Advances in Industrial Engineeringand Operations Research

Lecture Notes in Electrical Engineering

Volume 5

Advances in Industrial Engineering and Operations ResearchAlan H. S. Chan, and Sio-Iong AoISBN 978-0-387-74903-7, 2008

Advances in Communication Systems and Electrical EngineeringXu Huang, Yuh-Shyan Chen, and Sio-Iong AoISBN 978-0-387-74937-2, 2008

Time-Domain Beamforming and Blind Source SeparationJulien Bourgeois, and Wolfgang MinkerISBN 978-0-387-68835-0, 2007

Digital Noise Monitoring of Defect OriginTelman AlievISBN 978-0-387-71753-1, 2007

Multi-Carrier Spread Spectrum 2007Simon Plass, Armin Dammann, Stefan Kaiser, and K. FazelISBN 978-1-4020-6128-8, 2007

Alan H. S. Chan • Sio-Iong AoEditors

Advances in IndustrialEngineering andOperations Research

123

Edited by:Alan H.S. Chan Sio-Iong AoCity University of Hong Kong IAENG SecretariatDepartment of Manufacturing Engineering 37-39 Hung To Roadand Engineering Management Unit 1, 1/F83 Tat Chee Avenue HONG KONGKOWLOON PEOPLE’S REPUBLIC OF CHINAHONG KONG/PEOPLES REP. OF CHINA

Library of Congress Control Number: 2007935316

ISBN 978-0-387-74903-7 e-ISBN 978-0-387-74905-1

Printed on acid-free paper

c© 2008 Springer Science + Business Media, LLCAll rights reserved. This work may not be translated or copied in whole or in part without the writtenpermission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York,NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use inconnection with any form of information storage and retrieval, electronic adaptation, computer software,or by similar or dissimilar methodology now known or hereafter developed is forbidden.The use in this publication of trade names, trademarks, service marks, and similar terms, even if they arenot identified as such, is not to be taken as an expression of opinion as to whether or not they are subjectto proprietary rights.

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Preface

A large international conference on industrial engineering and operations researchwas held in Hong Kong, March 21–23, 2007, under the International Multi-Conference of Engineers and Computer Scientists (IMECS) 2007. The IMECS 2007is organized by the International Association of Engineers (IAENG), a nonprofitinternational association for engineers and computer scientists. The IMECS confer-ences serve as platforms for the engineering community to meet with each other andto exchange ideas. The conferences have also struck a balance between theory andapplication development. The conference committees consist of over 200 commit-tee members who are mainly research center heads, faculty deans, department heads,professors, and research scientists from over 30 countries. The conferences are trulyinternational meetings with a high level of participation from many countries. Theresponse that we have received for the multi-conference is excellent. There havebeen more than 1100 manuscript submissions for the IMECS 2007. All submittedpapers have gone through the peer review process, and the overall acceptance rateis 58.46%.

This volume contains revised and extended research articles on industrialengineering and operations research written by prominent researchers partici-pating in the multi-conference IMECS 2007. Topics covered include qualitymanagement systems, reliability and quality control, engineering experimental de-sign, computer-supported collaborative engineering, human factors and ergonomics,computer-aided manufacturing, manufacturing processes and methods, engineeringmanagement and leadership, optimization, transportation network design, stochasticmodeling, queuing theory, and industrial applications. The papers are representativein these subjects, sitting at the top end of the highest technologies in these fields.This book presents state-of-the-art advances in industrial engineering and opera-tions research and serves as an excellent reference work for researchers and graduatestudents working with industrial engineering and operations research.

v

Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . v

Contributors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xxi

1 A Comprehensive Movement Compatibility Study forHong Kong Chinese . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1W.H. Chan and Alan H.S. Chan1.1 Introduction 11.2 Methods 4

1.2.1 Experimental Design 41.2.2 Subjects 4

1.3 Results and Discussion 41.3.1 Response Preference and Mean Stereotype Strength 41.3.2 Reversibility 71.3.3 Response Time 8

1.4 Conclusion and Recommendations 9References 10

2 A Study of Comparative Design Satisfaction Between Cultureand Modern Bamboo Chair . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13Vanchai Laemlaksaku and Sittichai Kaewkuekool2.1 Introduction 132.2 Methodology 14

2.2.1 Design and Fabrication 142.2.2 Sample Selection 172.2.3 Questionnaire 17

2.3 Results 182.3.1 Dimensional Appropriateness of Chair 182.3.2 Comfort Level of Chair 21

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viii Contents

2.4 Conclusions 242.4.1 Dimensional Appropriateness of the Chair 242.4.2 Comfort Level 242.4.3 Aesthetic Appeal of the Modern Bamboo Chair 25

