導讀： 資源介紹 語言: 英文 地區: 美國 圖書fenlei: 管理 中文名: 運籌學導論 發行時間: 2010年 原名: Introduction to Operations Research 資源格式: PDF 版本: 文字版; 第九版 簡介: 內容介紹運籌學最新版 資源介紹 語言: 英文 地區: 美國 圖書fenlei: 管理 中文名: 運籌學導論 發行時間: 2010年 原名: Introduction to Operations Research 資源格式: PDF 版本: 文字版; 第九版 簡介:
內容介紹
運籌學最新版, 美國管理類課程必備課本.筒子們不用幾百刀買書了...
A Greatly Increased Emphasis on Real Applications. Unbeknownst to the general
public, the field of operations research is continuing to have an increasingly dramatic
impact on the success of numerous companies and organizations around the world.
Therefore, a special goal of this edition has been to tell this story much more forcefully,
thereby exciting students about the great relevance of the material they are studying. We
have pursued this goal in four ways. One is the addition of 29 application vignettes separated
from the regular textual material that describe in a few paragraphs how an actual
application of operations research had a powerful impact on a company or organization
by using techniques like those being studied in that portion of the book. A second is the
addition of 71 selected references of award winning OR applications given at the end
of various chapters. A third is the addition of a link to the journal articles that fully
describe these 100 applications, through a special arrangement with INFORMS. The final
way is the addition of many problems that require reading one or more of these articles.
Thus, the instructor now can motivate his or her lectures by having the students
delve into real applications that dramatically demonstrate the relevance of the material
being covered in the lectures.
We are particularly excited about our new partnership with INFORMS, our field’s
preeminent professional society, to provide a link to these 100 articles describing dramatic
OR applications. The Institute for Operations Research and the Management
Sciences (INFORMS®) is a learned professional society for students, academics, and
practitioners in quantitative and analytical fields. Information about INFORMS®
journals, meetings, job bank, scholarships, awards, and teaching materials is at
www.informs.org.
• Approximately 200 New or Revised Problems. The new problems include the ones
involving real applications mentioned above. Other new problems also have been added,
including a considerable number that support the new or revised topics mentioned later.
Two new cases have been added for the chapter on decision analysis that are less complex
than the two that already were there. In addition, many of the problems from the
eighth edition have been revised. Therefore, an instructor who does not wish to assign
problems that were assigned in previous classes has a substantial number from which
to choose.
• An Updating of the Software Accompanying the Book. The next section will outline
the wealth of software options that are provided with this new edition. The main
difference from the eighth edition is that new, improved versions of several of the software
packages now are available. For example, Excel 2007 represents by far the most
major revision of Excel and its user interface in many, many years, so this new version
of Excel and its Solver has been fully integrated into the book (while pointing
out differences for those still using old versions). Another important example is that,
for the first time in 10 years, new versions of TreePlan and SensIt have just now
become available and have been fully integrated into the decision analysis chapter.
The latest versions of all the other software packages also are being provided with
this new edition.
• A New Section on Revenue Management. A hallmark of new editions of this book
has been the addition of substantial coverage of dramatic, recent developments that are
beginning to revolutionize how certain areas of operations research are being practiced.
For example, the eighth edition added a new chapter on metaheuristics, a new section
on the incorporation of constraint programming, and a new section on multiechelon inventory
models for supply chain management. This edition is adding another key new
topic with the addition of a complete section on revenue management in the chapter on
inventory theory. This is a timely addition because of the dramatic impact that revenue
management has been having in the airline industry and now is beginning to have in
several other industries.
• A Reorganization of the Chapter on the Theory of the Simplex Method. Some instructors
do not wish to take the time to cover the revised simplex method but may still
want to introduce the matrix form of the simplex method and may still want to cover
what we call the “fundamental insight” regarding the simplex method. Therefore, rather
than covering the revised simplex method in Section 5.2 before turning to the fundamental
insight in Section 5.3—as in the eighth edition—we now simply introduce the
matrix form of the simplex method in Section 5.2, which flows directly into the fundamental
insight in Section 5.3, after which we focus on the revised simplex method as
an optional topic in Section 5.4.
• A Simplified Method for Determining Utilities. Among the various other smaller revisions
throughout the book, perhaps the most noteworthy is a simplified presentation
in Section 15.6 of how to determine utilities. This is done through outlining a simple
“equivalent lottery method.”
