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Spreadsheet Modeling and Decision Analysis: A Practical Introduction to Management Science

by
Edition:
4th
ISBN13:

9780324183993

ISBN10:
0324183992
Format:
Hardcover
Pub. Date:
6/1/2003
Publisher(s):
Cengage Learning
List Price: $112.95
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Table of Contents

Introduction to Modeling and Decision Analysis
1(16)
Introduction
1(1)
The Modeling Approach to Decision Making
2(2)
Characteristics and Benefits of Modeling
4(1)
Mathematical Models
5(2)
Categories of Mathematical Models
7(1)
The Problem-Solving Process
8(3)
Anchoring and Framing Effects
11(1)
Good Decisions vs. Good Outcomes
12(1)
Summary
13(1)
References
13(1)
The World of Management Science
14(2)
Questions and Problems
16(1)
Introduction to Optimization and Linear Programming
17(28)
Introduction
17(1)
Applications of Mathematical Optimization
18(1)
Characteristics of Optimization Problems
18(1)
Expressing Optimization Problems Mathematically
19(2)
Decisions
19(1)
Constraints
20(1)
Objective
20(1)
Mathematical Programming Techniques
21(1)
An Example LP Problem
21(1)
Formulating LP Models
22(1)
Steps in Formulating an LP Model
22(1)
Summary of the LP Model for the Example Problem
23(1)
The General Form of an LP Model
24(1)
Solving LP Problems: An Intuitive Approach
25(1)
Solving LP Problems: A Graphical Approach
26(8)
Plotting the First Constraint
26(1)
Plotting the Second Constraint
27(2)
Plotting the Third Constraint
29(1)
The Feasible Region
30(1)
Plotting the Objective Function
30(2)
Finding the Optimal Solution Using Level Curves
32(1)
Finding the Optimal Solution by Enumerating the Corner Points
33(1)
Summary of Graphical Solution to LP Problems
33(1)
Special Conditions in LP Models
34(6)
Alternate Optimal Solutions
35(1)
Redundant Constraints
35(2)
Unbounded Solutions
37(2)
Infeasibility
39(1)
Summary
40(1)
References
40(1)
Questions and Problems
41(4)
Modeling and Solving LP Problems in a Spreadsheet
45(98)
Introduction
45(1)
Spreadsheet Solvers
46(1)
Solving LP Problems in a Spreadsheet
46(1)
The Steps in Implementing an LP Model in a Spreadsheet
47(1)
A Spreadsheet Model for the Blue Ridge Hot Tubs Problem
48(4)
Organizing the Data
48(1)
Representing the Decision Variables
49(1)
Representing the Objective Function
49(1)
Representing the Constraints
50(1)
Representing the Bounds on the Decision Variables
51(1)
How Solver Views the Model
52(2)
Using Solver
54(8)
Defining the Set (or Target) Cell
56(1)
Defining the Variable Cells
57(1)
Defining the Constraint Cells
58(1)
Defining the Nonnegativity Conditions
59(1)
Reviewing the Model
60(1)
Options
60(1)
Solving the Model
60(2)
Goals and Guidelines for Spreadsheet Design
62(2)
Make vs. Buy Decisions
64(5)
Defining the Decision Variables
65(1)
Defining the Objective Function
65(1)
Defining the Constraints
66(1)
Implementing the Model
66(2)
Solving the Model
68(1)
Analyzing the Solution
68(1)
An Investment Problem
69(5)
Defining the Decision Variables
70(1)
Defining the Objective Function
71(1)
Defining the Constraints
71(1)
Implementing the Model
71(2)
Solving the Model
73(1)
Analyzing the Solution
74(1)
A Transportation Problem
74(7)
Defining the Decision Variables
75(1)
Defining the Objective Function
76(1)
Defining the Constraints
76(1)
Implementing the Model
77(2)
Heuristic Solution for the Model
79(1)
Solving the Model
80(1)
Analyzing the Solution
80(1)
A Blending Problem
81(7)
Defining the Decision Variables
82(1)
Defining the Objective Function
82(1)
Defining the Constraints
82(1)
Some Observations About Constraints, Reporting, and Scaling
83(1)
Rescaling the Model
84(1)
