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Operations research : principles and practice 2nd ed

Operations research : principles and practice 2nd ed (11회 대출)

자료유형
단행본
개인저자
Phillips, Don T. Ravindran, A., 1944-. Solberg, James J., 1942-.
서명 / 저자사항
Operations research : principles and practice / A. Ravindran, Don T. Phillips, James J. Solberg.
판사항
2nd ed.
발행사항
New York :   Wiley,   c1987.  
형태사항
xviii, 637 p. : ill. ; 27 cm.
ISBN
0471086088
서지주기
Includes bibliographies and index.
일반주제명
Operations research.
비통제주제어
Operations research,,
000 00975camuuu200313 a 4500
001 000000586704
003 OCoLC
005 19980527151030.0
008 860303s1987 nyua b 001 0 eng
010 ▼a 86005561
015 ▼a GB88-13175
019 ▼a 17585953
020 ▼a 0471086088
040 ▼a DLC ▼c DLC ▼d UKM
049 ▼l 121007007 ▼f 과학 ▼l 121007008 ▼f 과학 ▼l 121007172 ▼f 과학 ▼l 121007377 ▼f 과학 ▼l 121007378 ▼f 과학
050 0 ▼a T57.6 ▼b .P48 1987
082 0 ▼a 001.4/24 ▼2 19
090 ▼a 001.424 ▼b P558o2
100 1 ▼a Phillips, Don T.
245 1 0 ▼a Operations research : ▼b principles and practice / ▼c A. Ravindran, Don T. Phillips, James J. Solberg.
250 ▼a 2nd ed.
260 ▼a New York : ▼b Wiley, ▼c c1987.
300 ▼a xviii, 637 p. : ▼b ill. ; ▼c 27 cm.
504 ▼a Includes bibliographies and index.
650 0 ▼a Operations research.
653 ▼a Operations research
700 1 ▼a Ravindran, A., ▼d 1944-.
700 1 ▼a Solberg, James J., ▼d 1942-.

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컨텐츠정보

목차


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


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