• A Reorganization to Reduce the Size of the Book. An unfortunate trend with early
editions of this book was that each new edition was significantly larger than the previous
one. This continued until the seventh edition had become considerably larger than
is desirable for an introductory survey textbook. Therefore, I worked hard to substantially
reduce the size of the eighth edition and adopted the goal of avoiding any growth
in subsequent editions. The goal has been achieved for the current edition. This was
accomplished through a variety of means. One was being careful not to add too much
new material. Another was deleting two sections on real applications that had been in
the eighth edition but no longer were needed because of the addition of application vignettes.
Another was moving both the long Appendix 3.1 on the LINGO modeling language
and the section on optimizing with OptQuest to the supplements on the book’s
website. (This decision regarding OptQuest was made easy by the fact that a new version
is due out momentarily, but not in time for this edition, so it will be added later
as a supplement.) Finally, a considerable number of sections were shortened. Otherwise,
I have stuck closely to what I hope has become the familiar organization of the
eighth edition after having made major changes for that edition.
• Updating to Reflect the Current State of the Art. A special effort has been made to
keep the book completely up to date. This has included carefully updating both the selected
references at the end of each chapter and the various footnotes referencing the
latest research on the topics being covered.
內容截圖
目錄: PREFACE xviii
CHAPTER 1
Introduction 1
1.1 The Origins of Operations Research 1
1.2 The Nature of Operations Research 2
1.3 The Impact of Operations Research 3
1.4 Algorithms and OR Courseware 5
Selected References 7
Problems 7
CHAPTER 2
Overview of the Operations Research Modeling Approach 8
2.1 Defining the Problem and Gathering Data 8
2.2 Formulating a Mathematical Model 11
2.3 Deriving Solutions from the Model 13
2.4 Testing the Model 16
2.5 Preparing to Apply the Model 17
2.6 Implementation 18
2.7 Conclusions 19
Selected References 19
Problems 20
CHAPTER 3
Introduction to Linear Programming 23
3.1 Prototype Example 24
3.2 The Linear Programming Model 30
3.3 Assumptions of Linear Programming 36
3.4 Additional Examples 42
3.5 Formulating and Solving Linear Programming Models on a Spreadsheet 60
3.6 Formulating Very Large Linear Programming Models 68
3.7 Conclusions 75
Selected References 75
Learning Aids for This Chapter on Our Website 76
Problems 77
Case 3.1 Auto Assembly 86
Previews of Added Cases on Our Website 88
Case 3.2 Cutting Cafeteria Costs 88
Case 3.3 Staffing a Call Center 88
Case 3.4 Promoting a Breakfast Cereal 88
CHAPTER 4
Solving Linear Programming Problems: The Simplex Method 89
4.1 The Essence of the Simplex Method 89
4.2 Setting Up the Simplex Method 94
4.3 The Algebra of the Simplex Method 97
4.4 The Simplex Method in Tabular Form 103
4.5 Tie Breaking in the Simplex Method 108
4.6 Adapting to Other Model Forms 111
4.7 Postoptimality Analysis 129
4.8 Computer Implementation 137
4.9 The Interior-Point Approach to Solving Linear Programming Problems 140
4.10 Conclusions 145
Appendix 4.1 An Introduction to Using LINDO and LINGO 145
Selected References 149
Learning Aids for This Chapter on Our Website 149
Problems 150
Case 4.1 Fabrics and Fall Fashions 158
Previews of Added Cases on Our Website 160
Case 4.2 New Frontiers 160
Case 4.3 Assigning Students to Schools 160
CHAPTER 5
The Theory of the Simplex Method 161
5.1 Foundations of the Simplex Method 161
5.2 The Simplex Method in Matrix Form 172
5.3 A Fundamental Insight 181
5.4 The Revised Simplex Method 184
5.5 Conclusions 187
Selected References 187
Learning Aids for This Chapter on Our Website 188
Problems 188
CHAPTER 6
Duality Theory and Sensitivity Analysis 195
6.1 The Essence of Duality Theory 196
6.2 Economic Interpretation of Duality 203
6.3 Primal–Dual Relationships 206
6.4 Adapting to Other Primal Forms 211
6.5 The Role of Duality Theory in Sensitivity Analysis 215
