DECISION SUPPORT SYSTEMS AND INTELLIGENT SYSTEMS SIXTH EDITION【2025|PDF下载-Epub版本|mobi电子书|kindle百度云盘下载】

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PART Ⅰ: DECISION MAKING AND COMPUTERIZED SUPPORT1

CHAPTER 1 Management Support Systems: An Overview2

1.1 Opening Vignette: Decision Support at Roadway Package System3

1.2 Managers and Decision Making4

1.3 Managerial Decision Making and Information Systems6

1.4 Managers and Computerized Support8

1.5 The Need for Computerized Decision Support and the Supporting Technologies9

1.6 A Framework for Decision Support11

1.7 The Concept of Decision Support Systems13

1.8 Group Support Systems15

1.9 Executive Information （Support） Systems16

1.10 Expert Systems and Intelligent Agents17

1.11 Artificial Neural Networks18

1.12 Knowledge Management Systems19

1.13 Supporting Enterprise Resources Planning and Supply Chain Management19

1.14 Hybrid Support Systems20

1.15 The Evolution and Attributes of Computerized Decision Aids21

1.16 Plan of This Book24

Case Application 1.1 Manufacturing and Marketing of Machine Devices29

CHAPTER 2 Decision Making, Systems, Modeling, and Support30

2.1 Opening Vignette: How to Invest $10 Million30

2.2 Decision Making: Introduction and Definitions32

2.3 Systems34

2.4 Models38

2.5 A Preview of the Modeling Process39

2.6 Decision Making: The Intelligence Phase42

2.7 Decision Making: The Design Phase45

2.8 Decision Making: The Choice Phase57

2.9 Evaluation: Multiple Goals, Sensitivity Analysis, What-If, and Goal Seeking60

2.10 Decision Making: The Implementation Phase67

2.11 How Decisions Are Supported68

2.12 Alternative Decision-Making Models70

2.13 Personality Types, Gender, Human Cognition, and DecisionStyles73

2.14 The Decision Makers77

Case Application 2.1 Clay Process Planning at IMERYS: A ClassicalCase of Decision Making—Part 185

Case Application 2.2 Clay Process Planning at IMERYS: A ClassicalCase of Decision Making—Part 286

Case Application 2.3 Key Grip Uses the Analytical Hierarchy ProcessApproach to Select Film Projects89

PART Ⅱ: DECISION SUPPORT SYSTEMS93

CHAPTER 3 Decision Support Systems: An Overview94

3.1 Opening Vignette: Evaluating the Quality of Journals inHong Kong94

3.2 DSS Configurations96

3.3 What Is a DSS?96

3.4 Characteristics and Capabilities of DSS98

3.5 Components of DSS100

3.6 The Data Management Subsystem101

3.7 The Model Management Subsystem104

3.8 The Knowledge-Based Management Subsystem107

3.9 The User Interface （Dialog） Subsystem107

3.10 The User109

3.11 DSS Hardware110

3.12 Distinguishing DSS from Management Science and MIS110

3.13 DSS Classifications113

3.14 The Big Picture120

Case Application 3.1 Decision Support for Military HousingManagers125

CHAPTER 4 Data Warehousing, Access, Analysis, Mining, andVisualization128

4.1 Opening Vignette: OBI Makes the Best Out of the DataWarehouse128

4.2 Data Warehousing, Access, Analysis, and Visualization130

4.3 The Nature and Sources of Data131

4.4 Data Collection, Problems, and Quality132

4.5 The Internet and Commercial Database Services134

4.6 Database Management Systems in DSS136

4.7 Database Organization and Structures136

4.8 Data Warehousing141

4.9 OLAP: Data Access, Querying, and Analysis146

4.10 Data Mining148

4.11 Data Visualization and Multidimensionality152

4.12 Geographic Information Systems and Virtual Reality154

4.13 Business Intelligence and the Web158

4.14 The Big Picture159

CHAPTER 5 Modeling and Analysis165

5.1 Opening Vignette: DuPont Simulates Rail Transportation System and Avoids Costly Capital Expense166

5.2 Modeling for MSS167

5.3 Static and Dynamic Models170

5.4 Treating Certainty, Uncertainty, and Risk171

5.5 Influence Diagrams172

5.6 MSS Modeling in Spreadsheets176

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

5.8 Optimization via Mathematical Programming182

5.9 Heuristic Programming186

5.10 Simulation189

5.11 Multidimensional Modeling —OLAP192

5.12 Visual Interactive Modeling and Visual Interactive Simulation198

5.13 Quantitative Software Packages—OLAP201

5.14 Model Base Management203

Case Application 5.1 Procter & Gamble （P&G） Blends Models, Judgment, and GIS to Restructure the Supply Chain214