2.5 Recommendations 25References 25

3 Factors Influencing Symbol-Training Effectiveness . . . . . . . . . . . . . . . . . . 27Annie W.Y. Ng and Alan H.S. Chan3.1 Introduction 273.2 Factors Influencing Symbol-Training Effectiveness 28

3.2.1 Training Method 283.2.2 Other Training Factors 34

3.3 Experimental Design and Analysis for Symbol-TrainingEffectiveness Research 34

3.4 Conclusion 36References 36

4 Multiple-Colony Ant Algorithm with Forward–BackwardScheduling Approach for Job-Shop Scheduling Problem . . . . . . . . . . . . . 39Apinanthana Udomsakdigool and Voratas Kachitvichyanukul4.1 Introduction 394.2 Problem Definition and Graph-Based Representation 40

4.2.1 Problem Definition 404.2.2 Graph-Based Representation 414.2.3 The General Concept of ACO Algorithm 424.2.4 Memory Requirement for Ant and Colony 434.2.5 Hierarchical Cooperation in Multiple Colonies 444.2.6 Backward Scheduling Approach 45

4.3 Description of Algorithm 464.3.1 Initialize Pheromone and Parameter Setting 474.3.2 Local Improvement 504.3.3 Pheromone Updating 514.3.4 Restart Process 53

4.4 Experimental Results 534.5 Conclusion and Recommendation 53

4.5.1 Conclusion 534.5.2 Recommendation 54

References 54

5 Proposal of New Paradigm for Hand and Foot Controls in theContext of Spatial Compatibility Effect . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57Alan H.S. Chan and Ken W.L. Chan5.1 Introduction 57

5.1.1 Spatial Stimulus–Response (SR) Compatibility 58

Contents ix

5.2 Research Plan and Methodology 595.3 Experiment 1: Spatial SR Compatibility Effect of

Foot Controls 595.3.1 Design 59

5.4 Experiment 2: Spatial SR Compatibility Effect of Hand andFoot Controls 605.4.1 Design 61

5.5 Experiment 3: Spatial SR Compatibility Effect of Handand Foot Controls for Stimulus and Response Arrays onOrthogonal Planes 615.5.1 Design 62

5.6 Experiment 4: Spatial SR Compatibility Effect of Hand and FootControls for Stimulus and Response Arrays on Parallel andOrthogonal Planes 625.6.1 Design 63

5.7 Analysis 64References 64

6 Development of a Mathematical Model for Processwith S-Type Quality Characteristics to a QualitySelection Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67K. Tahera, R.N. Ibrahim, and P.B. Lochert6.1 Introduction 676.2 Model Development 696.3 Genetic Algorithm 74

6.3.1 Genetic Representation 756.3.2 Population Size 766.3.3 Generating Initial Population 766.3.4 Fitness Function 776.3.5 Selection 776.3.6 Mating or Crossover 776.3.7 Mutation 786.3.8 Termination Criteria 796.3.9 Generic Algorithm Parameters 79

6.4 Numerical Example 796.5 Conclusions 80References 80

7 Temporal Aggregation and the Production SmoothingModel: Evidence from Electronic Parts and ComponentsManufacturing in Taiwan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83Chien-wen Shen7.1 Introduction 837.2 Literature Review 847.3 Model Specifications 85

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7.4 Empirical Results 877.4.1 Tests of Production-Smoothing Hypotheses 887.4.2 Model Analyses 88

7.5 Conclusions 93References 94

8 Simulations of Gear Shaving and the Tooth Contact Analysis . . . . . . . 95Shinn-Liang Chang, Hung-Jeng Lin, Jia-Hung Liu,and Ching-Hua Hung8.1 Introduction 958.2 Mathematical Model of the Shaving Machine 968.3 Tooth Contact Analysis of the Shaved Gear 1038.4 Longitudinal Tooth Crowning Introduced by Litvin 1058.5 Conclusion 109

References 109

9 On Aggregative Methods of Supplier Assessment . . . . . . . . . . . . . . . . . . . 111Vladimır Modrak9.1 Introduction 1119.2 Research Background and Motivation 1129.3 The Importance of Suppliers Assessment and Selection 1149.4 Alternative Techniques of Supplier Assessment 116

9.4.1 Assessment of the Quality of Supplied Products 1179.4.2 Assessment by Adherence to Time Schedules 1179.4.3 Supplier Assessment with Respect to the Schedule

of Quantity 1209.4.4 Aggregative Supplier Assessment 123

9.5 Discussion and Closing Remarks 124References 124

10 Human Factors and Ergonomics for Nondestructive Testing . . . . . . . . 127B.L. Luk and Alan H.S. Chan10.1 Introduction 12710.2 Principles and Procedures 128

