DECISION SUPPORT SYSTEMS AND INTELLIGENT SYSTEMS SIXTH EDITIONPDF电子书下载

PART Ⅰ: DECISION MAKING AND COMPUTERIZED SUPPORT 1

CHAPTER 1 Management Support Systems: An Overview 2

1.1 Opening Vignette: Decision Support at Roadway Package System 3

1.2 Managers and Decision Making 4

1.3 Managerial Decision Making and Information Systems 6

1.4 Managers and Computerized Support 8

1.5 The Need for Computerized Decision Support and the Supporting Technologies 9

1.6 A Framework for Decision Support 11

1.7 The Concept of Decision Support Systems 13

1.8 Group Support Systems 15

1.9 Executive Information （Support） Systems 16

1.10 Expert Systems and Intelligent Agents 17

1.11 Artificial Neural Networks 18

1.12 Knowledge Management Systems 19

1.13 Supporting Enterprise Resources Planning and Supply Chain Management 19

1.14 Hybrid Support Systems 20

1.15 The Evolution and Attributes of Computerized Decision Aids 21

1.16 Plan of This Book 24

Case Application 1.1 Manufacturing and Marketing of Machine Devices 29

CHAPTER 2 Decision Making, Systems, Modeling, and Support 30

2.1 Opening Vignette: How to Invest $10 Million 30

2.2 Decision Making: Introduction and Definitions 32

2.3 Systems 34

2.4 Models 38

2.5 A Preview of the Modeling Process 39

2.6 Decision Making: The Intelligence Phase 42

2.7 Decision Making: The Design Phase 45

2.8 Decision Making: The Choice Phase 57

2.9 Evaluation: Multiple Goals, Sensitivity Analysis, What-If, and Goal Seeking 60

2.10 Decision Making: The Implementation Phase 67

2.11 How Decisions Are Supported 68

2.12 Alternative Decision-Making Models 70

2.13 Personality Types, Gender, Human Cognition, and DecisionStyles 73

2.14 The Decision Makers 77

Case Application 2.1 Clay Process Planning at IMERYS: A ClassicalCase of Decision Making—Part 1 85

Case Application 2.2 Clay Process Planning at IMERYS: A ClassicalCase of Decision Making—Part 2 86

Case Application 2.3 Key Grip Uses the Analytical Hierarchy ProcessApproach to Select Film Projects 89

PART Ⅱ: DECISION SUPPORT SYSTEMS 93

CHAPTER 3 Decision Support Systems: An Overview 94

3.1 Opening Vignette: Evaluating the Quality of Journals inHong Kong 94

3.2 DSS Configurations 96

3.3 What Is a DSS? 96

3.4 Characteristics and Capabilities of DSS 98

3.5 Components of DSS 100

3.6 The Data Management Subsystem 101

3.7 The Model Management Subsystem 104

3.8 The Knowledge-Based Management Subsystem 107

3.9 The User Interface （Dialog） Subsystem 107

3.10 The User 109

3.11 DSS Hardware 110

3.12 Distinguishing DSS from Management Science and MIS 110

3.13 DSS Classifications 113

3.14 The Big Picture 120

Case Application 3.1 Decision Support for Military HousingManagers 125

CHAPTER 4 Data Warehousing, Access, Analysis, Mining, andVisualization 128

4.1 Opening Vignette: OBI Makes the Best Out of the DataWarehouse 128

4.2 Data Warehousing, Access, Analysis, and Visualization 130

4.3 The Nature and Sources of Data 131

4.4 Data Collection, Problems, and Quality 132

4.5 The Internet and Commercial Database Services 134

4.6 Database Management Systems in DSS 136

4.7 Database Organization and Structures 136

4.8 Data Warehousing 141

4.9 OLAP: Data Access, Querying, and Analysis 146

4.10 Data Mining 148

4.11 Data Visualization and Multidimensionality 152

4.12 Geographic Information Systems and Virtual Reality 154

4.13 Business Intelligence and the Web 158

4.14 The Big Picture 159

CHAPTER 5 Modeling and Analysis 165

5.1 Opening Vignette: DuPont Simulates Rail Transportation System and Avoids Costly Capital Expense 166

5.2 Modeling for MSS 167

5.3 Static and Dynamic Models 170

5.4 Treating Certainty, Uncertainty, and Risk 171

5.5 Influence Diagrams 172

5.6 MSS Modeling in Spreadsheets 176

5.7 Decision Analysis of a Few Alternatives （Decision Tables and Decision Trees） 178

5.8 Optimization via Mathematical Programming 182

5.9 Heuristic Programming 186

5.10 Simulation 189

5.11 Multidimensional Modeling —OLAP 192

5.12 Visual Interactive Modeling and Visual Interactive Simulation 198

5.13 Quantitative Software Packages—OLAP 201

5.14 Model Base Management 203

Case Application 5.1 Procter & Gamble （P&G） Blends Models, Judgment, and GIS to Restructure the Supply Chain 214

