OR(テキスト・第4版)<br>Operations Research with Infotrac : Applications and Algorithms (4 HAR/CDR)

  • ポイントキャンペーン

OR(テキスト・第4版)
Operations Research with Infotrac : Applications and Algorithms (4 HAR/CDR)

  • ただいまウェブストアではご注文を受け付けておりません。 ⇒古書を探す
  • 製本 Hardcover:ハードカバー版
  • 言語 ENG
  • 商品コード 9780534380588
  • DDC分類 003

Full Description


The market-leading textbook for the course, Winston's OPERATIONS RESEARCH owes much of its success to its practical orientation and consistent emphasis on model formulation and model building. It moves beyond a mere study of algorithms without sacrificing the rigor that faculty desire. As in every edition, Winston reinforces the book's successful features and coverage with the most recent developments in the field. The Student Suite CD-ROM, which now accompanies every new copy of the text, contains the latest versions of commercial software for optimization, simulation, and decision analysis.

Table of Contents

Preface                                            xii
About the Author xvi
An Introduction to Model-Building 1 (10)
An Introduction to Modeling 1 (4)
The Seven-Step Model-Building Process 5 (1)
CITGO Petroleum 6 (1)
San Francisco Police Department Scheduling 7 (2)
GE Capital 9 (2)
Basic Linear Algebra 11 (38)
Matrices and Vectors 11 (9)
Matrices and Systems of Linear Equations 20 (2)
The Gauss-Jordan Method for Solving 22 (10)
Systems of Linear Equations
Linear Independence and Linear Dependence 32 (4)
The Inverse of a Matrix 36 (6)
Determinants 42 (7)
Introduction to Linear Programming 49 (78)
What Is a Linear Programming Problem? 49 (7)
The Graphical Solution of Two-Variable 56 (7)
Linear Programming Problems
Special Cases 63 (5)
A Diet Problem 68 (4)
A Work-Scheduling Problem 72 (4)
A Capital Budgeting Problem 76 (6)
Short-Term Financial Planning 82 (3)
Blending Problems 85 (10)
Production Process Models 95 (5)
Using Linear Programming to Solve 100 (5)
Multiperiod Decision Problems: An
Inventory Model
Multiperiod Financial Models 105 (4)
Multiperiod Work Scheduling 109 (18)
The Simplex Algorithm and Goal Programming 127 (100)
How to Convert an LP to Standard Form 127 (3)
Preview of the Simplex Algorithm 130 (4)
Direction of Unboundedness 134 (2)
Why Does an LP Have an Optimal bfs? 136 (4)
The Simplex Algorithm 140 (9)
Using the Simplex Algorithm to Solve 149 (3)
Minimization Problems
Alternative Optimal Solutions 152 (2)
Unbounded LPs 154 (4)
The LINDO Computer Package 158 (5)
Matrix Generators, LINGO, and Scaling of 163 (5)
LPs
Degeneracy and the Convergence of the 168 (4)
Simplex Algorithm
The Big M Method 172 (6)
The Two-Phase Simplex Method 178 (6)
Unrestricted-in-Sign Variables 184 (6)
Karmarkar's Method for Solving LPs 190 (1)
Multiattribute Decision Making in the 191 (11)
Absence of Uncertainty: Goal Programming
Using the Excel Solver to Solve LPs 202 (25)
Sensitivity Analysis: An Applied Approach 227 (35)
A Graphical Introduction to Sensitivity 227 (5)
Analysis
The Computer and Sensitivity Analysis 232 (14)
Managerial Use of Shadow Prices 246 (2)
What Happens to the Optimal z-Value If 248 (14)
the Current Basis Is No Longer Optimal?
Sensitivity Analysis and Duality 262 (98)
A Graphical Introduction to Sensitivity 262 (5)
Analysis
Some Important Formulas 267 (8)
Sensitivity Analysis 275 (14)
Sensitivity Analysis When More Than One 289 (6)
Parameter Is Changed: The 100% Rule
Finding the Dual of an LP 295 (7)
Economic Interpretation of the Dual 302 (2)
Problem
The Dual Theorem and Its Consequences 304 (9)
Shadow Prices 313 (10)
Duality and Sensitivity Analysis 323 (2)
Complementary Slackness 325 (4)
The Dual Simplex Method 329 (6)
Data Envelopment Analysis 335 (25)
Transportation, Assignment, and 360 (53)
Transshipment Problems
Formulating Transportation Problems 360 (13)
Finding Basic Feasible Solutions for 373 (9)
Transportation Problems
The Transportation Simplex Method 382 (8)
