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Table of Contents
Intro
Preface
Contents
1 Introduction to the Packing and Cutting Problem
1.1 Problem Definition
1.1.1 Packing Problem
1.1.2 Cutting Problem
1.2 Literature Review
1.2.1 Review for 2DRSP
1.2.2 Review for 2DISP
1.2.3 Review for CSP
1.3 Development Trends
References
2 Intelligent Algorithms for Rectangular Packing Problem
2.1 Problem Description
2.2 Memetic Algorithm for the Problem
2.2.1 Introduction
2.2.2 The Placement Strategy
2.2.3 The Memetic Algorithm
2.2.4 Implementation of Memetic Algorithm
2.2.5 Experimental Results
2.3 Discrete Grey Wolf Optimization
2.3.1 Introduction
2.3.2 Improved Best-Fit Heuristic Algorithm
2.3.3 Discrete Grey Wolf Optimization
2.3.4 Experimentation and Results
2.4 Conclusions
References
3 Intelligent Algorithms for Irregular Packing Problem
3.1 Problem Description
3.2 The Geometrical Technique
3.3 Memetic Algorithm for the Problem
3.3.1 Introduction
3.3.2 The Memetic Algorithm
3.3.3 The Realization of the Adaptive Memetic Algorithm
3.3.4 Experimental Study and Discussions
3.4 Beam Search Hybridized with Tabu Search for the Problem
3.4.1 Introduction
3.4.2 Placement Principle Based on Improved NFP
3.4.3 The Hybrid Algorithm for Searching Sequence
3.4.4 Experimental Results and Discussions
3.5 Biased Genetic Algorithm Hybridized with VNS for the Problem
3.5.1 Introduction
3.5.2 Placement Method
3.5.3 Biased Genetic Algorithm Hybridized with VNS
3.5.4 Experimental Results and Discussions
3.6 Conclusions
Appendix
References
4 Novel Algorithms for 2DRSP and 2DISP
4.1 Reinforcement Learning Algorithm for 2DRPP
4.1.1 Introduction and Problem Description
4.1.2 Lowest Centroid Placement Method
4.1.3 Sequence Optimization Based on Q-learning
4.1.4 Computational Packing Experiments
4.2 Reinforcement Learning Algorithm for 2DIPP
4.2.1 Introduction
4.2.2 Description of Packing Problem
4.2.3 Positioning Strategy Based on BL
4.2.4 Sequence Optimization Strategy
4.2.5 Computational Experiment
4.3 Sequential Transfer-Based PSO for 2DIPP
4.3.1 Introduction
4.3.2 Novel Positioning Strategy Based on NFP
4.3.3 Description of Sequence Transfer
4.3.4 Computational Experiments
References
5 Solutions for New Variants of Packing Problem
5.1 Knapsack Packing Problem with Defects
5.1.1 Introduction and Literature Review
5.1.2 Problem Description
5.1.3 The Approach for the Problem
5.1.4 Numerical Experiments and Conclusions
5.2 Irregular Packing Problem with Defects
5.2.1 Introduction and Problem Description
5.2.2 Literature Review
5.2.3 Genetic Algorithm and Grey Wolf Optimization
5.2.4 Heuristic Placement Algorithm
5.2.5 Computational Results and Conclusions
5.3 Rectangular Packing Problem with Divisible Items
Preface
Contents
1 Introduction to the Packing and Cutting Problem
1.1 Problem Definition
1.1.1 Packing Problem
1.1.2 Cutting Problem
1.2 Literature Review
1.2.1 Review for 2DRSP
1.2.2 Review for 2DISP
1.2.3 Review for CSP
1.3 Development Trends
References
2 Intelligent Algorithms for Rectangular Packing Problem
2.1 Problem Description
2.2 Memetic Algorithm for the Problem
2.2.1 Introduction
2.2.2 The Placement Strategy
2.2.3 The Memetic Algorithm
2.2.4 Implementation of Memetic Algorithm
2.2.5 Experimental Results
2.3 Discrete Grey Wolf Optimization
2.3.1 Introduction
2.3.2 Improved Best-Fit Heuristic Algorithm
2.3.3 Discrete Grey Wolf Optimization
2.3.4 Experimentation and Results
2.4 Conclusions
References
3 Intelligent Algorithms for Irregular Packing Problem
3.1 Problem Description
3.2 The Geometrical Technique
3.3 Memetic Algorithm for the Problem
3.3.1 Introduction
3.3.2 The Memetic Algorithm
3.3.3 The Realization of the Adaptive Memetic Algorithm
3.3.4 Experimental Study and Discussions
3.4 Beam Search Hybridized with Tabu Search for the Problem
3.4.1 Introduction
3.4.2 Placement Principle Based on Improved NFP
3.4.3 The Hybrid Algorithm for Searching Sequence
3.4.4 Experimental Results and Discussions
3.5 Biased Genetic Algorithm Hybridized with VNS for the Problem
3.5.1 Introduction
3.5.2 Placement Method
3.5.3 Biased Genetic Algorithm Hybridized with VNS
3.5.4 Experimental Results and Discussions
3.6 Conclusions
Appendix
References
4 Novel Algorithms for 2DRSP and 2DISP
4.1 Reinforcement Learning Algorithm for 2DRPP
4.1.1 Introduction and Problem Description
4.1.2 Lowest Centroid Placement Method
4.1.3 Sequence Optimization Based on Q-learning
4.1.4 Computational Packing Experiments
4.2 Reinforcement Learning Algorithm for 2DIPP
4.2.1 Introduction
4.2.2 Description of Packing Problem
4.2.3 Positioning Strategy Based on BL
4.2.4 Sequence Optimization Strategy
4.2.5 Computational Experiment
4.3 Sequential Transfer-Based PSO for 2DIPP
4.3.1 Introduction
4.3.2 Novel Positioning Strategy Based on NFP
4.3.3 Description of Sequence Transfer
4.3.4 Computational Experiments
References
5 Solutions for New Variants of Packing Problem
5.1 Knapsack Packing Problem with Defects
5.1.1 Introduction and Literature Review
5.1.2 Problem Description
5.1.3 The Approach for the Problem
5.1.4 Numerical Experiments and Conclusions
5.2 Irregular Packing Problem with Defects
5.2.1 Introduction and Problem Description
5.2.2 Literature Review
5.2.3 Genetic Algorithm and Grey Wolf Optimization
5.2.4 Heuristic Placement Algorithm
5.2.5 Computational Results and Conclusions
5.3 Rectangular Packing Problem with Divisible Items