Case Application 5.2 Scott Homes Constructs an Expert Choice Multicriteria Model-Based DSS for Selecting a Mobile Home Supplier 217

Case Application 5.3 Clay Process Planning at IMERYS: A Classical Case of Decision Making 221

CHAPTER 6 Decision Support System Development 224

6.1 Opening Vignette: Osram Sylvania Thinks Small, Strategizes Big—Develops the InfoNet HR Portal System 224

6.2 Introduction to DSS Development 227

6.3 The Traditional System Development Life Cycle 229

6.4 Alternate Development Methodologies 235

6.5 Prototyping: The DSS Development Methodology 237

6.6 DSS Technology Levels and Tools 240

6.7 DSS Development Platforms 241

6.8 DSS Development Tool Selection 243

6.9 Team-Developed DSS 244

6.10 End User-Developed DSS 245

6.11 Developing DSS: Putting the System Together 248

6.12 DSS Research Directions and the DSS of the Future 249

Case Application 6.1 Clay Process at IMERYS: A Classical Case of Decision Making 254

PART Ⅲ: COLLABORATION, COMMUNICATION, ENTERPRISEDECISION SUPPORT SYSTEMS, AND KNOWLEDGEMANAGEMENT 259

CHAPTER 7 Collaborative Computing Technologies: Group SupportSystems 260

7.1 Opening Vignette: Chrysler SCORES with Groupware 261

7.2 Group Decision Making, Communication, and Collaboration 263

7.3 Communication Support 264

7.4 Collaboration Support: Computer-Supported CooperativeWork 266

7.5 Group Support Systems 271

7.6 Group Support Systems Technologies 275

7.7 GroupSystems 276

7.8 The GSS Meeting Process 278

7.9 Distance Learning 280

7.10 Creativity and Idea Generation 287

7.11 GSS and Collaborative Computing Issues and Research 292

Case Application 7.1 WELCOM Way to Share Ideas in a WorldForum 301

Case Application 7.2 Pfizer’s Effective and Safe CollaborativeComputing Pill 302

CHAPTER 8 Enterprise Decision Support Systems 304

8.1 Opening Vignette: Pizzeria Uno’s Enterprise System Makes theDifference 305

8.2 Enterprise Systems: Concepts and Definitions 306

8.3 The Evolution of Executive and Enterprise Information Systems 306

8.4 Executives’ Roles and Their Information Needs 309

8.5 Characteristics and Capabilities of Executive Support Systems 310

8.6 Comparing and Integrating EIS and DSS 314

8.7 EIS, Data Access, Data Warehousing, OLAP, MultidimensionalAnalysis, Presentation, and the Web 317

8.8 Including Soft Information in Enterprise Systems 320

8.9 Organizational DSS 321

8.10 Supply and Value Chains and Decision Support 322

8.11 Supply Chain Problems and Solutions 327

8.12 Computerized Systems: MRP, ERP and SCM 330

8.13 Frontline Decision Support Systems 335

8.14 The Future of Executives and Enterprise Support Systems 337

CHAPTER 9 Knowledge Management 344

9.1 Opening Vignette: Knowledge Management Gives Mitre a SharperEdge 344

9.2 Introduction to Knowledge Management 346

9.3 Knowledge 349

9.4 Organizational Learning and Organizational Memory 352

9.5 Knowledge Management 356

9.6 The Chief Knowledge Officer 365

9.7 Knowledge Management Development 366

9.8 Knowledge Management Methods, Technologies, and Tools 370

9.9 Knowledge Management Success 375

9.10 Knowledge Management and Artificial Intelligence 381

9.11 Electronic Document Management 382

9.12 Knowledge Management Issues and the Future 383

Case Application 9.1 Chrysler’s New Know-Mobiles 390

Case Application 9.2 Knowledge the Chevron Way 392

PART Ⅳ: FUNDAMENTALS OF INTELLIGENT SYSTEMS 395

CHAPTER 10 Knowledge-Based Decision Support: Artificial Intelligenceand Expert Systems 396