10.2.1 Dye Penetrant Inspection 12810.2.2 Magnetic Particles Inspection 12910.2.3 Ultrasonic Inspection 13010.2.4 Eddy Current 132

10.3 Human Abilities and Skills Required 13310.3.1 Perceptual and Cognitive Abilities 13310.3.2 Physical Strength 13510.3.3 Surface Preparation Technique 135

10.4 Ergonomics, Safety, and Health Problems 13610.4.1 Illumination 136

Contents xi

10.4.2 Working Posture 13610.4.3 Potential Chemical Hazards 138

10.5 Conclusions and Recommendations 140References 141

11 A Novel Matrix Approach to Determine Makespan forZero-Wait Batch Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143Amir Shafeeq, M.I. Abdul Mutalib, K.A. Amminudin,and Ayyaz Muhammad11.1 Introduction 14311.2 Batch Process 144

11.2.1 Makespan for Single-Product Batch Processing 14511.2.2 Makespan for Multiproduct Batch Process 146

11.3 The Matrix Method 14811.4 Application of the Matrix Method 15011.5 Conclusion 152References 153

12 Interactive Meta-Goal Programming: A Decision AnalysisApproach for Collaborative Manufacturing . . . . . . . . . . . . . . . . . . . . . . . . 155Hao W. Lin, Sev V. Nagalingam, and Grier C.I. Lin12.1 Introduction 15512.2 Decision Making in Collaborative Manufacturing 15612.3 Interactive Meta-Goal Programming-Based Decision

Analysis Framework 15712.3.1 Meta-Goals 15712.3.2 Interactive Process 16112.3.3 Interactive Meta-Goal Programming-Based Decision

Analysis Workflow 16712.4 Example 16912.5 Conclusion 171References 172

13 Nonlinear Programming Based on Particle SwarmOptimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173Takeshi Matsui, Kosuke Kato, Masatoshi Sakawa, Takeshi Uno,and Kenji Morihara13.1 Introduction 17313.2 Nonlinear Programming Problem 17413.3 Particle Swarm Optimization 17413.4 Improvement of Particle Swarm Optimization 175

13.4.1 Generation of Initial Search Positions of Particles 17613.4.2 Modified Move Schemes of a Particle 17613.4.3 Division of the Swarm into Two Subswarms 17713.4.4 Secession 178

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13.4.5 Multiple Stretching Technique 17913.4.6 The Procedure of Revised PSO 180

13.5 Numerical Example 18113.6 Conclusions 183References 183

14 A Heuristic for the Capacitated Single AllocationHub Location Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185Jeng-Fung Chen14.1 Introduction 18514.2 Previous Related Studies 18614.3 A Model 18714.4 Heuristic 188

14.4.1 Determining the Number of Hubs 18814.4.2 Selecting Hub Locations 18914.4.3 Allocating Nonhubs To Hubs 18914.4.4 Heuristic SATLCHLP 190

14.5 Computational Results 19014.5.1 Australia Post Data Set 19114.5.2 Results 191

14.6 Conclusions and Suggestions for Future Research 194References 195

15 Multimodal Transport: A Framework for Analysis . . . . . . . . . . . . . . . . . 197Mark K.H. Goh, Robert DeSouza, Miti Garg, Sumeet Gupta,and Luo Lei15.1 Introduction 19715.2 Literature Review 19915.3 Theoretical Framework 20015.4 Research Methodology 20115.5 Case Study 202

15.5.1 Railways 20315.5.2 Road 20315.5.3 Maritime 20315.5.4 Multimodal 204

15.6 Future Directions for Research 207References 208

16 Fractional Matchings of Graphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209Jiguo Yu and Baoxiang Cao16.1 Terminology and Notation 20916.2 Basic Results on Fractional Matching 21016.3 Fractional Factor-Critical Graph 21116.4 Fractional Deleted Graphs 21516.5 Fractional Covered Graphs 21716.6 Fractional Extendable Graph 220

Contents xiii

16.7 Conclusion 224References 224

17 Correlation Functions for Dynamic Load Balancingof Cycle Shops . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227Claudia Fiedler and Wolfgang Meyer17.1 Problem Statement 22717.2 Load-Balancing Systems: State of the Art 22917.3 Process Plan and Resource Model 23017.4 Theory of Correlation Scheduling 232

17.4.1 Two Processes Being Sent to the Plant 23217.4.2 Three Processes Being Sent to the Plant 23317.4.3 Generalization to n Processes 233

17.5 Dynamic Scheduling 23417.5.1 Collision Functions 23417.5.2 Scheduling Procedure 235

17.6 Load Balancing 23717.6.1 Load Balancing at System Level 23717.6.2 Load Balancing at Subsystem Level 238

17.7 Conclusion 242References 243

18 Neural Network-Based Integral Sliding Mode Controlfor Nonlinear Uncertain Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 245S.W. Wang and D.L. Yu18.1 Introduction 245

