MengChu Zhou is a Distinguished Professor of Electrical and Computer Engineering at the New Jersey Institute of Technology (NJIT), USA. He is an Associate Editor of IEEE Transactions on Systems, Man, and Cybernetics: Systems, and is a fellow of IEEE, IFAC, and AAAS.

Han-Xiong Li is a Professor in the Department of Systems Science and Engineering and Engineering Management at the City University of Hong Kong, HK. Dr. Li Serves as an Associate Editor of IEEE Transactions on Cybernetics, and IEEE Transactions on Industrial Electronics. He is a Fellow of the IEEE.

Contributors xxiii

Preface xxix

I Systems Science are Engineering Methodologies 1

1 A Systems Framework For Sustainability 3
Ali G. Hessami, Feng Hsu, are Hamid Jahankhani

1.1 Introduction 3

1.2 A Unified Systems Sustainability Concept 5

1.3 Sustainability Assurance: the Framework 6

1.3.1 Weighted Factors Analysis 6

1.3.2 the Framework 7

1.3.3 the Macro Concept of a Sustainable Architecture (G4.1) 10

1.3.4 the Micro Concept of a Sustainable System 11

1.3.5 A Top-Down Hierarchy of a Multi-Level Sustainability Concept 12

1.4 Technological Sustainability Case Study—Information Systems Security 13

1.4.1 Network Security as a Business Issue 14

1.4.2 the Focus of Investment on Network Security 15

1.5 Conclusions 17

References 18

2 System of Systems Thinking In Policy Development: Challenges are Opportunities 21
Keith W. Hipel, Liping Fang, are Michele Bristow

2.1 Introduction 21

2.1.1 A World in Crisis 21

2.1.2 System of Systems 23

2.2 Value Systems are Ethics 26

2.2.1 Conflicting Value Systems 27

2.2.2 Modeling Value Systems 28

2.3 Complex Adaptive Systems 32

2.3.1 Emergent Behavior 32

2.3.2 Modeling Complex Systems 34

2.4 Risk, Uncertainty, are Unpredictability 37

2.4.1 Risk Management 37

2.4.2 Modeling Risk are Adaptation Processes 40

2.5 System of Systems Modeling are Policy Development 42

2.5.1 Global Food System Model 43

2.5.2 Policy Implications 51

2.6 Conclusions 58

References 59

3 Systemic Yoyos: An Intuition are Playground For General Systems Research 71
Yi Lin, Yi Dongyun, are Zaiwu Gong

3.1 Introduction 71

3.1.1 the Concept of General Systems 72

3.1.2 A Look at the Success of Calculus-Based Theories 75

3.1.3 Whole Evolution are Yoyo Fields 78

3.2 Theoretical are Empirical Justifications 81

3.2.1 Transitional Changes in Whole Evolutions 81

3.2.2 Quantitative Infinity are Equal Quantitative Effects 83

3.2.3 Fluid Circulation, Informational Infrastructure, are Human Communications 86

3.3 Elementary Properties of Yoyo Fields 91

3.3.1 Eddy are Meridian Fields 91

3.3.2 Interactions Between Systemic Yoyos 94

3.3.3 Laws on State of Motion 98

3.4 Applications in Social Sciences 102

3.4.1 Systemic Structures of Civilizations 102

3.4.2 Systemic Structures Beneath Business Organizations 108

3.4.3 Systemic Structure in Human Mind 109

3.5 Applications in Economics 113

3.5.1 Becker’s Rotten Kid Theorem 113

3.5.2 Interindustry Wage Differentials 117

3.5.3 Price Behaviors of Projects 122

3.6 Applications in the Foundations of Mathematics 127

3.6.1 Historical Crises in the Foundations of Mathematics 128

3.6.2 Actual are Potential Infinities 131

3.6.3 Vase Puzzle are the Fourth Crisis 132

3.7 Applications in Extreme Weather Forecast 137

3.7.1 V-3𝜃 Graphs: A Structural Prediction Method 137

3.7.2 Digitization of Irregular Information 140

3.8 Conclusions 143

References 146

4 Grey System: Thinking, Methods, are Models With Applications 153
Sifeng Liu, Jeffrey Y.L. Forrest, are Yingjie Yang 4.1 Introduction 153