10.1 Opening Vignette: A Knowledge-Based DSS in a Chinese ChemicalPlant 397

10.2 Concepts and Definitions of Artificial Intelligence 398

10.3 Artificial Intelligence Versus Natural Intelligence 401

10.4 The Artificial Intelligence Field 402

10.5 Types of Knowledge-Based Decision Support Systems 406

10.6 Basic Concepts of Expert Systems 407

10.7 Structure of Expert Systems 410

10.8 The Human Element in Expert Systems 413

10.9 How Expert Systems Work 414

10.10 Example of an Expert System Consultation 415

10.11 Problem Areas Addressed by Expert Systems 417

10.12 Benefits of Expert Systems 420

10.13 Problems and Limitations of Expert Systems 423

10.14 Expert System Success Factors 424

10.15 Types of Expert Systems 425

10.16 Expert Systems and the Internet/Intranets/Web 428

Case Application 10.1 Gate Assignment Display System 436

CHAPTER 11 Knowledge Acquisition and Validation 437

11.1 Opening Vignette: American Express Improves Approval Selection with Machine Learning 438

11.2 Knowledge Engineering 438

11.3 Scope of Knowledge 441

11.4 Difficulties in Knowledge Acquisition 444

11.5 Methods of Knowledge Acquisition: An Overview 447

11.6 Interviews 449

11.7 Tracking Methods 451

11.8 Observations and Other Manual Methods 453

11.9 Expert-Driven Methods 454

11.10 Repertory Grid Analysis 456

11.11 Supporting the Knowledge Engineer 458

11.12 Machine Learning: Rule Induction, Case-Based Reasoning, Neural Computing, and Intelligent Agents 461

11.13 Selecting an Appropriate Knowledge Acquisition Method 467

11.14 Knowledge Acquisition from Multiple Experts 468

11.15 Validation and Verification of the Knowledge Base 470

11.16 Analyzing, Coding, Documenting, and Diagramming 472

11.17 Numeric and Documented Knowledge Acquisition 473

11.18 Knowledge Acquisition and the Internet/Intranets 474

11.19 Induction Table Example 476

CHAPTER 12 Knowledge Representation 484

12.1 Opening Vignette: An Intelligent System Manages Ford’s Assembly Plants 484

12.2 Introduction 485

12.3 Representation in Logic and Other Schemas 485

12.4 Semantic Networks 490

12.5 Production Rules 491

12.6 Frames 494

12.7 Multiple Knowledge Representation 499

12.8 Experimental Knowledge Representations 501

12.9 Representing Uncertainty: An Overview 503

CHAPTER 13 Inference Techniques 509

13.1 Opening Vignette: Konica Automates a Help Desk with Case-Based Reasoning 509

13.2 Reasoning in Artificial Intelligence 510

13.3 Inferencing with Rules: Forward and Backward Chaining 512

13.4 The Inference Tree 517

13.5 Inferencing with Frames 519

13.6 Model-Based Reasoning 520

13.7 Case-Based Reasoning 522

13.8 Explanation and Metaknowledge 530

13.9 Inferencing with Uncertainty 534

13.10 Representing Uncertainty 535

13.11 Probabilities and Related Approaches 537

13.12 Theory of Certainty （Certainty Factors） 538

13.13 Approximate Reasoning Using Fuzzy Logic 541

Case Application 13.1 Compaq QuickSource: Using Case-Based Reasoning for Problem Determination 548

CHAPTER 14 Intelligent Systems Development 550

14.1 Opening Vignette: Development of an Expert System to Detect Insider Stock Trades 550

14.2 Prototyping: The Expert System Development Life Cycle 552

14.3 Phase Ⅰ: Project Initialization 555

14.4 Phase Ⅱ: System Analysis and Design 564

14.5 Software Classification: ES Technology Levels 567

14.6 Building Expert Systems with Tools 571

14.7 Shells and Environments 571

14.8 Software Selection 573

14.9 Hardware 576

14.10 Phase Ⅲ: Rapid Prototyping and a DemonstrationPrototype 576

14.11 Phase Ⅳ: System Development 578

14.12 Phase Ⅴ:Implementation 583

14.13 Phase Ⅵ: Postimplementation 585

14.14 The Future of Expert System Development Processes 589

Appendix 14-A Developing a Small （Rule-Based） Expert System for Wine Selection 597

Case Application 14.1 The Development of the Logistics Management System （LMS） at IBM 598

