Table Of ContentSpringer Series in Advanced Manufacturing
Duc Truong Pham
Natalia Hartono Editors
Intelligent
Production and
Manufacturing
Optimisation—
The Bees Algorithm
Approach
Springer Series in Advanced Manufacturing
Series Editor
Duc Truong Pham, University of Birmingham, Birmingham, UK
The Springer Series in Advanced Manufacturing includes advanced textbooks,
research monographs, edited works and conference proceedings covering all major
subjects in the field of advanced manufacturing.
The following is a non-exclusive list of subjects relevant to the series:
1. Manufacturing processes and operations (material processing; assembly; test and
inspection; packaging and shipping).
2. Manufacturing product and process design (product design; product data
management; product development; manufacturing system planning).
3. Enterprise management (product life cycle management; production planning
and control; quality management).
Emphasis will be placed on novel material of topical interest (for example, books
on nanomanufacturing) as well as new treatments of more traditional areas.
As advanced manufacturing usually involves extensive use of information and
communication technology (ICT), books dealing with advanced ICT tools for
advanced manufacturing are also of interest to the Series.
Springer and Professor Pham welcome book ideas from authors. Potential
authors who wish to submit a book proposal should contact Anthony Doyle,
Executive Editor, Springer, e-mail: [email protected].
·
Duc Truong Pham Natalia Hartono
Editors
Intelligent Production
and Manufacturing
Optimisation—The Bees
Algorithm Approach
Editors
Duc Truong Pham Natalia Hartono
Department of Mechanical Engineering Department of Mechanical Engineering
University of Birmingham University of Birmingham
Birmingham, UK Birmingham, UK
ISSN 1860-5168 ISSN 2196-1735 (electronic)
Springer Series in Advanced Manufacturing
ISBN 978-3-031-14536-0 ISBN 978-3-031-14537-7 (eBook)
https://doi.org/10.1007/978-3-031-14537-7
© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature
Switzerland AG 2023
This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether
the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse
of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and
transmission or information storage and retrieval, electronic adaptation, computer software, or by similar
or dissimilar methodology now known or hereafter developed.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication
does not imply, even in the absence of a specific statement, that such names are exempt from the relevant
protective laws and regulations and therefore free for general use.
The publisher, the authors, and the editors are safe to assume that the advice and information in this book
are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or
the editors give a warranty, expressed or implied, with respect to the material contained herein or for any
errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional
claims in published maps and institutional affiliations.
This Springer imprint is published by the registered company Springer Nature Switzerland AG
The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
With the advent of the Fourth Industrial Revolution, production and manufacturing
processes and systems have become more complex. To obtain the best performance
from them requires efficient and effective optimisation techniques that do not depend
on the availability of process or system models. Such models are usually either not
obtainable or mathematically intractable due to the high degrees of nonlinearities and
uncertainties in the processes and systems to be represented. The Bees Algorithm is a
powerful swarm-based intelligent optimisation metaheuristic inspired by the foraging
behaviour of honeybees. The algorithm is conceptually elegant and extremely easy
to apply. All it needs to solve an optimisation problem is a means to evaluate the
quality of potential solutions. This is the reason why, since the algorithm was first
published by Pham et al. in 2005, it has attracted users from virtually all fields of
engineering and natural, physical, medical and social sciences.
This book is the first work dedicated to the Bees Algorithm. Following a gentle
introduction to the main ideas underpinning the algorithm, the book divides into five
parts focusing on recent results and developments relating to the algorithm and its
use to solve optimisation problems in production and manufacturing.
Part 1 comprising four chapters presents manufacturing process optimisation
applications. The chapter “Minimising Printed Circuit Board Assembly Time Using
the Bees Algorithm with TRIZ-Inspired Operators” (by Ang and Ng) discusses
the optimisation of process plans for printed circuit board assembly. The chapter
describes how the Bees Algorithm was effectively combined with TRIZ to minimise
assembly time. The chapter “The application of the Bees Algorithm in a Digital Twin
for Optimising the Wire Electrical Discharge Machining (WEDM) Process Param-
eters” (by Packianather, Alexopoulos and Squire) deals with optimising process
parameters for wire electrical discharge machining. The authors used the Bees Algo-
rithm to obtain the best combination of process parameters for the digital twin of the
product being machined. The chapter “A Case Study with the BEE-Miner Algorithm:
Defects on the Production Line” (by Ay, Baykasoglu, Ozbakir and Kulluk) examines
defect classification in manufacturing. The authors showcase the strong performance
of Bee-Miner, a cost-sensitive classification algorithm for data mining derived from
v
vi Preface
the Bees Algorithm. The chapter “An Application of the Bees Algorithm to Pulsating
Hydroforming”(byÖ ztürk,S ¸en, Kalyoncu and Halkacı) describes the use of the Bees
Algorithm to find the parameters (pulse frequency, amplitude and base) for a test to
obtain the biaxial stress-strain curves required to control a pulsating hydroforming
machine to yield a uniform thickness distribution.
