《管理科学导论 英文版·第8版》求取 ⇩

ContentsCHAPTER ONE Introduction1

F Answers to Even-Numbered Problems F-1

E References and Bibliography E-1

D Matnx Notation and Operations D-1

C Values of e-λC-1

B Random Digits B-1

Appendixes A-1

G Solutions to Self-Test Problems G-1

A Areas for the Standard Normal Distribution A-2

1.1 Problem Sotving and Decision Making2

1.2 Quantitative Analvsis and Decision Making3

1.3 Guantitative Analysis5

Model Development6

Data Preparation9

Model Solution10

Report Generation11

A Note Regarding Implementation11

Revenue and Volume Models12

Cost and Volume Models12

1.4 Models of Cost,Revenue,and Profit12

Profit and Volume Models13

Break-Even Analysis13

1.5 Management Science in Practice14

Management Science Techniques14

Methods Used Most Frequently15

Glossary17

Summary17

Problems18

Appendix 1.1 Spreadsheets for Management Science21

Appendix 1.2 The Management Scientist Software Package22

Management Science in Practice:Mead Corporation25

CHAPTER TWO Linear Programming:The Graphical Method27

2.1 A Simple Maximization Problem28

The Objective Function29

The Constraints30

Mathematical Statement of the Par,Inc.,Problem31

2.2 Graphical Solution32

A Note on Graphing Lines41

Summary of the Graphical Solution Procedure for Maximization Problems43

Slack Variables43

2.3 Extreme Points and the Optimal Solution45

2.4 A Simple Minimization Problem47

Surplus Variables50

Summary of the Graphical Solution Procedure for Minimization Problems50

Alternative Optimal Solutions52

2.5 Special Cases52

Infeasibility53

Unbounded54

2.6 Introduction to Sensitivity Analysis56

Obiective Function Coefficients57

2.7 Graphical Sensitivity Analysis57

Right-Hand Sides62

Glossary64

Summary64

Problems65

Case Problem:Production Strategy79

Case Problem:Advertising Strategy79

CHAPTER THREE Linear Programming:Formulation,Computer Solution,and Interpretation81

3.1 Computer Solution of Linear Programs81

Interpretation of Computer Output82

Simultaneous Changes85

Interpretation of Computer Output—A Second Example87

Cautionary Note on the Interpretation of Dual Prices89

3.2 More Than Two Decision Variables90

The Modified Par,Inc.,Problem90

The Bluegrass Farms Problem95

Formulation of the Bluegrass Farms Problem95

Computer Solution and Interpretation for the Bluegrass Farms Problem97

Guidelines for Model Formulation99

3.3 Modeling99

Management Science in Action:An Optimal Wood Procurement Policy100

The Electronic Communications Problem101

Formulation of the Electronic Communications Problem102

Computer Solution and Interpretation for the Electronic Communications Problem103

Management Science in Action:Using Linear Programming for Traffic Control106

Glossary107

Summary107

Problems108

case Problem:Product Mix121

Case Problem:Truck Leasing Strategy122

Appendix 3.2:Solving Linear Programs with LINDO/PC123

Appendix 3.1:Solving Linear Programs with The Management Scientist123

Appendix 3.3:Spreadsheet Solution of Linear Programs126

Management Science in Practice:Eastman Kodak130

CHAPTER FOUR Linear Programming Applications132

4.1 Marketing Applications132

Media Selection132

Marketing Research136

Portfolio Selection139

4.2 Financial Applications139

Financial Planning143

Management Science in Action:Using Linear Programming for Optimal Lease Structuring143