18.1.1 Sliding Mode Control 24518.1.2 Integral Sliding Mode Control 24618.1.3 Radial Basis Function Neural Network Approximation 246

18.2 Problem Statement 24718.3 New Integral Sliding Surface 24818.4 Sliding Mode Control Law 25318.5 Numerical Example 25418.6 Conclusions 256References 256

19 Decentralized Neuro-Fuzzy Control of a Class ofNonlinear Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 259Miguel A. Hernandez and Yu Tang19.1 Introduction 25919.2 Problem Statement 26019.3 Recurrent Neuro-fuzzy Networks 26219.4 Design of the Decentralized Control 263

19.4.1 Control Law 26319.4.2 Stability Analysis 264

19.5 Output Feedback 269

xiv Contents

19.6 Experimental Results 26919.7 Conclusions 272References 273

20 A New Training Algorithm of Adaptive Fuzzy Control forChaotic Dynamic Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 275Chun-Fei Hsu, Bore-Kuen Lee, and Tsu-Tian Lee20.1 Introduction 27520.2 Problem Formulation 27720.3 Design of AFC with PID-Type Learning Algorithm 278

20.3.1 Approximation of Fuzzy System 27820.3.2 Design of PID-AFC 27920.3.3 Design of PID-AFC with Bound Estimation 281

20.4 Simulation Results 28420.5 Conclusions 287References 289

21 General-Purpose Simulation Management for SatelliteNavigation Signal Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291Ge Li, Xinyu Yao, and Kedi Huang21.1 Introduction 29121.2 The Real-Time Application Requirements 292

21.2.1 Requirements for the Simulation Architecture 29221.2.2 Requirements of the Real-Time Calculation for the

High-Fidelity Model 29221.2.3 Requirements of the Data Communication for

Different Layers 29321.2.4 Requirements of the Real-Time Simulation Engine 293

21.3 A General-Purpose Architecture for Satellite Navigation SignalSimulation 293

21.4 General-Purpose Real-Time Distributed SimulationManagements 29521.4.1 Experiment Design and Management Techniques 29521.4.2 Simulation Database Techniques 29621.4.3 Simulation Management Techniques 29721.4.4 System Scalability Realization 298

21.5 Conclusions 298References 299

22 Multilayered Quality-of-Service Architecture with Cross-layerCoordination for Teleoperation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301X.U. Lei and L.I. Guo-dong22.1 Introduction 30122.2 Network Performance Parameters Analysis 30222.3 Architectural Framework 303

Contents xv

22.4 Communication Network QoS Enhancement 30522.4.1 Network Layer QoS Optimization 30522.4.2 Data Link Layer QoS Optimization 306

22.5 Resource Network QoS Enhancement 30722.5.1 Transport Layer QoS Optimization 30722.5.2 Presentation Layer QoS Enhancing 30822.5.3 Session Layer QoS Supervision 30922.5.4 Application Layer QoS 309

22.6 Cross-layer Coordination and Adaptation 30922.7 Application Scenarios 31122.8 Conclusion 313References 313

23 Improvement of State Estimation for Systems withChaotic Noise . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315Pitikhate Sooraksa and Prakob Jandaeng23.1 Introduction 31523.2 Improvement of Adaptive Kalman Filtering 31623.3 Results 319

23.3.1 Model 31923.3.2 Computer Simulation 319

23.4 Conclusion 325References 325

24 Combined Sensitivity–Complementary Sensitivity Min–MaxApproach for Load Disturbance–Setpoint Tradeoff Design . . . . . . . . . . 327Ramon Vilanova and Orlando Arrieta24.1 Introduction 32724.2 Problem Formulation 328

24.2.1 PID Controller 32924.2.2 Process Model 32924.2.3 Design Problem Formulation 329

24.3 Solution to the Optimal Approximation Problem 33124.4 Step Response Tuning 33324.5 Disturbance Attenuation Tuning 33524.6 Example 33624.7 Trade-off Tuning 33824.8 Conclusions 342References 342

25 Nonlinear Adaptive Sliding Mode Control for a RotaryInverted Pendulum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345Yanliang Zhang, Wei Tech Ang, Jiong Jin, Shudong Zhang,and Zhihong Man25.1 Introduction 345

xvi Contents

25.2 Background 34625.2.1 Mathematical Model of System 34625.2.2 Sliding Mode Control 34725.2.3 Adaptive Control 348

25.3 Sliding Mode Control: Design and Simulation 34825.3.1 Linear Sliding Mode Control 34825.3.2 Nonlinear Sliding Mode Control 350

