Intelligent Production and Manufacturing Optimisation―The Bees Algorithm Approach

Preface
Acknowledgements
Contents
Introduction
The Bees Algorithm—A Gentle Introduction
	1 Introduction
	2 Parameter Optimisation
		2.1 Discrete Optimisation: The Travelling Salesman Problem
		2.2 Heuristics
		2.3 Continuous Optimisation: Function Maximisation
	3 Bee Inspired! Intelligent Optimisation with the Bees Algorithm
		3.1 Honey Bees Foraging Behaviour
		3.2 The Bees Algorithm
		3.3 The Standard Bees Algorithm
		3.4 The Bees Algorithm for Discrete Optimisation Problems
	4 Variants of the Bees Algorithm
	5 Discussion
	6 Conclusions
	References
Manufacturing Process Optimisation
Minimising Printed Circuit Board Assembly Time Using the Bees Algorithm with TRIZ-Inspired Operators
	1 Introduction
	2 Bees Algorithm
	3 Theory of Inventive Problem Solving
	4 MBTD PCB Assembly
	5 Application of TRIZ to Operator Generation
		5.1 Dynamisation Operator
		5.2 Segmentation Operator
		5.3 Local Quality Operator
	6 The Bees Algorithm with TRIZ-Inspired Operators
	7 PCB Assembly Involving 50 Components (10 Different Types of Components)—A Case Study
	8 Discussions
	9 Conclusions
	References
The application of the Bees Algorithm in a Digital Twin for Optimising the Wire Electrical Discharge Machining (WEDM) Process Parameters
	1 Introduction
	2 Literature Review
	3 Wire Electrical Discharge Machining (WEDM)
	4 Virtual Machine Creation
		4.1 Program Architecture
		4.2 Bees Algorithm Optimisation Testing and Results
	5 Discussion of Results
	6 Conclusions and Future Work
	References
A Case Study with the BEE-Miner Algorithm: Defects on the Production Line
	1 Introduction
	2 Cost-Sensitive Classifiers
		2.1 BEE-Miner Algorithm
		2.2 MEPAR-Miner Algorithm
	3 Experimental Study
	4 Conclusion
	References
An Application of the Bees Algorithm to Pulsating Hydroforming
	1 Introduction
	2 Methodology
		2.1 Design of the Experimental Data Set
		2.2 Obtaining Mathematical Expression for the Bulge Height via Experimental Data
		2.3 Validation of Mathematical Modelling
		2.4 Applying the Bees Algorithm to the Hydroforming Process
	3 Results and Discussion
	4 Conclusion
	References
Production Equipment Optimisation
Shape Recognition for Industrial Robot Manipulation with the Bees Algorithm
	1 Introduction
	2 Literature Review
	3 Primitive Fitting Methods
		3.1 Representation Scheme
		3.2 Fitness Function
		3.3 Local Search Operator
	4 Experimental Set Up
		4.1 Datasets
		4.2 Error Evaluation Function
		4.3 Parameterisation of Algorithms
	5 Results
	6 Conclusions and Further Work
	References
Bees Algorithm Models for the Identification and Measurement of Tool Wear
	1 Introduction
	2 Bees Algorithm
	3 Turning Trials
		3.1 Tool Wear Measurements
	4 Bees Algorithm for Tool Wear Detection
	5 Results
	6 Discussion and Conclusions
	References
Global Optimisation for Point Cloud Registration with the Bees Algorithm
	1 Introduction
	2 Related Work
	3 Problem Formulation
	4 Methodology
		4.1 Encoding of the Candidate Solutions
		4.2 Fitness Function for 3D Registration Evaluation
		4.3 SVD Operation for 3D Point Cloud Registration
		4.4 Iterative Closest Point (ICP)
		4.5 The Bees Algorithm for 3D Registration
	5 Experiments and Discussion
		5.1 Dataset and Parameter Settings
		5.2 Consistency
		5.3 Precision
		5.4 Robustness to Noise
	6 Conclusion
	References
Automatic PID Tuning Toolkit Using the Multi-Objective Bees Algorithm
	1 Introduction
	2 PID Control and Tuning Methods
	3 Bees Algorithm and Multi-Objective Optimisation
	4 Automatic PID Tuner
	5 Discussion and Conclusion
	References
The Effect of Harmony Memory Integration into the Bees Algorithm
	1 Introduction
	2 The Design Problem
	3 The Bees Algorithm and Integration
	4 Results and Discussion
	5 Conclusion
	References
Memory-Based Bees Algorithm with Lévy Flights for Multilevel Image Thresholding
	1 Introduction
	2 Lévy Flights and Honey Bees
		2.1 Lévy Flights with MBA
		2.2 Initialisation Step-Based Lévy Flights
		2.3 Bees-Movement-Based Lévy Flights
	3 Otsu’s Image Thresholding and PSNR
	4 Experimental Results
		4.1 Benchmark Test Functions
		4.2 MBA and LMBA with PSNR for Multilevel Image Thresholding
	5 Results Analysis for Standard Images
	6 Conclusion
	References
Α New Method to Generate the Initial Population of the Bees Algorithm for Robot Path Planning in a Static Environment
	1 Introduction
	2 The Proposed Method
		2.1 Configuration Space
		2.2 Initialise the Population of the Bees Algorithm