Implementing the Model
85(2)
Solving the Model
87(1)
Analyzing the Solution
87(1)
A Production and Inventory Planning Problem
88(6)
Defining the Decision Variables
89(1)
Defining the Objective Function
89(1)
Defining the Constraints
90(1)
Implementing the Model
91(2)
Solving the Model
93(1)
Analyzing the Solution
93(1)
A Multiperiod Cash Flow Problem
94(13)
Defining the Decision Variables
95(1)
Defining the Objective Function
96(1)
Defining the Constraints
96(2)
Implementing the Model
98(3)
Solving the Model
101(1)
Analyzing the Solution
102(1)
Modifying the Taco-Viva Problem to Account for Risk (Optional)
102(2)
Implementing the Risk Constraints
104(2)
Solving the Model
106(1)
Analyzing the Solution
107(1)
Data Envelopment Analysis
107(11)
Defining the Decision Variables
108(1)
Defining the Objective
108(1)
Defining the Constraints
108(1)
Implementing the Model
109(2)
Solving the Model
111(5)
Analyzing the Solution
116(2)
Summary
118(1)
References
118(1)
The World of Management Science
118(1)
Questions and Problems
119(18)
Cases
137(6)
Sensitivity Analysis and the Simplex Method
143(42)
Introduction
143(1)
The Purpose of Sensitivity Analysis
143(1)
Approaches to Sensitivity Analysis
144(1)
An Example Problem
145(1)
The Answer Report
146(1)
The Sensitivity Report
147(12)
Changes in the Objective Function Coefficients
147(2)
A Note About Constancy
149(1)
Alternate Optimal Solutions
150(1)
Changes in the RHS Values
150(1)
Shadow Prices for Nonbinding Constraints
151(1)
A Note About Shadow Prices
151(1)
Shadow Prices and the Value of Additional Resources
152(2)
Other Uses of Shadow Prices
154(1)
The Meaning of the Reduced Costs
154(3)
Analyzing Changes in Constraint Coefficients
157(1)
Simultaneous Changes in Objective Function Coefficients
158(1)
A Warning About Degeneracy
159(1)
The Limits Report
159(1)
The Sensitivity Assistant Add-In (Optional)
160(7)
Creating Spider Tables and Plots
161(3)
Creating a Solver Table
164(3)
Comments
167(1)
The Simplex Method (Optional)
167(4)
Creating Equality Constraints Using Slack Variables
167(1)
Basic Feasible Solution
168(2)
Finding the Best Solution
170(1)
Summary
171(1)
References
171(1)
The World of Management Science
172(1)
Questions and Problems
173(8)
Cases
181(4)
Network Modeling
185(59)
Introduction
185(1)
The Transshipment Problem
185(7)
Characteristics of Network Flow Problems
186(1)
The Decision Variables for Network Flow Problems
187(1)
The Objective Function for Network Flow Problems
188(1)
The Constraints for Network Flow Problems
188(2)
Implementing the Model in a Spreadsheet
190(1)
Analyzing the Solution
191(1)
The Shortest Path Problem
192(5)
An LP Model for the Example Problem
194(1)
The Spreadsheet Model and Solution
195(2)
Network Flow Models and Integer Solutions
197(1)
The Equipment Replacement Problem
197(4)
The Spreadsheet Model and Solution
199(2)
Transportation/Assignment Problems
201(2)
Generalized Network Flow Problems
203(7)
Formulating an LP Model for the Recyling Problem
204(1)
Implementing the Model
205(2)
Analyzing the Solution
207(2)
Generalized Network Flow Problems and Feasibility
209(1)
Maximal Flow Problems
210(5)
An Example of a Maximal Flow Problem
211(1)
The Spreadsheet Model and Solution
212(3)
Special Modeling Considerations
215(3)
Minimal Spanning Tree Problems
218(2)
An Algorithm for the Minimal Spanning Tree Problem
219(1)
Solving the Example Problem
219(1)
Summary
220(1)
References
221(1)
The World of Management Science
221(1)
Questions and Problems
222(14)
Cases
236(8)
Integer Linear Programming
244(69)
Introduction
244(1)
Integrality Conditions
244(1)
Relaxation
245(1)
Solving the Relaxed Problem
246(2)