6.6 The Essence of Sensitivity Analysis 217
6.7 Applying Sensitivity Analysis 225
6.8 Performing Sensitivity Analysis on a Spreadsheet 245
6.9 Conclusions 259
Selected References 260
Learning Aids for This Chapter on Our Website 260
Problems 261
Case 6.1 Controlling Air Pollution 274
Previews of Added Cases on Our Website 275
Case 6.2 Farm Management 275
Case 6.3 Assigning Students to Schools, Revisited 275
Case 6.4 Writing a Nontechnical Memo 275
CHAPTER 7
Other Algorithms for Linear Programming 276
7.1 The Dual Simplex Method 276
7.2 Parametric Linear Programming 280
7.3 The Upper Bound Technique 285
7.4 An Interior-Point Algorithm 287
7.5 Conclusions 298
Selected References 299
Learning Aids for This Chapter on Our Website 299
Problems 300
CHAPTER 8
The Transportation and Assignment Problems 304
8.1 The Transportation Problem 305
8.2 A Streamlined Simplex Method for the Transportation Problem 319
8.3 The Assignment Problem 334
8.4 A Special Algorithm for the Assignment Problem 342
8.5 Conclusions 346
Selected References 347
Learning Aids for This Chapter on Our Website 347
Problems 348
Case 8.1 Shipping Wood to Market 356
Previews of Added Cases on Our Website 357
Case 8.2 Continuation of the Texago Case Study 357
Case 8.3 Project Pickings 357
CHAPTER 9
Network Optimization Models 358
9.1 Prototype Example 359
9.2 The Terminology of Networks 360
9.3 The Shortest-Path Problem 363
9.4 The Minimum Spanning Tree Problem 368
9.5 The Maximum Flow Problem 373
9.6 The Minimum Cost Flow Problem 380
9.7 The Network Simplex Method 389
9.8 A Network Model for Optimizing a Project’s Time-Cost Trade-Off 399
9.9 Conclusions 410
Selected References 411
Learning Aids for This Chapter on Our Website 411
Problems 412
Case 9.1 Money in Motion 420
Previews of Added Cases on Our Website 423
Case 9.2 Aiding Allies 423
Case 9.3 Steps to Success 423
CHAPTER 10
Dynamic Programming 424
10.1 A Prototype Example for Dynamic Programming 424
10.2 Characteristics of Dynamic Programming Problems 429
10.3 Deterministic Dynamic Programming 431
10.4 Probabilistic Dynamic Programming 451
10.5 Conclusions 457
Selected References 457
Learning Aids for This Chapter on Our Website 457
Problems 458
CHAPTER 11
Integer Programming 464
11.1 Prototype Example 465
11.2 Some BIP Applications 468
11.3 Innovative Uses of Binary Variables in Model Formulation 473
11.4 Some Formulation Examples 479
11.5 Some Perspectives on Solving Integer Programming Problems 487
11.6 The Branch-and-Bound Technique and Its Application to Binary
Integer Programming 491
11.7 A Branch-and-Bound Algorithm for Mixed Integer
Programming 503
11.8 The Branch-and-Cut Approach to Solving BIP Problems 509
11.9 The Incorporation of Constraint Programming 515
11.10 Conclusions 521
Selected References 522
Learning Aids for This Chapter on Our Website 523
Problems 524
Case 11.1 Capacity Concerns 533
Previews of Added Cases on Our Website 535
Case 11.2 Assigning Art 535
Case 11.3 Stocking Sets 535
Case 11.4 Assigning Students to Schools, Revisited Again 536
CHAPTER 12
Nonlinear Programming 537
12.1 Sample Applications 538
12.2 Graphical Illustration of Nonlinear Programming Problems 542
12.3 Types of Nonlinear Programming Problems 546
12.4 One-Variable Unconstrained Optimization 552
12.5 Multivariable Unconstrained Optimization 557
12.6 The Karush-Kuhn-Tucker (KKT) Conditions for Constrained Optimization 563
12.7 Quadratic Programming 567
12.8 Separable Programming 573
12.9 Convex Programming 580
12.10 Nonconvex Programming (with Spreadsheets) 588
12.11 Conclusions 592
Selected References 593
Learning Aids for This Chapter on Our Website 593
Problems 594
Case 12.1 Savvy Stock Selection 605
Previews of Added Cases on Our Website 606
Case 12.2 International Investments 606
Case 12.3 Promoting a Breakfast Cereal, Revisited 606
CHAPTER 13
Metaheuristics 607
13.1 The Nature of Metaheuristics 608
13.2 Tabu Search 615
13.3 Simulated Annealing 626
13.4 Genetic Algorithms 635
13.5 Conclusions 645
Selected References 646
Learning Aids for This Chapter on Our Website 646
Problems 647
CHAPTER 14