25.4 Nonlinear Adaptive Sliding Mode Control Designand Simulation 35325.4.1 System Parameters 35325.4.2 Parameter Selection 356

25.5 Experimental Results 35725.6 Conclusion 359References 359

26 Robust Load Frequency Sliding Mode Control Based onUncertainty and Disturbance Estimator . . . . . . . . . . . . . . . . . . . . . . . . . . . . 361P.D. Shendge, B.M. Patre, and S.B. Phadke26.1 Introduction 36126.2 Dynamic Model for Load Frequency Control 36226.3 Model Following and UDE-Based Control Law 36426.4 Design of Control 364

26.4.1 Uncertainty and Disturbance Estimationwith First-Order Filter 366

26.4.2 Uncertainty and Disturbance Estimationwith Second-Order Filter 366

26.4.3 Uncertainty and Disturbance Estimationwith nth-Order Filter 367

26.5 Model Following and UDE Based LFC 36826.6 Results 37026.7 Conclusion 373References 373

27 Robust Intelligent Motion Control for Linear PiezoelectricCeramic Motor System Using Self-constructing Neural Network . . . . 375Chun-Fei Hsu, Bore-Kuen Lee, and Tsu-Tian Lee27.1 Introduction 37527.2 Problem Formulation 37727.3 Robust Intelligent Motion Controller Design 378

27.3.1 Description of SCNN 37927.3.2 Approximation of SCNN 38127.3.3 Design of RIMC 382

27.4 Experimental Results 38427.5 Conclusions 388References 390

Contents xvii

28 Development of Hybrid Magnetic Bearings System forAxial-Flow Blood Pump . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391Lim Tau Meng and Cheng Shanbao28.1 Introduction 39128.2 Design of Axial-flow Blood Pump 39228.3 Principles of Magnetic Bearings 39428.4 Principles of Lorentz-type Motor 39428.5 Control of the HMBs System 39628.6 Performance of the HMBs System 39728.7 Conclusions and Future Work 399References 399

29 Critical Angle for Optimal Correlation Assignment to ControlMemory and Computational Load Requirements in a DenselyPopulated Target Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 401D.M. Akbar Hussain and Zaki Ahmed29.1 Introduction 40229.2 Critical Angle Representation 40229.3 Motion Model Consideration 40429.4 Implementation 40629.5 Performance Parameter 40729.6 Simulation Results 40829.7 Conclusion 412References 413

30 High-Precision Finite Difference Method Calculations ofElectrostatic Potential . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415David Edwards, Jr.30.1 Introduction 415

30.1.1 Historical Development 1970–2007 41530.1.2 Brief Description of the Process 416

30.2 Construction of Order-10 Algorithm for General Mesh Points andthe Definition of the grad6 Function 417

30.3 Properties of the grad6 Function and the Definition of theMaximum-Error Function 419

30.4 Comparison of Different Algorithms for the Two-TubeZero-Gap Lens 423

30.5 Application to Region Construction 42530.6 Dependence of Algorithm Precision upon the Set of

Surrounding Points 42630.7 Notes of Caution 42830.8 Summary and Conclusion 430Appendix A 431References 432

xviii Contents

31 Newton–Tau Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433Karim Ivaz and Bahram Sadigh Mostahkam31.1 Introduction 43331.2 Solving Nonlinear Fredholm Integral Equation 433

31.2.1 Formulation of the Problem 43331.2.2 Application of the Newton Method 43431.2.3 Application of the Tau Method 43531.2.4 Numerical Examples 436

31.3 Solving a System of Nonlinear Integral Equations 43831.3.1 Formulation of the Problem 43831.3.2 Application of the Newton Method to SNFIE 44031.3.3 The Tau Method Applied to (8) 44131.3.4 Numerical Examples 443

31.4 Solving Nonlinear Integro-Differential Equation 44531.4.1 Formulation of the Problem 44531.4.2 Application of the Newton Method 44731.4.3 Application of the Tau Method 44831.4.4 Numerical Examples 451

References 452

32 Reconfigurable Hardware Implementation of the SuccessiveOverrelaxation Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 453Safaa J. Kasbah, Ramzi A. Haraty, and Issam W. Damaj32.1 Introduction 45332.2 Description of the Algorithm 45532.3 Reconfigurable Computing 455

32.3.1 Hardware Compilation 45632.3.2 Handel-C Language 457

32.4 Hardware Implementation of SOR 45932.5 Experimental Results 46232.6 Conclusion 464References 464

33 Tabu Search Algorithm Based on Strategic Oscillation forNonlinear Minimum Spanning Tree Problems . . . . . . . . . . . . . . . . . . . . . . 467Hideki Katagiri, Masatoshi Sakawa, Kosuke Kato, Ichiro Nishizaki,Takeshi Uno, and Tomohiro Hayashida33.1 Introduction 46733.2 Problem Formulation 46833.3 Summary of Tabu Search 46933.4 Tabu Search Algorithm Based on Strategic Oscillation for