PART Ⅴ: ADVANCED INTELLIGENT SYSTEMS 601

CHAPTER 15 Neural Computing: The Basics 602

15.1 Opening Vignette: Household Financial’s Vision Speeds Loan Approvals with Neural Networks 603

15.2 Machine Learning 605

15.3 Neural Computing 606

15.4 The Biology Analogy 607

15.5 Neural Network Fundamentals 609

15.6 Neural Network Application Development 614

15.7 Data Collection and Preparation 616

15.8 Neural Network Architecture 616

15.9 Neural Network Preparation 619

15.10 Training the Network 619

15.11 Learning Algorithms 620

15.12 Backpropagation 622

15.13 Testing 623

15.14 Implementation 624

15.15 Neural Network Software 625

15.16 Neural Network Hardware 626

15.17 Neural Network Development Examples 627

15.18 The Self-Organizing Map: An Alternative Neural Network Architecture 632

15.19 Benefits of Neural Networks 634

15.20 Limitations of Neural Networks 636

15.21 Neural Networks and Expert Systems 636

15.22 Neural Networks for Decision Support 638

Case Application 15.1 Maximizing the Value of the John Deere & Company Pension Fund 646

CHAPTER 16 Neural Computing Applications, and Advanced Artifiicial Intelligent Systems and Applications 648

16.1 Opening Vignette: New York City’s Public Housing Authority Gets Warm and Fuzzy 649

16.2 Overview of ANN Application Areas 650

16.3 Credit Approval with Neural Networks 651

16.4 Bankruptcy Prediction with Neural Networks 656

16.5 Stock Market Prediction System with Modular Neural Networks 658

16.6 Integrated ANNs and Expert Systems 661

16.7 Genetic Algorithms 664

16.8 Optimization Algorithms 671

16.9 Fuzzy Logic 672

16.10 Qualitative Reasoning 676

16.11 Intelligent Systems Integration 678

16.12 Data Mining and Knowledge Discovery in Databases 681

CHAPTER 17 Intelligent Software Agents and Creativity 688

17.1 Opening Vignettes: Examples of Intelligent Agents 688

17.2 Intelligent Agents: An Overview 690

17.3 Characteristics of Agents 692

17.4 Single Task 693

17.5 Why Intelligent Agents? 694

17.6 Classification and Types of Agents 696

17.7 Internet-Based Software Agents 699

17.8 Electronic Commerce Agents 703

17.9 Other Agents, Including Data Mining, User Interface, and Interactive, Believable Agents 708

17.10 Distributed AI, Multiagents, and Communities of Agents 714

17.11 DSS Agents 719

17.12 Managerial Issues 721

PART Ⅵ: IMPLEMENTATION, INTEGRATION, AND IMPACTS 727

CHAPTER 18 Implementing and Integrating Management SupportSystems 728

18.1 Opening Vignette: INCA Expert Systems for the SWIFTNetwork 728

18.2 Implementation: An Overview 730

18.3 The Major Issues of Implementation 733

18.4 Implementation Strategies 741

18.5 What Is System Integration and Why Integrate? 744

18.6 Generic Models of MSS Integration 746

18.7 Models of ES and DSS Integration 748

18.8 Integrating EIS, DSS, and ES, and Global Integration 751

18.9 Intelligent DSS 755

18.10 Intelligent Modeling and Model Management 757

18.11 Examples of Integrated Systems 760

18.12 Problems and Issues in Integration 768

Case Application 18.1 Urban Traffic Management 774

CHAPTER 19 Impacts of Management Support Systems 776

19.1 Opening Vignette: Police Department Uses Neural Networks to Assess Employees 776

19.2 Introduction 777

19.3 Overview of Impacts 778

19.4 Organizational Structure and Related Areas 780

19.5 MSS Support to Business Process Reengineering 782

19.6 Personnel Management Issues 786

19.7 Impact on Individuals 787

19.8 Impacts on Productivity, Quality, and Competitiveness 788

19.9 Decision Making and the Manager’s Job 789

19.10 Issues of Legality, Privacy, and Ethics 790

19.11 Intelligent Systems and Employment Levels 793

19.12 Internet Communities 795

19.13 Other Societal Impacts 796

19.14 Managerial Implications and Social Responsibilities 798

19.15 The Future of Management Support Systems 799

Case Application 19.1 Xerox Reengineers Its $3 Billion Purchasing Process with Graphical Modeling and Simulation 806

Glossary 807

References 821

Index 851