Bounds
248(1)
Rounding
249(2)
Stopping Rules
251(2)
Solving ILP Problems Using Solver
253(2)
Other ILP Problems
255(2)
An Employee Scheduling Problem
257(5)
Defining the Decision Variables
258(1)
Defining the Objective Function
258(1)
Defining the Constraints
258(1)
A Note About the Constraints
259(1)
Implementing the Model
260(1)
Solving the Model
260(1)
Analyzing the Solution
261(1)
Binary Variables
262(1)
A Capital Budgeting Problem
262(6)
Defining the Decision Variables
263(1)
Defining the Objective Function
263(1)
Defining the Constraints
264(1)
Setting Up the Binary Variables
264(1)
Implementing the Model
264(1)
Solving the Model
265(1)
Comparing the Optimal Solution to a Heuristic Solution
266(2)
Binary Variables and Logical Conditions
268(1)
The Fixed-Charge Problem
268(8)
Defining the Decision Variables
269(1)
Defining the Objective Function
270(1)
Defining the Constraints
270(1)
Determining Values for ``Big M''
271(1)
Implementing the Model
271(2)
Solving the Model
273(1)
Analyzing the Solution
274(2)
Minimum Order/Purchase Size
276(1)
Quantity Discounts
276(2)
Formulating the Model
277(1)
The Missing Constraints
277(1)
A Contract Award Problem
278(6)
Formulating the Model: The Objective Function and Transportation Constraints
279(1)
Implementing the Transportation Constraints
279(1)
Formulating the Model: The Side Constraints
280(1)
Implementing the Side Constraints
281(2)
Solving the Model
283(1)
Analyzing the Solution
283(1)
The Branch-and-Bound Algorithm (Optional)
284(6)
Branching
285(2)
Bounding
287(1)
Branching Again
288(1)
Bounding Again
288(1)
Summary of B&B Example
289(1)
Summary
290(2)
References
292(1)
The World of Management Science
292(1)
Questions and Problems
293(13)
Cases
306(7)
Goal Programming and Multiple Objective Optimization
313(41)
Introduction
313(1)
Goal Programming
314(1)
A Goal Programming Example
314(10)
Defining the Decision Variables
315(1)
Defining the Goals
315(1)
Defining the Goal Constraints
315(1)
Defining the Hard Constraints
316(1)
GP Objective Functions
317(1)
Defining the Objective
318(1)
Implementing the Model
319(2)
Solving the Model
321(1)
Analyzing the Solution
321(1)
Revising the Model
321(1)
Trade-offs: The Nature of GP
322(2)
Comments About Goal Programming
324(1)
Multiple Objective Optimization
325(1)
An MOLP Example
326(11)
Defining the Decisions Variables
326(1)
Defining the Objectives
327(1)
Defining the Constraints
327(1)
Implementing the Model
327(1)
Determining Target Values for the Objectives
328(3)
Summarizing the Target Solutions
331(1)
Determing a GP Objective
332(1)
The MINIMAX Objective
333(1)
Implementing the Revised Model
334(1)
Solving the Model
335(2)
Comments on MOLP
337(1)
Summary
338(1)
References
339(1)
The World of Management Science
339(1)
Questions and Problems
340(11)
Cases
351(3)
Nonlinear Programming and Evolutionary Optimization
354(75)
Introduction
354(1)
The Nature of NLP Problems
355(1)
Solution Strategies for NLP Problems
356(1)
Local vs. Global Optimal Solutions
357(3)
Economic Order Quantity Models
360(5)
Implementing the Model
362(1)
Solving the Model
363(1)
Analyzing the Solution
363(2)
Comments on the EOQ Model
365(1)
Location Problems
365(6)
Defining the Decision Variables
367(1)
Defining the Objective
367(1)
Defining the Constraints
367(1)
Implementing the Model
368(1)
Solving the Model and Analyzing the Solution
368(2)
Another Solution to the Problem
370(1)
Some Comments About the Solution to Location problems
371(1)
Nonlinear Network Flow Problem
371(5)
Defining the Decision Variables
372(1)
Defining the Objectives
373(1)
Defining the Constraints
373(1)
Implementing the Model
374(1)