Nonlinear MST Problems 46933.4.1 Initial Solution 47033.4.2 Neighborhood Structure and Local Search 47133.4.3 Tabu List and Aspiration Criterion 471

Contents xix

33.4.4 Strategic Oscillation 47133.4.5 Diversification 472

33.5 Numerical Experiment 47233.6 Conclusion 475References 475

34 Customization of Visual Lobe Measurement System for Testingthe Effects of Foveal Load . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 477Cathy H.Y. Chiu and Alan H.S. Chan34.1 Introduction 47734.2 Design 480

34.2.1 Additional Features 48034.2.2 Stimuli 48134.2.3 Software 48334.2.4 Apparatus 48334.2.5 Output 483

34.3 Conclusion 484References 485

Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 487

Contributors

Dr. Zaki AhmedSchool of Electronic, Communication and Electrical Engineering, University ofHertfordshire, College Lane, Hatfield, Herts AL10 9AB UK, +44 1707286279,z.1.ahmad@herts.ac.uk

K.A. AmminudinChemical Engineering Program, Universiti Teknologi Petronas, 31750, Tronoh,Perak, Malaysia

Wei Tech AngSchool of Mechanical and Aerospace Engineering, Nanyang Technological Univer-sity, Blk N3, B4a-02A, Singapore 639798

Orlando ArrietaTelecommunication and System Engineering Department, ETSE, UniversitatAut‘onoma de Barcelona, 08193 Bellaterra, Barcelona, Spain, Orlando.Arrieta@uab.cat

Baoxiang CaoSchool of Computer Science, Qufu Normal University, Ri-zhao, Shandong, 276826,P. R. China, bxcao@126.com

Alan H.S. ChanDepartment of Manufacturing Engineering and Engineering Management, CityUniversity of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, alan.chan@cityu.edu.hk

Ken W.L. ChanDepartment of Manufacturing Engineering and Engineering Management, CityUniversity of Hong Kong, Kowloon Tong, Kowloon, Hong Kong, wl.chan@student.cityu.edu.hk

xxi

xxii Contributors

W.H. ChanDepartment of Manufacturing Engineering and Engineering Management, City Uni-versity of Hong Kong

Shinn-Liang ChangDepartment of Power Mechanical Engineering, National Formosa University,Huwei, Yunlin, Taiwan 632, ROC.

Jeng-Fung ChenDepartment of Industrial Engineering and Systems Management, Feng Chia Uni-versity, P.O. Box 25-097, Taichung, Taiwan, R.O.C. 40724

Cathy H.Y. ChiuDepartment of Manufacturing Engineering and Engineering Management, City Uni-versity of Hong Kong

Issam W. DamajDepartment of Electrical and Computer Engineer, Dhofar University, Salalah, Sul-tanate of Oman, i damaj@du.edu.om

David Edwards, Jr.IJL Research Center, Newark, Vt. 05871, dej@kingcon.com

Claudia FiedlerUniversity of Technology, 21071 Hamburg, Germany, claudia.fiedler@tu-harburg.de

Miti GargResearch Engineer, The Logistics Institute—Asia Pacific, Block E3A, Level 3, 7Engineering Drive 1, NUS, Singapore 117574, tlimg@nus.edu.sg

Mark K.H. GohDirector of Industry Research, The Logistics Institute—Asia Pacific, Block E3A,Level 3, 7 Engineering Drive 1, NUS, Singapore 117574, tligohkh@nus.edu.sg

L.I. Guo-dongComputer Science and Technology Department North China Electric Power Uni-versity, Beijing, 102206 P. R. China, lgdlgl bj@yahoo.com.cn

Sumeet Gupta, Ph.D.Research Fellow, The Logistics Institute—Asia Pacific, Block E3A, Level 3, 7 En-gineering Drive 1, NUS, Singapore 117574, tlisg@nus.edu.sg

Ramzi A. HaratyDivision of Computer Science and Mathematics, Lebanese American University,Beirut, Lebanon, rharaty@lau.edu.lb

Tomohiro HayashidaGraduate School of Engineering, Hiroshima University, Kagami-yama 1-4-1,Higashi-hiroshima, Hiroshima, 739-8527 Japan, hayashida@hiroshima-u.ac.jp

Contributors xxiii

Miguel A. HernandezFaculty of Engineering, National University of Mexico, FI-UNAM, P.O. Box 70-273, 04510 Mexico D.F., Mexico

Chun-Fei HsuDepartment of Electrical Engineering, Chung Hua University, Hsinchu 300, Taiwan,Republic of China, fei@chu.edu.tw

Kedi HuangInstitute for Automation, National University of Defense Technology, Changsha,Hunan, China, 410073

Ching-Hua HungMechanical Engineering Department, National Chiao Tung University, Hsinchu,Taiwan 300, ROC.