Game Theory 651
14.1 The Formulation of Two-Person, Zero-Sum Games 651
14.2 Solving Simple Games—A Prototype Example 653
14.3 Games with Mixed Strategies 658
14.4 Graphical Solution Procedure 660
14.5 Solving by Linear Programming 662
14.6 Extensions 666
14.7 Conclusions 667
Selected References 667
Learning Aids for This Chapter on Our Website 667
Problems 668
CHAPTER 15
Decision Analysis 672
15.1 A Prototype Example 673
15.2 Decision Making without Experimentation 674
15.3 Decision Making with Experimentation 680
15.4 Decision Trees 686
15.5 Using Spreadsheets to Perform Sensitivity Analysis on Decision Trees 690
15.6 Utility Theory 700
15.7 The Practical Application of Decision Analysis 707
15.8 Conclusions 708
Selected References 709
Learning Aids for This Chapter on Our Website 709
Problems 710
Case 15.1 Brainy Business 720
Preview of Added Cases on Our Website 722
Case 15.2 Smart Steering Support 722
Case 15.3 Who Wants to be a Millionaire? 722
Case 15.4 University Toys and the Engineering Professor Action Figures 722
CHAPTER 16
Markov Chains 723
16.1 Stochastic Processes 723
16.2 Markov Chains 725
16.3 Chapman-Kolmogorov Equations 732
16.4 Classification of States of a Markov Chain 735
16.5 Long-Run Properties of Markov Chains 737
16.6 First Passage Times 743
16.7 Absorbing States 745
16.8 Continuous Time Markov Chains 748
Selected References 753
Learning Aids for This Chapter on Our Website 753
Problems 754
CHAPTER 17
Queueing Theory 759
17.1 Prototype Example 760
17.2 Basic Structure of Queueing Models 760
17.3 Examples of Real Queueing Systems 765
17.4 The Role of the Exponential Distribution 767
17.5 The Birth-and-Death Process 773
17.6 Queueing Models Based on the Birth-and-Death Process 777
17.7 Queueing Models Involving Nonexponential Distributions 790
17.8 Priority-Discipline Queueing Models 798
17.9 Queueing Networks 803
17.10 The Application of Queueing Theory 807
17.11 Conclusions 812
Selected References 812
Learning Aids for This Chapter on Our Website 813
Problems 814
Case 17.1 Reducing In-Process Inventory 826
Preview of an Added Case on Our Website 827
Case 17.2 Queueing Quandary 827
CHAPTER 18
Inventory Theory 828
18.1 Examples 829
18.2 Components of Inventory Models 831
18.3 Deterministic Continuous-Review Models 833
18.4 A Deterministic Periodic-Review Model 843
18.5 Deterministic Multiechelon Inventory Models for Supply
Chain Management 848
18.6 A Stochastic Continuous-Review Model 866
18.7 A Stochastic Single-Period Model for Perishable Products 870
18.8 Revenue Management 882
18.9 Conclusions 890
Selected References 890
Learning Aids for This Chapter on Our Website 891
Problems 892
Case 18.1 Brushing Up on Inventory Control 902
Previews of Added Cases on Our Website 904
Case 18.2 TNT: Tackling Newsboy’s Teachings 904
Case 18.3 Jettisoning Surplus Stock 904
CHAPTER 19
Markov Decision Processes 905
19.1 A Prototype Example 905
19.2 A Model for Markov Decision Processes 908
19.3 Linear Programming and Optimal Policies 911
19.4 Policy Improvement Algorithm for Finding Optimal Policies 915
19.5 Discounted Cost Criterion 920
19.6 Conclusions 928
Selected References 928
Learning Aids for This Chapter on Our Website 929
Problems 929
CHAPTER 20
Simulation 934
20.1 The Essence of Simulation 934
20.2 Some Common Types of Applications of Simulation 946
20.3 Generation of Random Numbers 951
20.4 Generation of Random Observations from a Probability Distribution 955
20.5 Outline of a Major Simulation Study 959
20.6 Performing Simulations on Spreadsheets 963
20.7 Conclusions 979
Selected References 981
Learning Aids for This Chapter on Our Website 982
Problems 983
Case 20.1 Reducing In-Process Inventory, Revisited 989
Case 20.2 Action Adventures 989
Previews of Added Cases on Our Website 990
Case 20.3 Planning Planers 990
Case 20.4 Pricing under Pressure 990
APPENDIXES
1. Documentation for the OR Courseware 991
2. Convexity 993
3. Classical Optimization Methods 998
4. Matrices and Matrix Operations 1001
5. Table for a Normal Distribution 1006
PARTIAL ANSWERS TO SELECTED PROBLEMS 1008
INDEXES
Author Index 1023
Subject Index 1029