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Totally revised, this 2nd Edition includes a new chapter on decision analysis, and additional material on computer solutions of linear programming problems, LP applications, the use of sensitivity analysis output, minimal spanning tree, goal programming, network of queues, and more. Throughout, mathematics is kept to an intermediate level.

Totally revised, this 2nd Edition includes a new chapter on decision analysis, and additional material on computer solutions of linear programming problems, LP applications, the use of sensitivity analysis output, minimal spanning tree, goal programming, network of queues, and more. Throughout, mathematics is kept to an intermediate level.

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CONTENTS
CHAPTER 1 THE NATURE OF OPERATIONS RESEARCH = 1
 1.1 The History of Operations Research = 1
 1.2 The Meaning of Operations Research = 3
 1.3 Models in Operations Research = 4
 1.4 Principles of Modeling = 7
CHAPTER 2 LINEAR PROGRAMMING = 13
 2.1 Introduction = 13
 2.2 Formulation of Linear Programming Models = 14
 2.3 Graphical Solution of Linear Programs in Two Variables = 23
 2.4 Linear Program in Standard Form = 27
 2.5 Solving Systems of Linear Equations = 30
 2.6 Principles of the Simplex Method = 33
 2.7 Simplex Method in Tableau Form = 36
 2.8 Computational Problems = 43
 2.9 Finding a Feasible Basis = 46
 2.10 Computer Solution of Linear Programs = 53
 2.11 Sensitivity Analysis in Linear Programming = 55
 2.12 Applications = 58
 2.13 Additional Topics in Linear Programming = 59
  Recommended Readings = 60
  References = 60
  Exercises = 61
CHAPTER 3 NETWORK ANALYSIS = 73
 3.1 Some Examples of Network Flow Problems = 73
 3.2 Transportation Problems = 73
 3.3 AssignmentProblems = 88
 3.4 Maximal-Flow Problems = 92
 3.5 Shortest-Route Problems = 102
 3.6 Minimal Spanning Tree Problems = 107
 3.7 Project Management = 109
  Recommended Readings = 126
  References = 126
  Exercises = 127
CHAPTER 4 ADVANCED TOPICS IN LINEAR PROGRAMMING = 135
 4.1 The Revised Simplex Method = 135
 4.2 Duality Theory and Its Applications = 149
 4.3 The Dual Simplex Method = 163
 4.4 Sensitivity Analysis in Linear Programming = 169
 4.5 Parametric Programming = 179
 4.6 Integer Programming = 184
 4.7 Goal Programming = 198
  Recommended Readings = 207
  References = 207
  Exercises = 209
CHAPTER 5 DECISION ANALYSIS = 221
 5.1 Introduction = 221
 5.2 Characteristics of a Decision Problem = 223
 5.3 Terminal Decisions Based on Prior Information = 224
 5.4 Expected Value of Perfect Information (EVPI) = 229
 5.5 Decision Trees = 232
 5.6 Sequential Decisions = 234
 5.7 Information Acquisition Decisions = 239
  Summary = 245
  References = 245
  Exercises = 246
CHAPTER 6 RANDOM PROCESSES = 251
 6.1 Introduction = 251
 Ⅰ DISCRETETIMEPROCESSES = 252
  6.2 An Example = 252
  6.3 Modeling the Process = 252
  6.4 A Numerical Example = 260
  6.5 The Assumptions Reconsidered = 261
  6.6 Formal Definitions and Theory = 262
  6.7 First-Passage and First-Return Probabilities = 264
  6.8 Classification Terminology = 265
  6.9 Ergodic Markov Chains = 268
  6.10 Absorbing Markov Chains = 272
 Ⅱ CONTINUOUSTIMEPROCESSES = 278
  6.11 An Example = 278
  6.12 Formal Definitions and Theory = 282
  6.13 The Assumptions Reconsidered = 286
  6.14 Steady-State Probabilities = 287
  6.15 Birth-Death Processes = 290
  6.16 The Poisson Process = 291
  6.17 Conclusions = 296
   Recommended Readings = 296
   References = 297
   Exercises = 297
CHAPTER 7 QUEUEING MODELS = 305
 7.1 Introduction = 305
 7.2 An Example = 305
 7.3 General Characteristics = 309
 7.4 Performance Measures = 312
 7.5 Relations Among the Performance Measures = 314
 7.6 Markovian Queueing Models = 315
 7.7 TheM／M／iModel = 318
 7.8 Limited Queue Capacity = 322
 7.9 Multiple Servers = 325
 7.10 An Example = 327
 7.11 Finite Sources = 329
 7.12 Queue Disciplines = 331
 7.13 Non-Markovian Queues = 332
 7.14 Networks of Queues = 335
 7.15 Conclusions = 336
  Recommended Readings = 337
  References = 337
  Exercises = 337
CHAPTER 8 INVENTORY MODELS = 343
 8.1 Introduction = 343
 Ⅰ DETERMINISTIC MODELS = 344
  8.2 The Classical Economic Order Quantity = 345
  8.3 A Numerical Example = 348
  8.4 Sensitivity Analysis = 348
  8.5 Nonzero Lead Time = 349
  8.6 The EOQ with Shortages Allowed = 350
  8.7 The Production Lot-Size Model = 351