D.M. Akbar HussainInformation and Security Analysis Research Centre, Department of Computer Sci-ence and Engineering, Aalborg University, Niels Bohs Vej 8, 6700, Esbjerg, Den-mark, akbar@aaue.dk, http://www.cs.aue.auc.dk/∼akbar/

R. N. IbrahimMechanical Engineering, Monash University, Wellington Road, Clayton 3800, Aus-tralia, Raafat.Ibrahim@eng.monash.edu.au

Karim IvazDepartment of Mathematical Sciences, University of Tabriz, Tabriz, Iran, ivaz@tabrizu.ac.ir

Prakob JandaengDepartment of Information Engineering, Faculty of Engineering, King Mongkut’sInstitute of Technology Ladkrabang, Chalongkrung Rd., Ladkrabang, Bangkok,Thailand 10520

Jiong JinSchool of Mechanical and Aerospace Engineering, Nanyang Technological Univer-sity, Blk N3, B4a-02A, Singapore 639798

Voratas KachitvichyanukulThe Department of Industrial System Engineering, School of Engineering and Tech-nology, Asian Institute of Technology, P.O. Box 4, Klong Luang, Pathumthani12120, Thailand

Sittichai KaewkuekoolDepartment of Production Technology Education, King Mongkut’s University ofTechnology Thonburi, Thailand

Safaa J. KasbahDivision of Computer Science and Mathematics, Lebanese American University,Beirut, Lebanon, rharaty@lau.edu.lb

xxiv Contributors

Hideki KatagiriGraduate School of Engineering, Hiroshima University, Kagami-yama 1-4-1,Higashi-hiroshima, Hiroshima, 739-8527 Japan, katagiri-h@hiroshima-u.ac.jp

Kosuke KatoGraduate School of Engineering, Hiroshima University, Kagami-yama 1-4-1,Higashi-hiroshima, Hiroshima, 739-8527 Japan, kato@msl.sys.hiroshima-u.ac.jp

Vanchai LaemlaksakuDepartment of Industrial Engineering Technology, King Mongkut’s Institute ofTechnology North Bangkok, Thailand

Bore-Kuen LeeDepartment of Electrical Engineering, Chung Hua University, Hsinchu 300, Taiwan,Republic of China, bklee@chu.edu.tw

Tsu-Tian LeeDepartment of Electrical Engineering, National Taipei University of Technology,Taipei 106, Taiwan, Republic of China, ttlee@ntut.edu.tw

Luo Lei, Ph.D.Research Fellow, The Logistics Institute—Asia Pacific, Block E3A, Level 3, 7 En-gineering Drive 1, NUS, Singapore 117574, tlilll@nus.edu.sg

X.U. LeiProfessor, Computer Science and Technology Department, North China ElectricPower University, Beijing, P. R. China, xl@ncepu.edu.cn

Ge LiInstitute for Automation, National University of Defense Technology, Changsha,Hunan, China, 410073, geli@nudt.edu.cn

Grier C.I. LinCentre for Advanced Manufacturing Research, University of South Australia, SA5095 Australia (e-mail: grier.lin@unisa.edu.au)

Hao W. LinCentre for Advanced Manufacturing Research, University of South Australia, SA5095 Australia (61-08-8302-3112; fax: 61-08-8302-5292; e-mail: hao.lin@post-grads.unisa.edu.au)

Hung-Jeng LinDepartment of Power Mechanical Engineering, National Formosa University,Huwei, Yunlin, Taiwan 632, ROC.

Jia-Hung LiuMechanical Engineering Department, National Chiao Tung University, Hsinchu,Taiwan 300, ROC.

P.B. LochertMechanical Engineering, Monash University, Wellington Road, Clayton 3800, Aus-tralia, plochert@bigpond.net.au

Contributors xxv

B.L. LukCity University of Hong Kong, Kowloon Tong, Hong Kong

Zhihong ManSchool of Mechanical and Aerospace Engineering, Nanyang Technological Univer-sity, Blk N3, B4a-02A, Singapore 639798

Lim Tau MengAssociate Professor, Nanyang Technological University, Singapore, mtmlim@ntu.edu.sg

Wolfgang MeyerUniversity of Technology, 21071 Hamburg, Germany, w.meyer@tu-harburg.de

Takeshi MatsuiGraduate School of Engineering, Hiroshima University, fmatsui@msl.sys.hiroshima-u.ac.jp