Sensitivity Analysis for Transportation 390 (3)
Problems
Assignment Problems 393 (7)
Transshipment Problems 400 (13)
Network Models 413 (62)
Basic Definitions 413 (1)
Shortest-Path Problems 414 (5)
Maximum-Flow Problems 419 (12)
CPM and PERT 431 (19)
Minimum-Cost Network Flow Problems 450 (6)
Minimum Spanning Tree Problems 456 (3)
The Network Simplex Method 459 (16)
Integer Programming 475 (87)
Introduction to Integer Programming 475 (2)
Formulating Integer Programming Problems 477 (35)
The Branch-and-Bound Method for Solving 512 (11)
Pure Integer Programming Problems
The Branch-and-Bound Method for Solving 523 (1)
Mixed Integer Programming Problems
Solving Knapsack Problems by the 524 (3)
Branch-and-Bound Method
Solving Combinatorial Optimization 527 (13)
Problems by the Branch-and-Bound Method
Implicit Enumeration 540 (5)
The Cutting Plane Algorithm 545 (17)
Advanced Topics in Linear Programming 562 (48)
The Revised Simplex Algorithm 562 (5)
The Product Form of the Inverse 567 (3)
Using Column Generation to Solve 570 (6)
Large-Scale LPs
The Dantzig-Wolfe Decomposition Algorithm 576 (17)
The Simplex Method for Upper-Bounded 593 (4)
Variables
Karmarkar's Method for Solving LPs 597 (13)
Nonlinear Programming 610 (97)
Review of Differential Calculus 610 (6)
Introductory Concepts 616 (14)
Convex and Concave Functions 630 (7)
Solving NLPs with One Variable 637 (12)
Golden Section Search 649 (6)
Unconstrained Maximization and 655 (5)
Minimization with Several Variables
The Method of Steepest Ascent 660 (3)
Lagrange Multipliers 663 (7)
The Kuhn--Tucker Conditions 670 (10)
Quadratic Programming 680 (8)
Separable Programming 688 (5)
The Method of Feasible Directions 693 (2)
Pareto Optimality and Tradeoff Curves 695 (12)
Review of Calculus and Probability 707 (30)
Review of Integral Calculus 707 (3)
Differentiation of Integrals 710 (1)
Basic Rules of Probability 710 (3)
Bayes' Rule 713 (2)
Random Variables, Mean, Variance, and 715 (7)
Covariance
The Normal Distribution 722 (8)
z-Transforms 730 (7)
Decision Making under Uncertainty 737 (66)
Decision Criteria 737 (4)
Utility Theory 741 (14)
Flaws in Expected Maximization of 755 (3)
Utility: Prospect Theory and Framing
Effects
Decision Trees 758 (9)
Bayes' Rule and Decision Trees 767 (6)
Decision Making with Multiple Objectives 773 (12)
The Analytic Hierarchy Process 785 (18)
Game Theory 803 (43)
Two-Person Zero-Sum and Constant-Sum 803 (4)
Games: Saddle Points
Two-Person Zero-Sum Games: Randomized 807 (9)
Strategies, Domination, and Graphical
Solution
Linear Programming and Zero-Sum Games 816 (11)
Two-Person Nonconstant-Sum Games 827 (5)
Introduction to n-Person Game Theory 832 (2)
The Core of an n-Person Game 834 (3)
The Shapley Value 837 (9)
Deterministic EOQ Inventory Models 846 (34)
Introduction to Basic Inventory Models 846 (2)
The Basic Economic Order Quantity Model 848 (11)
Computing the Optimal Order Quantity When 859 (6)
Quantity Discounts Are Allowed
The Continuous Rate EOQ Model 865 (3)
The EOQ Model with Back Orders Allowed 868 (4)
When to Use EOQ Models 872 (1)
Multiple-Product EOQ Models 873 (7)
Probabilistic Inventory Models 880 (43)
Single-Period Decision Models 880 (1)
The Concept of Marginal Analysis 880 (1)
The News Vendor Problem: Discrete Demand 881 (5)
The News Vendor Problem: Continuous Demand 886 (2)
Other One-Period Models 888 (2)
The EOQ with Uncertain Demand: The (r, q) 890 (8)
and (s, S) Models
The EOQ with Uncertain Demand: The 898 (9)
Service Level Approach to Determining
Safety Stock Level
(R, S) Periodic Review Policy 907 (4)
The ABC Inventory Classification System 911 (2)
Exchange Curves 913 (10)
Markov Chains 923 (38)
What Is a Stochastic Process? 923 (1)
What Is a Markov Chain? 924 (4)
n-Step Transition Probabilities 928 (3)
Classification of States in a Markov Chain 931 (3)
Steady-State Probabilities and Mean First 934 (8)
Passage Times
Absorbing Chains 942 (8)
