《运筹学导论》(Introduction to Operations Research)文字版; 第九版[PDF]
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| [运筹学导论].Introduction.to.Operations.Research.9th.Edition.pdf | 20.07 MB |
| [运筹学导论].(Introduction.to.Operations.Research).FREDERICK.S.HILLIER.9th.Edition.pdf | 20.07 MB |
| 40.14 MB | |
电驴资源简介
| 资源介绍 |
| 语言: 英文 |
| 地区: 美国 |
| 图书fenlei: 管理 |
| 中文名: 运筹学导论 |
| 发行时间: 2010年 |
| 原名: Introduction to Operations Research |
| 资源格式: PDF |
| 版本: 文字版; 第九版 |
简介:  内容介绍 运筹学最新版, 美国管理类课程必备课本.筒子们不用几百刀买书了... 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. 内容截图 
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目录: 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 |