Vladimır ModrakTechnical University of Kosice, Faculty of Manufacturing Technologies, Bayerova1, 080 01 Presov, Slovakia

Kenji MoriharaGraduate School of Engineering, Hiroshima University, moriharag@msl.sys.hiroshima-u.ac.jp

Bahram Sadigh MostahkamDepartment of Mathematical Sciences, University of Tabriz, Tabriz, Iran,mostahkam@gmail.com

Ayyaz MuhammadChemical Engineering Program, Universiti Teknologi Petronas, 31750, Tronoh,Perak, Malaysia

M.I. Abdul MutalibChemical Engineering Program, Universiti Teknologi Petronas, 31750, Tronoh,Perak, Malaysia

Sev V. NagalingamCentre for Advanced Manufacturing Research, University of South Australia, SA5095 Australia (e-mail: sev.nagalingam@unisa.edu.au)

Annie W.Y. NgDepartment of Manufacturing Engineering and Engineering Management, City Uni-versity of Hong Kong

Ichiro NishizakiGraduate School of Engineering, Hiroshima University, Kagami-yama 1-4-1,Higashi-hiroshima, Hiroshima, 739-8527 Japan, nisizaki@hiroshima-u.ac.jp

B.M. PatreSGGS Institute of Engineering and Technology, Vishnupuri, Nanded - 431 606,India, bmpatre@yahoo.com

xxvi Contributors

S.B. PhadkeDefence Institute of Advanced Technology, Pune- 411 025, India, sbphadke@hotmail.com

Masatoshi SakawaGraduate School of Engineering, Hiroshima University, Kagami-yama 1-4-1,Higashi-hiroshima, Hiroshima, 739-8527 Japan, sakawa@msl.sys.hiroshima-u.ac.jp

Amir ShafeeqChemical Engineering Program, Universiti Teknologi Petronas, 31750, Tronoh,Perak, Malaysia

Cheng ShanbaoPh.D. research student, Nanyang Technological Univer-sity, Singapore, chen0181@ntu.edu.sg

Chien-wen ShenDepartment of Logistics Management, National Kaohsiung First University of Sci-ence and Technology, Taiwan

P.D. ShendgeCollege of Engineering, Pune-411 005, India, pdshendge@gmail.com

Pitikhate SooraksaDepartment of Information Engineering, Faculty of Engineering, King Mongkut’sInstitute of Technology Ladkrabang, Chalongkrung Rd., Ladkrabang, Bangkok,Thailand 10520, kspitikh@kmitl.ac.th

Robert De Souza, Ph.D.Executive Director, The Logistics Institute—Asia Pacific, Block E3A, Level 3, 7Engineering Drive 1, NUS, Singapore 117574, tlihead@nus.edu.sg

K. TaheraMechanical Engineering, Monash University, Wellington Road, Clayton 3800,Australia, Khadiza.Tahera@eng.monash.edu.au

Yu TangFaculty of Engineering, National University of Mexico, FI-UNAM, P.O.Box 70-273, 04510 Mexico D.F., Mexico

Takeshi UnoGraduate School of Engineering, Hiroshima University, Kagami-yama 1-4-1,Higashi-hiroshima, Hiroshima, 739-8527 Japan, uno@msl.sys.hiroshima-u.ac.jp

Apinanthana UdomsakdigoolThe department of Industrial Engineering Technology, College of IndustrialTechnology, King Mongkut’s Institute of Technology North Bangkok, 1518 Pibul-songkram Road, Bang Sue, Bangkok, 10800, Thailand

Contributors xxvii

Ramon VilanovaTelecommunication and System Engineering Department, ETSE, UniversitatAut‘onoma de Barcelona, 08193 Bellaterra, Barcelona, Spain, Ramon.Vilanova@uab.cat

S.W. WangWeihai Yuanhang Technology Development Co., Ltd. 19 Tangshan Road, Hi-techDistrict, Weihai, Shandong, 264209, P.R. China, wsw@wh-yuanhang.com

Xinyu YaoInstitute for Automation, National University of Defense Technology, Changsha,Hunan, China, 410073

D.L. YuControl Systems Research Group, School of Engineering, Liverpool John MooresUniversity, Byrom Street, Liverpool, L3 3AF, UK, d.yu@ljmu.ac.uk

Jiguo YuSchool of Computer Science, Qufu Normal University, Ri-zhao, Shandong, 276826,P. R. China, jiguoyu@sina.com

Shudong ZhangSchool of Mechanical and Aerospace Engineering, Nanyang Technological Univer-sity, Blk N3, B4a-02A, Singapore 639798

Yanliang ZhangSchool of Mechanical and Aerospace Engineering, Nanyang Technological Univer-sity, Blk N3, B4a-02A, Singapore 639798