A Make-or-Buy Decision147

4.3 Production Management Applications147

Production Scheduling150

Management Science in Action:Libbey-Owens-Ford157

Work-Force Assignment157

4.4 Blending Problems161

Evaluating the Performance of Hospitals166

4.5 Data Envelopment Analvsis166

An Overview of the DEA Approach167

The DEA Linear Programming Model168

Summary of the DEA Approach172

Summary173

Problems174

Case Problem:Environmental Protection187

Case Problem:Investment Strategy189

Case Problem:Textile Mill Scheduling190

Appendix 4.1 Spreadsheet Solution of Linear Programs191

Management Science in Practice:Marathon Oil Company193

CHAPTET FIVE Linear Programming:The Simplex Method195

5.1 An Algebraic Overview of the Simplex Method195

Management Science in Action:Fleet Assignment at Delta Air Lines196

Algebraic Properties of the Simplex Method197

Determining a Basic Solution197

5.2 Tableau Form198

Basic Feasible Solutions198

5.3 Setting Up the Initial Simplex Tableau200

5.4 Improving the Solution203

5.5 Calculating the Next Tableau205

Interpreting the Results of an Iteration207

Moving toward a Better Solution208

Interpreting the Optimal Solution210

Summary of the Simplex Method211

5.6 Tableau Form:The General Case212

Greater-Than-or-Equal-to Constraints213

Equality Constraints217

Eliminating Negative Right-Hand-Side Values217

Summary of the Steps to Create Tableau Form218

5.7 Solving a Minimization Problem220

5.8 Special Cases222

Infeasibility222

Unboundedness223

Altemative Optimal Solutions224

Degeneracy226

Summary227

Problems229

Glossary229

Objective Function Coefficients239

6.1 Sensitivity Analysis with the Simplexrableau239

CHAPTER SIX Simplex-Based Sensitivity Analysis and Duality239

Right-Haod-Side Values244

Simultaneous Changes250

6.2 Duality251

Economic Interpretation of the Dual Variables254

Using the Dual to Identify the Primal Solution255

Findingthe Dual of Anv Primal Problem256

Glossary258

Summary258

Problems259

Management Science in Practice:Performance Analysis Corporation266

CHAPTER　SEVEN　Transportation,Assignment,and Transshipment Problems268

7.1 The Transportation Problem:The Network Model and a Linear Programming Formulation268

Problem Variations273

A General Linear Programming Model of the Transportation Problem274

7.2 The Assignment Problem:The Network Model and a Linear Programming Formulation276

Management Science in Action:Marine Corps Mobilization276

Problem Variations279

Multiple Assignments280

A General Linear Programming Model of the Assignment Problem280

7.3 The Transshipment Problem:The Nelwork Model and a Linear Programming Formulation281

Problem Variations286

A General Linear Programming Model of the Transshipment Problem287

7.4 A Production and Inventory Application288

7.5 The Transportation Simplex Method:A Special-Purpose Solution Procedure(Optional)291

Phase Ⅰ:Finding an Initial Feasible Solution292

Phase Ⅱ:Iterating to the Optimal Solution296

Summary of the Transportation Simplex Method305

Problem Variations306

7.6 The Assignment Problem:A Special-Purpose Solution Procedure(Optional)307

Finding the Minimum Number of Lines310

Problem Variations310

Summary313

Glossary314

Problems315

Case Problem:Assigning Umpire Crews330

Case Problem:Distribution System Design332

Management Science jn Practice:Procter Gamble334

CHAPTER EIGHT Integer Linear Programming335

Management Science in Action:Scheduling Employees at McDonald's Restaurant336

8.1 Types of Integer Linear Programming Models336

8.2 Graphicaland Computer Solution for an All-Integer Linear Program338

Graphical Solution Procedure338

Computer Solution341

Management Science in Action:Cutting Photographic Color Paper Rolls341

8.3 Applications342

Capital Budgeting342

Models Involving Fixed Costs344

Distribution System Design346

ABank Location Application350

8.4 Modeling Flexibility Provided by 0-1 Integer Variables354

Multiple-Choice and Mutually Exclusive Constraints355

Management Science in Action:Analyzing Price Quotations Under Business Volume Discounts355