Part 2, the most voluminous section of the book, consists of seven chapters
broadly covering production equipment optimisation. The chapter “Shape Recog-
nition for Industrial Robot Manipulation with the Bees Algorithm” (by Castellani,
Baronti, Zheng and Lan) is relevant to 3D vision systems. The authors used the Bees
Algorithm to fit primitive shapes to point-cloud scenes for real-time 3D object recog-
nition. The chapter “Bees Algorithm Models for the Identification and Measurement
of Tool wear” (by d’Addona) describes the use of the Bees Algorithm for tool wear
identification and measurement during turning operations. The author’s goal was to
use the bees to define the contours of the wear area of a tool and locate the point
of maximum wear. The chapter “Global Optimisation for Point Cloud Registration
with the Bees Algorithm” (by Lan, Castellani, Wang and Zheng) complements the
previous chapter “Shape Recognition for Industrial Robot Manipulation with the Bees
Algorithm” and looks at the problem of finding a spatial transformation that aligns
two point clouds. The authors employed singular value decomposition to increase
the search efficiency of the Bees Algorithm, achieving higher consistency, precision
and robustness than the popular Iterative Closest Point method. The chapter “Auto-
matic PID Tuning Toolkit Using the Multi-Objective Bees Algorithm” (byS ¸ahin
and Çakırog˘lu) investigates the tuning of PID control systems such as those for
robotic equipment. A multi-objective Bees Algorithm was successfully applied to
minimise the settling time, rise time, overshoot and system error all at once. The
chapter “The Effect of Harmony Memory Integration into the Bees Algorithm”(by
Acar, Sag˘lam and S¸aka) compares the original Bees Algorithm and a hybrid version
that incorporates a harmony memory on the design of spherical four-link mecha-
nisms for robot grippers. The results obtained by the authors show the enhancement
afforded by hybridisation. The chapter “Memory-Based Bees Algorithm with Lévy
Flights for Multilevel Image Thresholding” (by Shatnawi, Sahran and Nasrudin) also
concerns enhancing the Bees Algorithm. An earlier memory-based version proposed
by the authors was improved by adding a Lévy search facility to reduce the number
of parameters that users need to select and applied successfully to multilevel thresh-
olding, a basic image processing function in computer vision. The chapter “ANew
Method to Generate the Initial Population of the Bees Algorithm for Robot Path
Planning in a Static Environment” (by Kashkash, Darwish and Joukhadar) presents
a modified Bees Algorithm with a new initial population generation method and
describes its use to find the shortest collision-free path for a mobile robot. Against
other algorithms tested by the authors, the modified algorithm demonstrated the best
performance.
Preface vii
Part 3 deals with production plan optimisation and includes three chapters. The
chapter “Method for the Production Planning and Scheduling of a Flexible Manufac-
turing Plant Based on the Bees Algorithm” (by Wang, Chen and Li) relates to produc-
tion planning and scheduling for a sheet metal fabrication plant. The authors found
that both the basic Bees Algorithm and the version implementing the site abandon-
ment strategy yielded good results, with the latter providing a superior performance
due to its ability to escape from local optima, as expected. The chapter “Appli-
cation of the Dual-population Bees Algorithm in a Parallel Machine Scheduling
Problem with a Time Window” (by Song, Xing and Chen) covers the solution of
the parallel machine scheduling problem with time windows. The authors employed
a Bees Algorithm with two populations: a search population of scout bees and a
supplementary population of forager bees. The purpose of having this dual popu-
lation was to increase the speed of convergence of the algorithm, enabling it to
find better solutions than could be achieved by the standard algorithm and other
optimisation procedures. The chapter “Parallel Multi-indicator-Assisted Dynamic
Bees Algorithm for Cloud-Edge Collaborative Manufacturing Task Scheduling”(by
Li, Peng, Laili and Zhang) discusses task scheduling for a cloud-edge collabora-
tive manufacturing environment. The authors present a dynamic version of the Bees
Algorithm in which the operators were adjusted according to a set of indicators, and
a parallel sorting scheme was adopted to accelerate the scheduling and selection of
cloud-edge resources and collaboration modes.