Work-Force Planning Models 950 (11)
Deterministic Dynamic Programming 961 (55)
Two Puzzles 961 (1)
A Network Problem 962 (7)
An Inventory Problem 969 (5)
Resource-Allocation Problems 974 (11)
Equipment-Replacement Problems 985 (4)
Formulating Dynamic Programming Recursions 989 (12)
The Wagner--Whitin Algorithm and the 1001(5)
Silver--Meal Heuristic
Using Excel to Solve Dynamic Programming 1006(10)
Problems
Probabilistic Dynamic Programming 1016(35)
When Current Stage Costs Are Uncertain, 1016(3)
but the Next Period's State Is Certain
A Probabilistic Inventory Model 1019(4)
How to Maximize the Probability of a 1023(6)
Favorable Event Occurring
Further Examples of Probabilistic Dynamic 1029(7)
Programming Formulations
Markov Decision Processes 1036(15)
Queuing Theory 1051(94)
Some Queuing Terminology 1051(2)
Modeling Arrival and Service Processes 1053(1)
Birth--Death Processes 1053(19)
The M/M/1/GD/∞/∞ Queuing 1072(11)
System and the Queuing Formula L =
λW
The M/M/1/GD/c/∞ Queuing System 1083(4)
The M/M/s/GD/∞/∞ Queuing 1087(8)
System
The M/G/∞/GD/∞/∞ and 1095(2)
GI/G/∞/GD/∞/∞ Models
The M/G/∞/GD/∞/∞ 1097(2)
Queuing System
Finite Source Models: The Machine Repair 1099(5)
Model
Exponential Queues in Series and Open 1104(8)
Queuing Networks
The M/G/s/GD/s/∞ System (Blocked 1112(3)
Customers Cleared)
How to Tell Whether Interarrival Times 1115(4)
and Service Times Are Exponential
Closed Queuing Networks 1119(5)
An Approximation for the G/G/m Queuing 1124(2)
System
Priority Queuing Models 1126(5)
Transient Behavior of Queuing Systems 1131(14)
Simulation 1145(46)
Basic Terminology 1145(1)
An Example of a Discrete-Event Simulation 1146(7)
Random Numbers and Monte Carlo Simulation 1153(5)
An Example of Monte Carlo Simulation 1158(4)
Simulations with Continuous Random 1162(11)
Variables
An Example of a Stochastic Simulation 1173(7)
Statistical Analysis in Simulations 1180(3)
Simulation Languages 1183(1)
The Simulation Process 1184(7)
Simulation with Process Model 1191(21)
Simulating an M/M/1 Queuing System 1191(4)
Simulating an M/M/2 System 1195(4)
Simulating a Series System 1199(4)
Simulating Open Queuing Networks 1203(4)
Simulating Erlang Service Times 1207(3)
What Else Can Process Model Do? 1210(2)
Simulation with the Excel Add-in @Risk 1212(63)
Introduction to @Risk: The News Vendor 1212(10)
Problem
Modeling Cash Flows from a New Product 1222(10)
Product Scheduling Models 1232(6)
Reliability and Warranty Modeling 1238(6)
The RISKGENERAL Function 1244(4)
The RISKCUMULATIVE Random Variable 1248(1)
The RISKTRIGEN Random Variable 1249(1)
Creating a Distribution Based on a Point 1250(2)
Forecast
Forecasting the Income of a Major 1252(4)
Corporation
Using Data to Obtain Inputs for New 1256(11)
Product Simulations
Simulation and Bidding 1267(2)
Playing Craps with @Risk 1269(2)
Simulating the NBA Finals 1271(4)
Forecasting Models 1275(61)
Moving-Average Forecast Methods 1275(6)
Simple Exponential Smoothing 1281(2)
Holt's Method: Exponential Smoothing with 1283(3)
Trend
Winter's Method: Exponential Smoothing 1286(6)
with Seasonality
Ad Hoc Forecasting 1292(10)
Simple Linear Regression 1302(10)
Fitting Nonlinear Relationships 1312(5)
Multiple Regression 1317(19)
Appendix 1: @Risk Crib Sheet 1336(14)
Appendix 2: Cases 1350(20)
Case 1 Help, I'm Not Getting Any Younger 1351(1)
Case 2 Solar Energy for Your Home 1351(1)
Case 3 Golf-Sport: Managing Operations 1352(3)
Case 4 Vision Corporation: Production 1355(1)
Planning and Shipping
Case 5 Material Handling in a General 1356(3)
Mail-Handling Facility
Case 6 Selecting Corporate Training 1359(3)
Programs
Case 7 Best Chip: Expansion Strategy 1362(2)
Case 8 Emergency Vehicle Location in 1364(1)
Springfield
Case 9 System Design: Project Management 1365(1)
Case 10 Modular Design for the Help-You 1366(2)
Company
Case 11 Brite Power: Capacity Expansion 1368(2)
Appendix 3: Answers to Selected Problems 1370(32)
Index 1402