Case Application 5.2 Scott Homes Constructs an Expert Choice Multicriteria Model-Based DSS for Selecting a Mobile Home Supplier217

Case Application 5.3 Clay Process Planning at IMERYS: A Classical Case of Decision Making221

CHAPTER 6 Decision Support System Development224

6.1 Opening Vignette: Osram Sylvania Thinks Small, Strategizes Big—Develops the InfoNet HR Portal System224

6.2 Introduction to DSS Development227

6.3 The Traditional System Development Life Cycle229

6.4 Alternate Development Methodologies235

6.5 Prototyping: The DSS Development Methodology237

6.6 DSS Technology Levels and Tools240

6.7 DSS Development Platforms241

6.8 DSS Development Tool Selection243

6.9 Team-Developed DSS244

6.10 End User-Developed DSS245

6.11 Developing DSS: Putting the System Together248

6.12 DSS Research Directions and the DSS of the Future249

Case Application 6.1 Clay Process at IMERYS: A Classical Case of Decision Making254

PART Ⅲ: COLLABORATION, COMMUNICATION, ENTERPRISEDECISION SUPPORT SYSTEMS, AND KNOWLEDGEMANAGEMENT259

CHAPTER 7 Collaborative Computing Technologies: Group SupportSystems260

7.1 Opening Vignette: Chrysler SCORES with Groupware261

7.2 Group Decision Making, Communication, and Collaboration263

7.3 Communication Support264

7.4 Collaboration Support: Computer-Supported CooperativeWork266

7.5 Group Support Systems271

7.6 Group Support Systems Technologies275

7.7 GroupSystems276

7.8 The GSS Meeting Process278

7.9 Distance Learning280

7.10 Creativity and Idea Generation287

7.11 GSS and Collaborative Computing Issues and Research292

Case Application 7.1 WELCOM Way to Share Ideas in a WorldForum301

Case Application 7.2 Pfizer’s Effective and Safe CollaborativeComputing Pill302

CHAPTER 8 Enterprise Decision Support Systems304

8.1 Opening Vignette: Pizzeria Uno’s Enterprise System Makes theDifference305

8.2 Enterprise Systems: Concepts and Definitions306

8.3 The Evolution of Executive and Enterprise Information Systems306

8.4 Executives’ Roles and Their Information Needs309

8.5 Characteristics and Capabilities of Executive Support Systems310

8.6 Comparing and Integrating EIS and DSS314

8.7 EIS, Data Access, Data Warehousing, OLAP, MultidimensionalAnalysis, Presentation, and the Web317

8.8 Including Soft Information in Enterprise Systems320

8.9 Organizational DSS321

8.10 Supply and Value Chains and Decision Support322

8.11 Supply Chain Problems and Solutions327

8.12 Computerized Systems: MRP, ERP and SCM330

8.13 Frontline Decision Support Systems335

8.14 The Future of Executives and Enterprise Support Systems337

CHAPTER 9 Knowledge Management344

9.1 Opening Vignette: Knowledge Management Gives Mitre a SharperEdge344

9.2 Introduction to Knowledge Management346

9.3 Knowledge349

9.4 Organizational Learning and Organizational Memory352

9.5 Knowledge Management356

9.6 The Chief Knowledge Officer365

9.7 Knowledge Management Development366

9.8 Knowledge Management Methods, Technologies, and Tools370

9.9 Knowledge Management Success375

9.10 Knowledge Management and Artificial Intelligence381

9.11 Electronic Document Management382

9.12 Knowledge Management Issues and the Future383

Case Application 9.1 Chrysler’s New Know-Mobiles390

Case Application 9.2 Knowledge the Chevron Way392

PART Ⅳ: FUNDAMENTALS OF INTELLIGENT SYSTEMS395

CHAPTER 10 Knowledge-Based Decision Support: Artificial Intelligenceand Expert Systems396