Part 4 comprises two chapters related to logistics and supply chain optimisa-
tion. The chapter “Bees Traplining Metaphors for the Vehicle Routing Problem
Using a Decomposition Approach” (by Ismail and Pham) describes the use of
the latest and simplest incarnation of the Bees Algorithm to solve the capacitated
vehicle routing problem. The algorithm which integrates exploration and exploita-
tion requires the setting of only two parameters, the number of scout bees and the
number of bees recruited by the scout that found the best flower patch. To speed
up the solution, a decomposition approach was adopted whereby customers were
first clustered before the optimal route was found for each cluster. The chapter
“Supply Chain Design and Multi-objective Optimisation with the Bees Algorithm”
(by Mastrocinque) studies the Bees Algorithm as a tool for optimising supply chain
networks and considers the case of designing a supply chain for a bulldozer based
on its Bill of Materials. The author shows that the Bees Algorithm performed better
than Ant Colony Optimisation, another well-known nature-inspired algorithm.
Part 5 the final section of the book, contains four chapters about remanufacturing
optimisation. The chapter “Collaborative Optimisation of Robotic Disassembly Plan-
ning Problems using the Bees Algorithm” (by J Liu, Q Liu, Zhou, Pham, Xu and Fang)
focuses on planning robotic disassembly for remanufacturing. The authors employed
a discrete Bees Algorithm simultaneously to optimise disassembly sequences and
balance the disassembly line, with the analytic process network assigning weights
to the different optimisation objectives. As it title implies, the chapter “Optimisation
of Robotic Disassembly Sequence Plans for Sustainability Using the Multi-objective
Bees Algorithm” (by Hartono, Ramirez and Pham) also deals with multi-objective
optimisation. The aim was to devise robotic disassembly sequence plans to achieve
viii Preface
maximum profit while minimising energy consumption and greenhouse gas emis-
sions. The authors compared a multi-objective Bees Algorithm (MOBA) against the
Non-dominated Sorting Genetic Algorithm II and the Pareto Envelope-based Selec-
tion Algorithm II and found MOBA to produce the best disassembly plans for the two
gear pumps studied. The chapter “Task Optimisation for a Modern Cloud Remanufac-
turing System Using the Bees Algorithm” (by Caterino, Fera, Macchiaroli and Pham)
proposes the concept of cloud remanufacturing and discusses the application of the
Bees Algorithm to task allocation in a cloud remanufacturing system. The chapter
describes a full-factorial experiment to determine the best values of the algorithm
parameters, highlighting the importance of increasing the number of scout bees and
exploiting the best and elite sites. The chapter “Prediction of the Remaining Useful
Life of Engines for Remanufacturing Using a Semi-supervised Deep Learning Model
Trained by the Bees Algorithm” (by Zeybek) investigates using the Bees Algorithm
to train a Long Short-Term Memory (LSTM) deep learning network to predict the
remaining useful life (RUL) of turbofan engines before they are available for reman-
ufacturing. The author proposes a modified version of the ternary Bees Algorithm
which has a minimal population of only three scout bees, each representing an LSTM
model. The results obtained show that the trained model could predict engine RUL
with an accuracy as high as 98%.
In assembling this small sample of applications of the Bees Algorithm, we want
to demonstrate the simplicity, effectiveness and versatility of the algorithm. We hope
this will encourage its further adoption and development by engineers and researchers
across the world to realise smart and sustainable manufacturing and production in
the age of Industry 4.0 and beyond.
Birmingham, UK Duc Truong Pham
Natalia Hartono
Acknowledgements
Our warm thanks go to the authors for their contributions to the book and, more impor-
tantly, to the development, application and dissemination of the Bees Algorithm. The
field of nature-inspired optimisation is richer through their work.
Numerous people have added value to the book, in particular, the committee
members of the 2021 International Workshop on the Bees Algorithm and its Applica-
tions, by critically reviewing and helping to improve submissions. We acknowledge
the input of Dr Luca Baronti, Dr Turki Binbakir, Dr Marco Castellani, Dr Mario
Caterino, Dr Naila Fares, Dr Fabio Fruggiero, Dr Asrul Harun Ismail, Ms Kaiwen
Jiang, Dr Joey Lim, Dr Murat Sahin and Dr Sultan Zeybek. Professor Zude Zhou
and Professor Wenjun Xu, the keynote speakers at the workshop, set the scene for
the book and deserve special credit.
The book was produced with the patient and expert support of Springer’s Executive
Editor Mr Anthony Doyle and his colleagues Mr Subodh Kumar Mohar Sahu, Mr
Prashanth Ravichandran, Ms Kavitha Sathish, Ms Vidyalakshmi Velmurugan and
Mr Manju Ramanathan to whom we express our sincere appreciation.
ix