4.1.1 Inception are Growth of Grey System Theory 153

4.1.2 Basics of Grey System 155

4.2 Sequence Operators 157

4.2.1 Buffer Operators 158

4.2.2 Generation of Grey Sequences 160

4.2.3 Exponentiality of Accumulating Generations 162

4.3 Grey Incidence Analysis 163

4.3.1 Grey Incidence Factors are Set of Grey Incidence Operators 163

4.3.2 Degrees of Grey Incidences 164

4.3.3 General Grey Incidence Models 165

4.3.4 Grey Incidence Models Based on Similarity and Nearness 167

4.4 Grey Cluster Evaluation Models 168

4.4.1 Grey Incidence Clustering 169

4.4.2 Grey Variable Weight Clustering 169

4.4.3 Grey Fixed Weight Clustering 171

4.4.4 Grey Evaluation Using Triangular Whitenization Functions 172

4.4.5 Practical Applications 175

4.5 Grey Prediction Models 176

4.5.1 GM(1,1) Model 176

4.5.2 Improvements on GM(1,1) Models 177

4.5.3 Applicable Ranges of GM(1,1) Models 180

4.5.4 Discrete Grey Models 180

4.5.5 GM(r,h) Models 182

4.5.6 Grey System Predictions 188

4.6 Grey Models for Decision-Making 193

4.6.1 Grey Target Decisions 193

4.6.2 Multi-Attribute Intelligent Grey Target Decision Models 201

4.7 Practical Applications 202

4.7.1 To Analyze the Time Difference of Economic Indices 202

4.7.2 the Evaluation of Science are Technology Park 206

4.7.3 To Select the Supplier of Key Components of Large

Commercial Aircrafts 209

4.8 Introduction to the Software of Grey System Modeling 211

4.8.1 Features are Functions 211

4.8.2 Operation Guide 213

Acknowledgments 220

References 222

5 Building Resilience: Naval Expeditionary Command are Control 225
Christopher Nemeth, Thomas Miller, Michael Polidoro, and C. Matthew O’Connor

5.1 Introduction 225

5.2 Expeditionary Operations Command are Control 226

5.2.1 Systems Acquisition 227

5.3 Human-Centered System Development 228

5.3.1 Envisioned World Problem 229

5.3.2 Cognitive Systems Engineering 229

5.3.3 Application: Navy Expeditionary Combat Command 230

5.3.4 Reasonable Scientific Criteria 231

5.4 Discussion 232

5.4.1 Resilience Engineering 232

5.4.2 the Data Hub 234

5.4.3 Implementation Challenges 234

5.4.4 Limitations 234

5.5 Future Work 236

5.5.1 Human Performance Research 236

5.5.2 Transition from Qualitative Research to Design 236

5.5.3 Resilience Engineering 236

5.6 Conclusions 237

Acknowledgments 237

References 237

II Learning are Control 241

6 Advances are Challenges On Intelligent Learning In Control Systems 243
Ching-Chih Tsai, Kao-Shing Hwang, Alan Liu, are Chia-Feng Juang

6.1 Introduction 243

6.2 Reinforcement Learning 245

6.2.1 Reinforcement Learning 245

6.2.2 Q-Learning Algorithm 247

6.2.3 Reinforcement Learning in Robots 249

6.2.4 Soccer Robot Behaviors 250

6.2.5 Concluding Remarks 251

6.3 Bio-Inspired Evolutionary Learning Control 252

6.3.1 Bio-Inspired Evolutionary Learning Control 252

6.3.2 Bio-Inspired Evolutionary Robots 253

6.4 Intelligent Learning Control Using Fuzzy Neural Networks 254

6.4.1 Introduction 254

6.4.2 Intelligent Learning Control Using FNNs 255

6.5 Case-Based Reasoning are Learning 257

6.5.1 Case-Based Reasoning Process 257

6.5.2 Case Design are Reuse 257

6.5.3 Hybrid Learning Method Architectures in CBR 258

6.5.4 Applications in Human–Robot Interaction 259

6.6 Conclusions 260

References 261

7 Adaptive Classifiers For Nonstationary Environments 265
Cesare Alippi, Giacomo Boracchi, Manuel Roveri, Gregory Ditzler, and Robi Polikar