  8.8 Other Deterministic Inventory Models = 351
 Ⅱ PROBABILISTICMODELS = 352
  8.9 The Newsboy Problem: A Single Period Model = 353
  8.10 A Lot Size, Reorder Point Model = 356
  8.11 Some Numerical Examples = 364
  8.12 Variable Lead Times = 367
  8.13 The Importance of Selecting the Right Model = 369
  8.14 Conclusions = 370
   Recommended Readings = 370
   References = 370
   Exercises = 371
CHAPTER 9 SIMULATION = 375
 Ⅰ BASIC CONCEPTS = 375
  9.1 Introduction = 375
  9.2 The Philosophy, Development and Implementation of Simulation Modeling = 377
  9.3 Design of Simulation Models = 381
 Ⅱ EXAMPLESOFSIMULATIONMODELING = 383
  9.4 Performance of a Baseball Hitter = 383
  9.5 Simulation of a Tool Crib = 385
  9.6 Production Line Maintenance = 388
 Ⅲ PSEUDO-RANDOM NUMBERS = 397
  9.7 The Uniform Distribution and Its Importance to Simulation = 397
  9.8 Generation of Random Numbers = 398
  9.9 The Logic in Generating Uniform Random Variates via a Congruential Method = 399
  9.10 Testing a Uniform Random-Number Generator = 400
 Ⅳ TECHNIQUES FOR GENERATING RANDOM DEVIATES = 401
  9.11 The lnverse Transformation Method = 401
  9.12 The Rejection Technique = 404
  9.13 The Composition Method = 408
  9.14 Mathematical Derivation Technique = 411
   The Box and Muller Technique for Generating Normal Deviates = 412
  9.15 Approximation Techniques = 412
  9.16 Special Probability Distributions = 414
 Ⅴ SIMULATION LANGUAGES = 416
  9.17 An Overview = 416
  9.18 Comparison of Selected Existing Simulation Languages = 418
   GPSS (General Purpose Systems Simulator) = 418
   SIMSCRIPT 2.5 = 419
   MAP / Ⅰ = 419
   SIMULA = 420
   DYNAMO = 421
   SLAM Ⅱ = 421
   SIMAN = 422
  9.19 The Mictocomputer Revolution in Simulation Applications = 423
 Ⅵ ADVANCED CONCEPTS IN SIMULATION ANALYSIS = 425
  9.20 Design of Simulation Experiments = 426
  9.21 Variance Reduction Techniques = 426
  9.22 Statistical Analysis of Simulation Output = 427
  9.23 Optimization of Simulation Parameters = 428
  9.24 Summary and Conclusions = 428
   Selected Reference Texts = 429
   References = 429
   Exercises = 432
CHAPTER 10 DYNAMIC PROGRAMMING = 437
 Ⅰ BASIC CONCEPTS = 437
  10.1 Introduction = 437
  10.2 Historical Background = 437
 Ⅱ THE DEVELOPMENT OF DYNAMIC PROGRAMMING = 438
  10.3 Mathematical Description = 438
  10.4 Developing an Optimal Decision Policy = 441
  10.5 Dynamic Programming in Perspective = 443
 Ⅲ ILLUSTRATIVE EXAMPLES = 443
  10.6 A Problem in Oil Transport Technology = 443
  10.7 A Facilities Selection Problem = 451
  10.8 The Optimal Cutting Stock Problem = 456
  10.9 A Problem in Inventory Control = 463
 Ⅳ CONTINUOUSSTATE DYNAMIC PROGRAMMING = 467
  10.10 Introduction = 467
  10.11 A Nonlinear Programming Problem = 467
  10.12 A Problem in Mutual Fund Investment Strategies = 469
 Ⅴ MULTIPLE STATE VARIABLES = 472
  10.13 The"Curse of Dimensionality" = 472
  10.14 A Nonlinear,lnteger Programming Problem = 472
  10.15 Elimination of State Variables = 477
 Ⅵ STOCHASTIC SYSTEMS = 477
  10.16 Stochastic Dynamic Programming-A Brief Overview = 477
  10.17 Summary and Conclusions = 479
   References = 479
   Exercises = 481
CHAPTER 11 NONLINEAR PROGRAMMING = 487
 Ⅰ BASIC CONCEPTS = 487
  11.1 Introduction = 487
  11.2 Taylor's Series Expansions ; Necessary and Sufficiency Conditions = 494
 Ⅱ UNCONSTRAINED OPTIMIZATION = 504
  11.3 Fibonacci and Golden Section Search = 504
  11.4 The Hooke and Jeeves Search Algorithm = 511
  11.5 Gradient Projection = 515
 Ⅲ CONSTRAINED OPTIMIZATION PROBLEMS: EQUALITY CONSTRAINTS = 524
  11.6 Lagrange Multipliers = 524
  11.7 Equality Constrained Optimization: Constrained Derivatives = 530
  11.8 Projected Gradient Methods with Equality Constraints = 534
 Ⅳ CONSTRAINED OPTIMIZATION PROBLEMS:INEQUALfTY CONSTRAINTS = 539
  11.9 Nonlinear Optimization-The Kuhn-Tucker Conditions = 539
  11.10 Quadratic Programming = 543
  11.11 Complementary Pivot Aigorithms = 547
  11.12 Separable Programming = 556
 Ⅴ THE GENERAL NONLINEAR PROGRAMMING PROBLEM = 563
  11.13 Nonlinear Objective Function Subject to Linear or NorAinear Constraints:A Cutting Planc Algorithm = 563
  11.14 Optimization by Geometric Programming = 569
   References = 579
   Exercises = 580
APPENDICES = 587
 Appendix A Review of Linear Algebra = 589
 Appendix B Probability Review = 597
 Appendix C Random Number Generation = 621
INDEX = 629