		2.3 The Fitness Function
		2.4 Local Search
		2.5 Global Search
		2.6 Neighbourhood Shrinking
	3 Results
	4 Comparison
	5 Conclusion
	References
Production Plan Optimisation
Method for the Production Planning and Scheduling of a Flexible Manufacturing Plant Based on the Bees Algorithm
	1 Introduction
		1.1 Background
		1.2 Literature Review
	2 Mathematical Modelling
		2.1 Objective Function
		2.2 Boundary Conditions
		2.3 Encoding and Decoding
	3 The Bees Algorithm
		3.1 BA with Site Abandonment Technique
		3.2 Local Search
	4 Simulation Results
		4.1 Results of BA
		4.2 Results of the Improved Bees Algorithm
		4.3 Comparison of Results
	5 Conclusions
	References
Application of the Dual-population Bees Algorithm in a Parallel Machine Scheduling Problem with a Time Window
	1 Introduction
	2 Model
		2.1 Symbols and Variables
		2.2 Problem Description
		2.3 Mathematical Model
	3 Dual-population Bees Algorithm
		3.1 Overall Flow of the Algorithm
		3.2 Coding
		3.3 Scout Bee Stage
		3.4 Forager Bee Stage
		3.5 Elite Bee Stage
		3.6 Population Dynamic Adjustment
		3.7 Fitness Function
	4 Experiment
	5 Conclusion
	References
A Parallel Multi-indicator-Assisted Dynamic Bees Algorithm for Cloud-Edge Collaborative Manufacturing Task Scheduling
	1 Introduction
	2 Modeling and Problem Formulation
	3 Algorithm
		3.1 Framework
		3.2 Individual Encoding
		3.3 Pre-allocation Mechanism
		3.4 Sorting Strategy
		3.5 Improved Bees Algorithm
		3.6 Merging and Adjustment Strategy
	4 Experiments
		4.1 Comparison of Sorting Strategies
		4.2 Comparison of Adjustment Strategies
		4.3 Algorithm Performance Comparison
	5 Conclusion
	References
Logistics and Supply Chain Optimisation
Bees Traplining Metaphors for the Vehicle Routing Problem Using a Decomposition Approach
	1 Introduction
	2 Bees Algorithm
		2.1 Basic Version
		2.2 Traplining Metaphor I: Parameter Reduction (Two-Parameter BA)
		2.3 Traplining Metaphor II: Intensifier (Bees Routing Optimiser)
	3 Vehicle Routing Problem
	4 Methodology
		4.1 Clustering Procedure
		4.2 Routing Procedure
	5 Experiments, Results and Discussion
	6 Conclusion
	Appendix A: The Complete Routing Plan
	Appendix B: Details of the Clustering Method
	Appendix C: Acronyms and Symbols
	References
Supply Chain Design and Multi-objective Optimisation with the Bees Algorithm
	1 Introduction
	2 Supply Chain Network Design Problem
		2.1 Mathematical Model
	3 The Bees Algorithm Approach
		3.1 Notes on the Foraging Behaviour of Honeybees
		3.2 Notes on the Bees Algorithm
	4 Bees Algorithm Approach for Solving the Multi-objective Supply Chain Optimisation Problem
		4.1 Numerical Example
	5 Conclusions
	References
Remanufacturing
Collaborative Optimisation of Robotic Disassembly Planning Problems using the Bees Algorithm
	1 Introduction
	2 Literature Review
	3 The Collaborative Optimization Problem
		3.1 Assumption, Definition and Workflow
		3.2 Feasible Disassembly Sequence Generation
		3.3 Optimization Objectives and the Weights
	4 The Improved Discrete Bees Algorithm
		4.1 Representation of Bees
		4.2 Variable Neighborhood Search
		4.3 Global Search
	5 Results and Simulations
		5.1 Case Studies
		5.2 Calculating Weights of Indicators
		5.3 Performance Analysis
		5.4 Simulations Based on RoboDK
	6 Conclusion
	Appendix
	References
Optimisation of Robotic Disassembly Sequence Plans for Sustainability Using the Multi-objective Bees Algorithm
	1 Introduction
	2 Literature Review
	3 Model and Methodology
	4 Experiments
		4.1 Robotic Cell
		4.2 Key Input Data and Calculation Assumptions
	5 Results and Discussion
	6 Conclusion
	Appendix
	References
Task Optimisation for a Modern Cloud Remanufacturing System Using the Bees Algorithm
	1 Introduction
	2 The Bees Algorithm in Manufacturing and Remanufacturing Contexts
	3 The Cloud Remanufacturing Model
		3.1 Completion Time Evaluation in the Cloud Remanufacturing Model
	4 The Makespan Problem
	5 Case Study
		5.1 Experiments Settings
		5.2 Results Analysis
	6 Conclusion
	Appendix
	References
Prediction of the Remaining Useful Life of Engines for Remanufacturing Using a Semi-supervised Deep Learning Model Trained by the Bees Algorithm
	1 Introduction
	2 Related Work
	3 Proposed Semi-supervised Deep Learning Model for RUL Estimation of the NASA Turbofan Engine Dataset
		3.1 Long Short-Term Memory (LSTM) Networks
		3.2 A Modified Ternary Bees Algorithm for Training a Deep Learning Model
	4 Experiments
		4.1 Experimental Setup and Parameter Tuning
		4.2 Results
	5 Conclusion
	References
Index