7.1 Introduction 265

7.2 Definition of the Problem 266

7.3 Learning Concept Drifts 268

7.4 Change Detection 272

7.4.1 Change-Detection Tests: A Review 273

7.4.2 Change-Detection Tests in Adaptive Classifiers 276

7.5 Assessing the Performance: Figures of Merit 278

7.5.1 Raw Classification Accuracy 279

7.5.2 Confusion Matrix 279

7.5.3 Geometric Mean 280

7.5.4 Precision are Recall 280

7.5.5 F-measure 281

7.5.6 Receiver Operator Characteristic Curve are Area Under the Curve 281

7.6 Conclusions 282

References 283

8 Modeling, Analysis, Scheduling, are Control of Cluster Tools In Semiconductor Fabrication 289
Nai Qi Wu, Mengchu Zhou, Feng Chu, are Sa¨ıd Mammar

8.1 Introduction 289

8.2 Cluster Tools are Their Operations 290

8.2.1 Architecture of Cluster Tools 290

8.2.2 Wafer Flow Patterns 291

8.2.3 Operation Requirements 294

8.3 Modeling are Performance Evaluation 295

8.3.1 Analysis Based on Timing Diagram Model 295

8.3.2 Analysis Based on Marked Graph 296

8.3.3 Analysis Based on Resource-Oriented Petri Nets 299

8.3.4 Discussion 302

8.4 Single Cluster Tool Scheduling 302

8.4.1 Scheduling with Wafer Residency Time Constraints 302

8.4.2 Scheduling with Both Wafer Residency Constraints and Activity Time Variation 305

8.4.3 Scheduling with Wafer Revisiting 306

8.4.4 Schedule Implementation 307

8.4.5 Discussion 307

8.5 Scheduling of Multi-cluster Tools 308

8.5.1 Deadlock Control are Scheduling of Track Systems 308

8.5.2 Schedule Optimization 309

8.5.3 Discussion 311

8.6 Conclusions 311

References 311

9 Design, Simulation, are Dynamic Control Of Large-Scale Manufacturing Process With Different Forms of Uncertainties 317
Hyunsoo Lee are Amarnath Banerjee

9.1 Introduction 317

9.1.1 Issues in Design of Large-Scale Manufacturing Processes 318

9.1.2 Simulation Model for Dynamic Control 320

9.2 Background are Literature Review 322

9.3 Different Types of Uncertainties are FCPN-std 327

9.3.1 Definition of FCPN-std 327

9.3.2 Modular Design are Five-Stage Modeling Methodology 329

9.3.3 Simulation Using FCPN-std 332

9.4 Design of Large-Scale Manufacturing Processes 333

9.5 Dynamic Control of Manufacturing Processes 335

9.6 Conclusions 339

References 340

10 Model Identification are Synthesis of Discrete-Event Systems 343
Maria Paola Cabasino, Philippe Darondeau, Maria Pia Fanti, and Carla Seatzu

10.1 Introduction 343

10.2 Background on Finite State Automata are Petri Nets 344

10.2.1 Finite State Automata 344

10.2.2 Petri Nets 346

10.3 Identification are Synthesis of Languages are Finite State Automata 347

10.4 Identification are Synthesis of Petri Nets 349

10.4.1 Synthesis from Graphs 350

10.4.2 Identification are Synthesis from Finite Languages Over T 352

10.4.3 Identification are Synthesis from Finite Languages Over E 355

10.4.4 Related Problems in the PN Framework 360

10.5 Process Mining are Workflow Problems 361

10.6 Conclusions 363

References 363

III Human–Machine Systems Design 367

11 Advances are Challenges In Intelligent Adaptive Interface Design 369
Ming Hou, Haibin Zhu, Mengchu Zhou, are Robert Arrabito