10.1 Opening Vignette: A Knowledge-Based DSS in a Chinese ChemicalPlant397

10.2 Concepts and Definitions of Artificial Intelligence398

10.3 Artificial Intelligence Versus Natural Intelligence401

10.4 The Artificial Intelligence Field402

10.5 Types of Knowledge-Based Decision Support Systems406

10.6 Basic Concepts of Expert Systems407

10.7 Structure of Expert Systems410

10.8 The Human Element in Expert Systems413

10.9 How Expert Systems Work414

10.10 Example of an Expert System Consultation415

10.11 Problem Areas Addressed by Expert Systems417

10.12 Benefits of Expert Systems420

10.13 Problems and Limitations of Expert Systems423

10.14 Expert System Success Factors424

10.15 Types of Expert Systems425

10.16 Expert Systems and the Internet/Intranets/Web428

Case Application 10.1 Gate Assignment Display System436

CHAPTER 11 Knowledge Acquisition and Validation437

11.1 Opening Vignette: American Express Improves Approval Selection with Machine Learning438

11.2 Knowledge Engineering438

11.3 Scope of Knowledge441

11.4 Difficulties in Knowledge Acquisition444

11.5 Methods of Knowledge Acquisition: An Overview447

11.6 Interviews449

11.7 Tracking Methods451

11.8 Observations and Other Manual Methods453

11.9 Expert-Driven Methods454

11.10 Repertory Grid Analysis456

11.11 Supporting the Knowledge Engineer458

11.12 Machine Learning: Rule Induction, Case-Based Reasoning, Neural Computing, and Intelligent Agents461

11.13 Selecting an Appropriate Knowledge Acquisition Method467

11.14 Knowledge Acquisition from Multiple Experts468

11.15 Validation and Verification of the Knowledge Base470

11.16 Analyzing, Coding, Documenting, and Diagramming472

11.17 Numeric and Documented Knowledge Acquisition473

11.18 Knowledge Acquisition and the Internet/Intranets474

11.19 Induction Table Example476

CHAPTER 12 Knowledge Representation484

12.1 Opening Vignette: An Intelligent System Manages Ford’s Assembly Plants484

12.2 Introduction485

12.3 Representation in Logic and Other Schemas485

12.4 Semantic Networks490

12.5 Production Rules491

12.6 Frames494

12.7 Multiple Knowledge Representation499

12.8 Experimental Knowledge Representations501

12.9 Representing Uncertainty: An Overview503

CHAPTER 13 Inference Techniques509

13.1 Opening Vignette: Konica Automates a Help Desk with Case-Based Reasoning509

13.2 Reasoning in Artificial Intelligence510

13.3 Inferencing with Rules: Forward and Backward Chaining512

13.4 The Inference Tree517

13.5 Inferencing with Frames519

13.6 Model-Based Reasoning520

13.7 Case-Based Reasoning522

13.8 Explanation and Metaknowledge530

13.9 Inferencing with Uncertainty534

13.10 Representing Uncertainty535

13.11 Probabilities and Related Approaches537

13.12 Theory of Certainty （Certainty Factors）538

13.13 Approximate Reasoning Using Fuzzy Logic541

Case Application 13.1 Compaq QuickSource: Using Case-Based Reasoning for Problem Determination548

CHAPTER 14 Intelligent Systems Development550

14.1 Opening Vignette: Development of an Expert System to Detect Insider Stock Trades550

14.2 Prototyping: The Expert System Development Life Cycle552

14.3 Phase Ⅰ: Project Initialization555

14.4 Phase Ⅱ: System Analysis and Design564

14.5 Software Classification: ES Technology Levels567

14.6 Building Expert Systems with Tools571

14.7 Shells and Environments571

14.8 Software Selection573

14.9 Hardware576

14.10 Phase Ⅲ: Rapid Prototyping and a DemonstrationPrototype576

14.11 Phase Ⅳ: System Development578

14.12 Phase Ⅴ:Implementation583

14.13 Phase Ⅵ: Postimplementation585

14.14 The Future of Expert System Development Processes589

Appendix 14-A Developing a Small （Rule-Based） Expert System for Wine Selection597

Case Application 14.1 The Development of the Logistics Management System （LMS） at IBM598

