
Effective Methods for Integrated Process Planning and Scheduling
Published by
Springer, Berlin, Heidelberg, January 2020
DOI 10.1007/978-3-662-55305-3_1
Book ISBNs
978-3-66-255303-9, 978-3-66-255305-3
Authors

Xinyu Li, Liang Gao, Li, Xinyu, Gao, Liang

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X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 13 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 3 23%
Lecturer 1 8%
Student > Bachelor 1 8%
Student > Doctoral Student 1 8%
Student > Ph. D. Student 1 8%
Other 1 8%
Unknown 5 38%
Readers by discipline Count As %
Engineering 4 31%
Computer Science 3 23%
Mathematics 1 8%
Unknown 5 38%