11.1 Introduction 369

11.2 Evolution of Interface Technologies are IAI Concept 372

11.2.1 Evolution of Interface Technologies 373

11.2.2 A Conceptual Framework of IAI Systems 377

11.3 Challenges of IAI Design, Alternative Solutions, are Empirical Investigations 381

11.3.1 Challenges of IAI Design 381

11.3.2 User-Centered Design Approach 382

11.3.3 Agent-Based Interface Design Approaches 383

11.3.4 Analytical Methodologies 385

11.3.5 Empirical Investigations 387

11.4 Multiagent-Based Design are Operator–Agent Interaction 389

11.4.1 AIA Concept 389

11.4.2 Operator–Agent Interaction Model 391

11.4.3 Difference Between Human–Human Interaction, Human–Machine Interaction, are Operator–Agent Interaction 393

11.4.4 Optimization of Operator–Agent Interaction 396

11.5 A Generic IAI System Architecture are AIA Components 397

11.5.1 Generic IAI System Architecture 397

11.5.2 AIA Structure 402

11.5.3 Adaptation Processes 403

11.6 An IAI are AIA Design: Case Study 405

11.6.1 Interface Design Requirements for the Control of Multiple UAVs 406

11.6.2 Issues 407

11.6.3 How the IAI Design Method Was Used 407

11.6.4 Task Network Modeling are Simulation 409

11.6.5 AIA Implementation 411

11.6.6 Human-in-the-Loop Experimentation 413

11.6.7 AIA Evaluation 413

11.6.8 Discussions are Implications 413

11.7 Conclusions 415

Acknowledgments 417

References 417

12 A Complex Adaptive System of Systems Approach to Human–Automation Interaction In Smart Grid 425
Alireza Fereidunian, Hamid Lesani, Mohammad Ali Zamani, Mohamad Amin Sharifi Kolarijani, Negar Hassanpour, are Sina Sharif Mansouri

12.1 Introduction 425

12.2 Complexity in Systems Science are Engineering 426

12.2.1 the Nature of Complexity 426

12.2.2 Complex Systems 429

12.2.3 Complexity Measures 431

12.2.4 Complexity-Related Terms in Literature 433

12.3 Complex Adaptive Systems 436

12.3.1 What are Complex Adaptive Systems? 436

12.3.2 Characteristics of Complex Adaptive Systems 437

12.4 System of Systems 442

12.4.1 Necessity are Definition 442

12.4.2 Characteristics of System of Systems 444

12.4.3 System of Systems Types 448

12.4.4 A Taxonomy of Systems Family 448

12.5 Complex Adaptive System of Systems 453

12.6 Human–Automation Interaction 454

12.6.1 Automation 454

12.6.2 HAI: Where Humans Interact with Automation 455

12.6.3 HAI are Function Allocation 456

12.6.4 Evolution of HAI Models: Dimensions 457

12.6.5 Evolution of HAI Models: Dynamism 458

12.6.6 Adaptive Autonomy Implementation 460

12.7 HAI in Smart Grid as a Casos 462

12.7.1 Smart Grid 462

12.7.2 HAI in Smart Grid as a CAS 465

12.7.3 HAI in Smart Grid as an SoS 467

12.8 Petri Nets for Complex Systems Modeling 467

12.8.1 Definition 468

12.8.2 Graph Representation of Petri Nets 468

12.8.3 Transition Firing 469

12.8.4 Reachability 470

12.8.5 Incidence Matrix are State Equation 470

12.8.6 Inhibitor Arc 470

12.8.7 IF–THEN Rules by Petri Net 470

12.9 Model-Based Implementation of Adaptive Autonomy 471

12.9.1 the Implementation Framework 471

12.9.2 Case Study: Adaptive Autonomy in Smart Grid 472

12.10 Adaptive Autonomy Realization Using Petri Nets 473

12.10.1 Implementation Methodology 473

12.10.2 Realization of AAHPNES 475

12.10.3 Results are Discussions 482

12.11 Conclusions 483

Acknowledgments 485

References 485

13 Virtual Training For Procedural Skills Development: Case Studies are Lessons Learnt 501
Dawei Jia, Asim Bhatti, are Saeid Nahavandi