k Out of n Alternatives Constraint356

Conditional and Corequisite Constraints356

Summary357

A Cautionary Note on Sensitivity Analysis357

Problems358

Glossary358

Case Problem:Textbook Publishing367

Case Problem:Production Scheduling with Changeover Costs369

Management Science in Practice:Ketron370

CHAPTER NINE Network Models372

9.1 The Shortest-Route Problem372

A Shortest-Route Algorithm373

A Minimal Spanning Tree Algorithm381

9.2 The Minimal Spanning Tree Problem381

9.3 The Maximal Flow Problem384

A Maximal Flow Algorithm385

Glossary390

Problems390

Summary390

Case Problem:Ambulance Routing397

Management Science in Practice:EDS399

CHAPTER TEN Project Scheduling:PERT/CPM401

10.1 Project Scheduling with Known Activity Times402

The Concepts of a Critical Path403

Determining the Critical Path404

Contributions of PERT/CPM409

Management Science in Action:Project Management on the PC410

Summary of the PERT/CPM Critical Path Procedure411

The Daugherty Porta-Vac Project412

10.2 Project Scheduling with Uncertain Activity Times412

Uncertain Activity Times413

The Critical Path415

Variability in Project Completion Time418

10.3 Considering Time-Cost Trade-Offs420

Crashing Activity Times421

A Linear Programming Model for Crashing Decisions423

Summary425

Glossary425

Problems426

Case Problem:Warehouse Expansion435

Management Science in Practice:Seasongood Mayer436

CHAPTER ELEVEN Inventory Models439

11.1 Economic Order Quantity(EOQ)Model　440

The How-Much-to-Order Decision443

The When-to-Order Decision445

Sensitivity Analysis in the EOQ Model446

The Manager's Use of the EOQ Model446

A Summary of the EOQ Model Assumptions447

How Has the EOQ Decision Model Helped?447

11.2 Economic Production Lot Size Model448

The Total Cost Model449

Finding the Economic Production Lot Size451

11.4 Quantity Discounts for the EOQ Model452

11.3 An Inventory Model with Planned Shortages452

11.5 A Single-Period lnventory Model with Probabilistic Demand458

The Johnson Shoe Company Problem459

The Kremer Chemical Company Problem462

11.6 An Order-Quantity,Reorder-Point Model with Probabilistic Demand464

The When-to-Order Decision466

The How-Much-to-Order Decision466

11.7 A Periodic-Review Model with Probabilistic Demand468

Management Science in Action:Information from a Netherlands Supplier Lowers Inventory Cost468

More Complex Periodic-Review Models471

Management Science in Action:Inventory Model Helps Hewlett-Packard's Product Design for Worldwide Markets471

11.8 Material Requirements Planning472

Dependent Demand and the MRP Concept473

Information System for MRP474

MRP Calculations476

11.9 The Just-in-Time Approach to Inventory Management478

Summary479

Glossary479

Problems481

Case Problem:A Make-or-Buy Analysis485

Appendix 11.1:Inventory Models with Spreadsheets489

Appendix 11.3 Development of the Optimal Lot Size(Q)Formula for the Production Lot Size Model492

Appendix 11.2 Development of the Optimal Order-Quantity(Q)Formula for the EOQ Model492

Appendix 11.4 Development of the Optimal Order-Quantity(Q)and Optimal Backorder(S)Formulas for the Planned Shortage Model493

Management Science in Practice:SupeRx.Inc.495

CHAPTER TWELVE Waiting Line Models497

12.1 The Structure of a Waiting Line System498

The Single-Channel Waiting Line498

The Distribution of Arrivals498

The Distribution of Service Times499

Queue Discipline500

Steady-State Operation500

The Operating Characteristics501

12.2 The Single-Channel Waiting Line Model with Poisson Arrivals and Exponential Service Times501

Operating Characteristics for the Burger Dome Problem502

The Manager's Use of Waiting Line Models503

Improving the Waiting Line Operation503

12.3 The Multiple-Channel Waiting Line Model with Poisson Arrivals and Exponential Service Times504

The Operatinig Characteristics505

Operating Characteristics for the Burger Dome Problem506

Management Science in Action:Hospital Staffing Based on a Multiple-Channel Waiting Line Model509

12.4 Some General Relationships for Waiting Line Models509

12.5 Economic Analysis of Waiting Lines511

12.6 Other Waiting Line Models513

Operating Characteristics for the M/G/l Model514

12.7 The Single-Channel Waiting Line Model with Poisson Arrivals and Arbitrary Service Times514

Constant Service Times515

12.8 A Multiple-Channel Model with Poisson Arrivals,Arbitrary Service Times,and No Waiting Line516

The Operating Characteristics for the M/G/K Model with Blocked Customers Cleared517

The Operating Characteristics for the M/M/l Model with a Finite Calling Population519

12.9 Waiting Line Models with Finite Calling Populations519

Summary522

Management Science in Action:Improving Fire Department Productivity522

Glossary523

Problems523

Case Problem:Airline Reservations530

Appendix 12.1:Waiting Line Models with Spreadsheets531

Management Science in Practice:CITIBANK533

CHAPER THIRETTEN Simulation535

13.1 Using Simulation for Risk Analysis536

The PortaCom Project536

The PortaCom Simulation Model537

Random Numbers and Simulating Values of Random Variables539

Using the Simulation Model541

Risk Analysis Conclusions542

Simulation Results542

13.2 An Inventory Simulation Model543

Some Simulation Terminology543

13.3 A Waiting Line Simulation Model546

The Hammondsport Savings and Loan Waiting Line546

Customer Arrival Times546

Customer Service Times547

The Simulation Model548

Simulation Results551

Management Science in Action:Red Cross Uses Simulation to Improve Bloodmobile Services553