PART Ⅴ: ADVANCED INTELLIGENT SYSTEMS601

CHAPTER 15 Neural Computing: The Basics602

15.1 Opening Vignette: Household Financial’s Vision Speeds Loan Approvals with Neural Networks603

15.2 Machine Learning605

15.3 Neural Computing606

15.4 The Biology Analogy607

15.5 Neural Network Fundamentals609

15.6 Neural Network Application Development614

15.7 Data Collection and Preparation616

15.8 Neural Network Architecture616

15.9 Neural Network Preparation619

15.10 Training the Network619

15.11 Learning Algorithms620

15.12 Backpropagation622

15.13 Testing623

15.14 Implementation624

15.15 Neural Network Software625

15.16 Neural Network Hardware626

15.17 Neural Network Development Examples627

15.18 The Self-Organizing Map: An Alternative Neural Network Architecture632

15.19 Benefits of Neural Networks634

15.20 Limitations of Neural Networks636

15.21 Neural Networks and Expert Systems636

15.22 Neural Networks for Decision Support638

Case Application 15.1 Maximizing the Value of the John Deere & Company Pension Fund646

CHAPTER 16 Neural Computing Applications, and Advanced Artifiicial Intelligent Systems and Applications648

16.1 Opening Vignette: New York City’s Public Housing Authority Gets Warm and Fuzzy649

16.2 Overview of ANN Application Areas650

16.3 Credit Approval with Neural Networks651

16.4 Bankruptcy Prediction with Neural Networks656

16.5 Stock Market Prediction System with Modular Neural Networks658

16.6 Integrated ANNs and Expert Systems661

16.7 Genetic Algorithms664

16.8 Optimization Algorithms671

16.9 Fuzzy Logic672

16.10 Qualitative Reasoning676

16.11 Intelligent Systems Integration678

16.12 Data Mining and Knowledge Discovery in Databases681

CHAPTER 17 Intelligent Software Agents and Creativity688

17.1 Opening Vignettes: Examples of Intelligent Agents688

17.2 Intelligent Agents: An Overview690

17.3 Characteristics of Agents692

17.4 Single Task693

17.5 Why Intelligent Agents?694

17.6 Classification and Types of Agents696

17.7 Internet-Based Software Agents699

17.8 Electronic Commerce Agents703

17.9 Other Agents, Including Data Mining, User Interface, and Interactive, Believable Agents708

17.10 Distributed AI, Multiagents, and Communities of Agents714

17.11 DSS Agents719

17.12 Managerial Issues721

PART Ⅵ: IMPLEMENTATION, INTEGRATION, AND IMPACTS727

CHAPTER 18 Implementing and Integrating Management SupportSystems728

18.1 Opening Vignette: INCA Expert Systems for the SWIFTNetwork728

18.2 Implementation: An Overview730

18.3 The Major Issues of Implementation733

18.4 Implementation Strategies741

18.5 What Is System Integration and Why Integrate?744

18.6 Generic Models of MSS Integration746

18.7 Models of ES and DSS Integration748

18.8 Integrating EIS, DSS, and ES, and Global Integration751

18.9 Intelligent DSS755

18.10 Intelligent Modeling and Model Management757

18.11 Examples of Integrated Systems760

18.12 Problems and Issues in Integration768

Case Application 18.1 Urban Traffic Management774

CHAPTER 19 Impacts of Management Support Systems776

19.1 Opening Vignette: Police Department Uses Neural Networks to Assess Employees776

19.2 Introduction777

19.3 Overview of Impacts778

19.4 Organizational Structure and Related Areas780

19.5 MSS Support to Business Process Reengineering782

19.6 Personnel Management Issues786

19.7 Impact on Individuals787

19.8 Impacts on Productivity, Quality, and Competitiveness788

19.9 Decision Making and the Manager’s Job789

19.10 Issues of Legality, Privacy, and Ethics790

19.11 Intelligent Systems and Employment Levels793

19.12 Internet Communities795

19.13 Other Societal Impacts796

19.14 Managerial Implications and Social Responsibilities798

19.15 The Future of Management Support Systems799

Case Application 19.1 Xerox Reengineers Its $3 Billion Purchasing Process with Graphical Modeling and Simulation806

Glossary807

References821

Index851