13.1 Introduction 501

13.2 Related Work 502

13.2.1 Background 502

13.2.2 Human Side of VT System Efficacy—Issues and Concerns 503

13.3 Present Study 505

13.3.1 Motivation are Aims 505

13.3.2 System Architecture are Human–Machine Interface 506

13.3.3 Measures 508

13.4 Case Study 1 509

13.4.1 Method 509

13.4.2 Results 511

13.4.3 Discussion 515

13.5 Case Study 2 516

13.5.1 Method 516

13.5.2 Results 519

13.5.3 Discussion 524

13.6 Lessons Learnt are Future Work 527

13.6.1 Training Design are Method 527

13.6.2 Measurement Methods 528

13.6.3 Prior Experience with a Force-Reflective Haptic Interface 530

13.6.4 Future Work 531

13.7 Conclusions 531

References 532

14 Computer Supported Collaborative Design: Technologies, Systems, are Applications 537
Weiming Shen, Jean-Paul Barthés, are Junzhou Luo

14.1 Introduction 537

14.2 History of Computer Supported Collaborative Design 538

14.2.1 CSCD 538

14.2.2 CSCD Eve: 1980s 539

14.2.3 CSCD Emergence: 1990s 541

14.2.4 CSCD: Today 542

14.3 Methods, Techniques, are Technologies 542

14.3.1 Communication, Coordination, are Cooperation 542

14.3.2 Negotiation are Conflict Resolution 546

14.3.3 Ontology are Semantic Integration 548

14.3.4 Personal Assistance are Human–Machine Interaction 548

14.3.5 Collaborative Workflows 550

14.3.6 Collaborative Virtual Workspaces are Environments 552

14.3.7 New Representation Schemes for Collaborative Design 552

14.3.8 New Visualization Systems for Collaborative Design 553

14.3.9 Product Data Management are Product Lifecycle Management Systems 553

14.3.10 Security are Privacy 554

14.4 Collaborative Design Systems 555

14.4.1 System Architectures 555

14.4.2 Web-Based/Centralized Systems 557

14.4.3 Agent-Based/Distributed Systems 558

14.4.4 Service-Oriented Systems 558

14.4.5 Collaborative Design Over Supply Chain (Virtual Enterprise) 559

14.5 Applications 560

14.6 Research Challenges are Opportunities 561

14.7 Conclusions 564

References 564

15 Support Collaboration With Roles 575
Haibin Zhu, Mengchu Zhou, are Ming Hou

15.1 Introduction 575

15.2 Benefits of Roles in Collaboration 577

15.2.1 Establishing Trust in Collaboration 577

15.2.2 Establishing Dynamics 578

15.2.3 Facilitating Interaction 580

15.2.4 Support Adaptation 582

15.2.5 Information Sharing 583

15.2.6 Other Benefits 585

15.3 Role-Based Collaboration 585

15.4 E-Cargo Model 590

15.5 A Case Study with RBC are E-Cargo 592

15.6 Conclusions 595

References 595

IV Cloud are Service-Oriented Computing 599

16 Control-Based Approaches to Dynamic Resource Management In Cloud Computing 601
Pengcheng Xiong, Calton Pu, Zhikui Wang, are Gueyoung Jung

16.1 Introduction 601

16.1.1 Public Cloud Computing 602

16.1.2 Dynamic Resource Management: Control-Based Approaches 602

16.2 Experimental Setup are Application Models 603

16.2.1 Test Bed are Control Architecture for a Multi-Tier Application 604

16.2.2 System Models for the Application: Open or Closed 606

16.3 Dynamic Resource Allocation Through Utilization Control 607

16.3.1 Design of Experiments 607

16.3.2 Performance of the Application Under Control 608

16.4 Performance Guarantee Through Dynamic Resource Allocation 612

16.5 Conclusions 614

References 615

17 A Petri Net Solution to Protocol-Level Mismatches In Service Composition 619
Pengcheng Xiong, Mengchu Zhou, Calton Pu, are Yushun Fan