Selecting a Simulation Language554

13.4 Other lssues554

Verification and Validation555

Keeping Track of Time556

Advantages and Disadvantages556

Management Science in Action:Simulation at Mexico's Vilpac Truck Company557

Summary557

Glossary558

Problems558

Case Problem:County Beverage Drive-Thru567

Case Problem:Machine Repair568

Appendix 13.1 Simulation with Spreadsheets569

Management Science in Practice:The Upjohn Company573

CHAPTER FOURTEEN Decision Analysis575

Payoff Tables576

14.1 Structuring the Decision Problem576

Decision Trees577

14.2 Decision Making Without Probabilities578

Optimistic Approach579

Conservative Approach579

Minimax Regret Approach580

14.3 Decision Making with Probabilities581

14.4 Sensitivity Analvsis584

Management Science in Action:Decision Analysis and the Selection of Home Mortgages584

14.5 Expected Value of Perfect Information587

14.6 Decision Analvsis with Sample Information589

14.7 Developing a Decision Strategy591

Computing Branch Probabilities592

An Optimal Decision Strategy594

Management Science in Action:Decision Analysis and Drug Testing for Student Athletes596

14.8 Expected Value of Sample Information597

Efficiency of Sample Information598

The Meaning of Utility599

14.9 Utility and Decision Making599

Developing Utilities for Payoffs600

The Expected Utility Approach603

Glossary605

Summary605

Problems606

Case Problem:Property Purchase Strategy622

Appendix 14.1:Decision Analysis and Spreadsheets623

Management Science in Practice:Ohio Edison Company627

CHAPTET FIFTEEN Multicriteria Decision Problems630

15.1 Goal Programming:Formulation and Graphical Solution631

Developing the Constraints and the Goal Equations632

Developing an Objective Function with Preemptive Priorities633

The Graphical Solution Procedure634

The Goal Programming Model638

15.2 Goal Programming:Solving More Complex Problems639

The Suncoast Office Supplies Problem639

Formulating the Goal Equations640

Formulating the Objective Function641

Computer Solution643

15.3 The Analytic Hierarchy Process646

Management Science in Action:Using AHP and Goal Programming to Plan Facility Locations647

Developing the Hierarchy648

15.4 Establishing Priorities Using AHP648

The Pairwise Comparison Matrix649

Pairwise Comparisons649

Procedure for Synthesizing Judgments650

Synthesis650

Consistency651

Estimating the Consistency Ratio652

Other Pairwise Comparisons for the Car-Selection Problem653

15.5 Using AHP to Develop an Overall Priority Ranking655

15.6 Using Expert Choice to Implement AHP656

Summary659

Glossary660

Problems661

Case Problem:Production Scheduling668

CHAPTER XIXTEEN Forecasting669

16.1 The Components of a Time Series670

Trend Component671

Cyclical Component672

Seasonal Component672

Irregular Component673

16.2 Smoothing Methods673

Moving Averages673

Weighted Moving Averages676

Exponential Smoothing676

16.3 Trend Projection681

16.4 Trend and Seasonal Components684

The Multiplicative Model685

Calculating the Seasonal Indexes685

Deseasonalizing the Time Series689

Using the Deseasonalized Time Series to Identify Trend690

Seasonal Adjustments692

Models Based on Monthy Data692

Cyclical Component692

16.5 Forecasting Using Regression Models693

Management Science in Action:Spare Parts Forecasting at American Airlines693

Using Regression Analysis When Time Series Data Are Not Available694

Using Regression Analysis with Time Series Data698

Delphi Method700

16.6 Qualitative Approaches to Forecasting700

Scenario Writing701

Expert Judgment701

Management Science in Action:The Business Week Industry Outlook 70lIntuitive Approaches702

Summary702

Glossary702

Problems703

Case Problem:Forecasting Sales712

Case Problem:Forecasting Lost Sales713

Appendix 16.1 Forecasting with Spreadsheets714

Management Science in Practice:The Cincinnati Gas Electric Company716

CHAPTER SEVENTEEN Markov Processes718

17.1 Market Share Analysis718

17.2 Accounts Receivable Analysis726

The Fundamental Matrix and Associated Calculations727

Establishing the Allowance for Doubtful Accounts729

Problems731

Glossary731

Summary731

Management Science in Practice:U.S.General Accounting Office735

18.1 A Shortest-Route Problem737

CHAPTER ELGHTEEN Dynamic Programming737

18.2 Dynamic Programming Notation742

18.3 The Knapsack Problem745

18.4 A Production and Inventory Control Problem751

Summary755

Glossary755

Problems756

Management Science in Practice:The U.S.Environmental Protection Agency762