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Operations Research Applications and Algorithms (with CD-ROM and InfoTrac),9780534380588
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Operations Research Applications and Algorithms (with CD-ROM and InfoTrac)

by
Edition:
4th
ISBN13:

9780534380588

ISBN10:
0534380581
Format:
Hardcover
Pub. Date:
7/25/2003
Publisher(s):
Cengage Learning
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Summary

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 Systems of Linear Equations
22(10)
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 Linear Programming Problems
56(7)
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 Multiperiod Decision Problems: An Inventory Model
100(5)
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 Minimization Problems
149(3)
Alternative Optimal Solutions
152(2)
Unbounded LPs
154(4)
The LINDO Computer Package
158(5)
Matrix Generators, LINGO, and Scaling of LPs
163(5)
Degeneracy and the Convergence of the Simplex Algorithm
168(4)
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 Absence of Uncertainty: Goal Programming
191(11)
Using the Excel Solver to Solve LPs
202(25)
Sensitivity Analysis: An Applied Approach
227(35)
A Graphical Introduction to Sensitivity Analysis
227(5)
The Computer and Sensitivity Analysis
232(14)
Managerial Use of Shadow Prices
246(2)
What Happens to the Optimal z-Value If the Current Basis Is No Longer Optimal?
248(14)
Sensitivity Analysis and Duality
262(98)
A Graphical Introduction to Sensitivity Analysis
262(5)
Some Important Formulas
267(8)
Sensitivity Analysis
275(14)
Sensitivity Analysis When More Than One Parameter Is Changed: The 100% Rule
289(6)
Finding the Dual of an LP
295(7)
Economic Interpretation of the Dual Problem
302(2)
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 Transshipment Problems
360(53)
Formulating Transportation Problems
360(13)
Finding Basic Feasible Solutions for Transportation Problems
373(9)
The Transportation Simplex Method
382(8)
Sensitivity Analysis for Transportation Problems
390(3)
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 Pure Integer Programming Problems
512(11)
The Branch-and-Bound Method for Solving Mixed Integer Programming Problems
523(1)
Solving Knapsack Problems by the Branch-and-Bound Method
524(3)
Solving Combinatorial Optimization Problems by the Branch-and-Bound Method
527(13)
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 Large-Scale LPs
570(6)
The Dantzig-Wolfe Decomposition Algorithm
576(17)
The Simplex Method for Upper-Bounded Variables
593(4)
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 Minimization with Several Variables
655(5)
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 Covariance
715(7)
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 Utility: Prospect Theory and Framing Effects
755(3)
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 Games: Saddle Points
803(4)
Two-Person Zero-Sum Games: Randomized Strategies, Domination, and Graphical Solution
807(9)
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 Quantity Discounts Are Allowed
859(6)
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) and (s, S) Models
890(8)
The EOQ with Uncertain Demand: The Service Level Approach to Determining Safety Stock Level
898(9)
(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 Passage Times
934(8)
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 Silver--Meal Heuristic
1001(5)
Using Excel to Solve Dynamic Programming Problems
1006(10)
Probabilistic Dynamic Programming
1016(35)
When Current Stage Costs Are Uncertain, but the Next Period's State Is Certain
1016(3)
A Probabilistic Inventory Model
1019(4)
How to Maximize the Probability of a Favorable Event Occurring
1023(6)
Further Examples of Probabilistic Dynamic Programming Formulations
1029(7)
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 System and the Queuing Formula L = λW
1072(11)
The M/M/1/GD/c/∞ Queuing System
1083(4)
The M/M/s/GD/∞/∞ Queuing System
1087(8)
The M/G/∞/GD/∞/∞ and GI/G/∞/GD/∞/∞ Models
1095(2)
The M/G/∞/GD/∞/∞ Queuing System
1097(2)
Finite Source Models: The Machine Repair Model
1099(5)
Exponential Queues in Series and Open Queuing Networks
1104(8)
The M/G/s/GD/s/∞ System (Blocked Customers Cleared)
1112(3)
How to Tell Whether Interarrival Times and Service Times Are Exponential
1115(4)
Closed Queuing Networks
1119(5)
An Approximation for the G/G/m Queuing System
1124(2)
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 Variables
1162(11)
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 Problem
1212(10)
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 Forecast
1250(2)
Forecasting the Income of a Major Corporation
1252(4)
Using Data to Obtain Inputs for New Product Simulations
1256(11)
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 Trend
1283(3)
Winter's Method: Exponential Smoothing with Seasonality
1286(6)
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 Planning and Shipping
1355(1)
Case 5 Material Handling in a General Mail-Handling Facility
1356(3)
Case 6 Selecting Corporate Training Programs
1359(3)
Case 7 Best Chip: Expansion Strategy
1362(2)
Case 8 Emergency Vehicle Location in Springfield
1364(1)
Case 9 System Design: Project Management
1365(1)
Case 10 Modular Design for the Help-You Company
1366(2)
Case 11 Brite Power: Capacity Expansion
1368(2)
Appendix 3: Answers to Selected Problems
1370(32)
Index 1402


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