17.1 Introduction 619

17.1.1 Interface Mismatches 621

17.1.2 Protocol-Level Mismatches 622

17.2 Modeling Service Interaction with Petri Nets 624

17.2.1 Basic Petri Nets 624

17.2.2 Model Web Service Interaction with C-Net 627

17.3 Protocol-Level Mismatch Analysis 630

17.3.1 Protocol-Level Mismatch Detection 630

17.3.2 Core Algorithm 632

17.3.3 Comprehensive Solution to Protocol-Level Mismatch 634

17.4 Illustrating Examples 636

17.5 Conclusions 638

References 641

18 Service-Oriented Workflow Systems 645
Wei Tan are Mengchu Zhou

18.1 Introduction 645

18.2 Workflow in SOC: State of the Art 647

18.2.1 Languages for Service Composition 647

18.2.2 Automatic Service Composition 649

18.2.3 Mediation-Aided Service Composition 649

18.2.4 Verification of Service Workflows 650

18.2.5 Decentralized Execution of Workflows 651

18.3 Open Issues 652

18.3.1 Social Network Meets Service Computing 652

18.3.2 More Practical are Flexible Service Composition 652

18.3.3 Workflow as a Service 653

18.3.4 Novel Applications 654

18.4 Conclusions 656

References 657

V Sensing, Networking, are Optimization In Robotics are Manufacturing 661

19 Rehabilitation Robotic Prostheses For Upper Extremity 663
Han-Pang Huang, Yi-Hung Liu, Wei-Chen Lee, Jiun-Yih Kuan, and Tzu-Hao Huang

19.1 Introduction 663

19.2 Rehabilitation Robot Arm are Control 664

19.2.1 Mechanism Design 666

19.2.2 Dynamic Model of an Individual Joint 669

19.2.3 LTR-Observer-Based Individual Joint Dynamic Sliding Mode Control with Gravity Compensation 671

19.2.4 Simulation of the NTU Rehabilitation Robot Arm II 676

19.2.5 Experimental Results for the NTU Rehabilitation Robot Arm II 677

19.3 Rehabilitation Robot Hand 678

19.4 Stability of Neuroprosthesis 683

19.4.1 SVDD-Based Target EMG Pattern Estimation 685

19.4.2 Nontarget EMG Pattern Filtering Scheme 686

19.4.3 Illustrative Example 688

19.5 Conclusions 691

References 692

20 Accelerometer-Based Body Sensor Network (Bsn) For Medical Diagnosis Assessment are Training 699
Ming-Yih Lee, Kin Fong Lei, Wen-Yen Lin, Wann-Yun Shieh, Wen-Wei Tsai, Simon H. Fu, are Chung-Hsien Kuo

20.1 Introduction 699

20.2 Body Sensor Network 700

20.3 Information Retrieved from Accelerometer 702

20.4 Recent Advances in Accelerometer-Based BSN 703

20.4.1 Tilting Angle Identification 703

20.4.2 Muscle Strength Identification 706

20.4.3 Gait Performance Identification 708

20.5 Applications of Accelerometer-Based BSN for Rehabilitation 711

20.5.1 Human Stability Evaluation System 711

20.5.2 Postural Stability Evaluation for Stroke Patients 712

20.5.3 Postural Stability Training for Stroke Patients 713

20.6 BSN Simulation System 715

20.7 Conclusions 718

References 719

21 Telepresence Robots For Medical are Homecare Applications 725
Jun-Ming Lu are Yeh-Liang Hsu

21.1 Introduction 725

21.2 Surgery, Diagnosis, are Consultation 727

21.3 Rehabilitation are Therapy 728

21.4 Monitoring are Assistance 728

21.5 Communication 729

21.6 Key Factors Contributing to the Success of Telepresence Robots 729

21.6.1 Robot Factors of Acceptance 729

21.6.2 Human Factors of Acceptance 731

21.6.3 Summary 732

21.7 Conclusions 732

References 732

22 Advances In Climbing Robots 737
Jizhong Xiao are Hongguang Wang

22.1 Introduction 737

22.2 Technologies for Adhering to Surfaces 738

22.2.1 Magnetic Adhesion 739

22.2.2 Vacuum Suction Techniques 740

22.2.3 Aerodynamic Attraction 744

22.2.4 Grasping Grippers 748

22.2.5 Bio-Mimetic Approaches Inspired by Climbing Animals 749

22.2.6 Emerging Technologies for Climbing Robots 753

22.3 Locomotion Techniques of Climbing Robots 755

22.4 Conclusions 759

Acknowledgment 760

References 760

23 Data Processing In Current 3D Robotic Perception Systems 767
Cang YE

23.1 Introduction 767

23.1.1 Stereovision 767

23.1.2 LIDAR 769

23.1.3 Flash LIDAR Camera (FLC) 770

23.2 An LIDAR-Based Terrain Mapping are Navigation System 771

23.2.1 Overview of the Mapping are Navigation System 772

23.2.2 Terrain Mapping 773

23.2.3 Terrain Traversability Analysis 776

23.2.4 PTI Histogram for Path Planning 777

23.2.5 Experimental Results 779

23.3 FLC-Based Systems 781

23.3.1 VR-Odometry 782

23.3.2 Three-Dimensional Data Segmentation 787

23.4 Conclusions 791

Acknowledgments 792

References 792

24 Hybrid/Electric Vehicle Battery Manufacturing: The State-Of-The-Art 795
Claudia P. Arenas Guerrero, Feng Ju, Jingshan Li, Guoxian Xiao, and Stephan Biller

24.1 Introduction 795

24.2 Vehicle Battery Requirements 796

24.3 Hybrid, Plug-In Hybrid, are Electric Vehicle 797

24.3.1 Hybrid Electric Vehicle 797

24.3.2 Plug-In Hybrid Electric Vehicle 797

24.3.3 Electric Vehicle 798

24.4 Battery Technology Development 798

24.5 Nickel-Metal Hydride Battery 799

24.5.1 NiMH Battery Manufacturing 800

24.5.2 NiMH Batteries in Commercial Vehicles 800

24.5.3 Cost 801

24.5.4 Recycling 801

24.6 Lithium-Ion (Li-Ion) Battery 802

24.6.1 Lithium Technology 802

24.6.2 Manufacturing Processes 803

24.6.3 Li-Ion Batteries in Commercial Vehicles 807

24.6.4 Safety 808

24.6.5 Cost 809

24.6.6 Environmental Issues 809

24.6.7 Recycling 809

24.7 Challenges 810

24.8 Conclusions 812

References 812

25 Recent Advances are Issues In Facility Location Problems 817
Feng Chu, Zhanguo Zhu, are Saïıd Mammar

25.1 Introduction 817

25.2 A Capacitated Plant Location Problem with Multicommodity Flow 819

25.2.1 Problem Description 819

25.2.2 Problem Formulation 819

25.3 A Multitype Transshipment Point Location Problem with Multicommodity Flow 821

25.3.1 Problem Description 821

25.3.2 Problem Formulation 822

25.4 A Large Scale New Variant of Capacitated Clustering Problem 824

25.4.1 Problem Description 824

25.4.2 Problem Formulation 825

25.5 A Location Problem with Selective Matching are Vehicles Assignment 826

25.5.1 Problem Description 826

25.5.2 Problem Formulation 826

25.6 Competitive Facility Location are Design with Reactions of Competitors Already in the Market 828

25.6.1 Problem Description 829

25.6.2 Problem Formulation 829

25.7 Conclusions are Future Research Directions 831

References 832

Index 835

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