Solving the Model and Analyzing the Solution
374(2)
Project Selection Problems
376(6)
Defining the Decision Variables
378(1)
Defining the Objective Function
378(1)
Defining the Constraints
378(1)
Implementing the Model
379(2)
Solving the Model
381(1)
Optimizing Existing Financial Spreadsheet Models
382(4)
Implementing the Model
383(1)
Optimizing the Spreadsheet Model
384(1)
Analyzing the Solution
384(1)
Comments on Optimizing Existing Spreadsheets
385(1)
The Portfolio Selection Problem
386(8)
Defining the Decision Variables
387(1)
Defining the Objective
388(1)
Defining the Constraints
388(1)
Implementing the Model
389(2)
Analyzing the Solution
391(1)
Handling Conflicting Objectives in Portfolio Problems
392(2)
Sensitivity Analysis
394(4)
Lagrange Multipliers
397(1)
Reduced Gradients
397(1)
Solver Options for Solving NLPs
398(1)
Evolutionary Algorithms
399(2)
Beating the Market
401(2)
A Spreadsheet Model for the Problem
401(1)
Solving the Model
401(1)
Analyzing the Solution
402(1)
The Traveling Salesperson Problem
403(5)
A Spreadsheet Model for the Problem
404(2)
Solving the Model
406(1)
Analyzing the Solution
407(1)
Summary
408(1)
References
408(1)
The World of Management Science
409(1)
Questions and Problems
409(15)
Cases
424(5)
Regression Analysis
429(56)
Introduction
429(1)
An Example
429(3)
Regression Models
432(1)
Simple Linear Regression Analysis
433(1)
Defining ``Best Fit''
434(1)
Solving the Problem Using Solver
435(2)
Solving the Problem Using the Regression Tool
437(2)
Evaluating the Fit
439(3)
The R2 Statistic
442(2)
Making Predictions
444(4)
The Standard Error
444(1)
Prediction Intervals for New Values of Y
444(3)
Confidence Intervals for Mean Values of Y
447(1)
A Note About Extrapolation
447(1)
Statistical Tests for Population Parameters
448(3)
Analysis of Variance
448(1)
Assumptions for the Statistical Tests
449(2)
A Note About Statistical Tests
451(1)
Introduction to Multiple Regression
451(2)
A Multiple Regression Example
453(1)
Selecting the Model
453(8)
Model with One Independent Variable
455(1)
Models with Two Independent Variables
456(2)
Inflating R2
458(1)
The Adjusted-R2 Statistics
458(1)
The Best Model with Two Independent Variables
459(1)
Multicollinearity
459(1)
The Model with Three Independent Variables
460(1)
Making Predictions
461(1)
Binary Independent Variables
462(1)
Statistical Tests for the Population Parameters
463(1)
Polynomial Regression
463(6)
Expressing Nonlinear Relatinships Using Linear Models
465(3)
Summary of Nonlinear Regression
468(1)
Summary
469(1)
References
470(1)
The World of Management Science
470(1)
Questions and Problems
471(11)
Cases
482(3)
Discriminant Analysis
485(30)
Introduction
485(1)
The Two-Group DA Problem
486(11)
Group Locations and Centroids
487(2)
Calculating Discriminant Scores
489(2)
The Classification Rule
491(2)
Refining the Cutoff Value
493(1)
Classification Accuracy
494(1)
Classifying New Employees
495(2)
The k-Group DA Problem
497(7)
Multiple Discriminant Analysis
498(2)
Distance Measures
500(2)
MDA Classification
502(2)
Summary
504(1)
References
505(1)
The World of Management Science
505(1)
Questions and Problems
506(5)
Cases
511(4)
Time Series Forecasting
515(80)
Introduction
515(1)
Time Series Methods
516(1)
Measuring Accuracy
517(1)
Stationary Models
517(1)
Moving Averages
518(4)
Forecasting with the Moving Average Model
521(1)
Weighted Moving Averages
522(3)
Forecasting with the Weighted Moving Average Model
524(1)
Exponential Smoothing
525(4)
Forecasting with the Exponential Smoothing Model
528(1)
Seasonality
529(1)
Stationary Data with Additive Seasonal Effects
529(5)
Forecasting with the Model
534(1)
Stationary Data with Multiplicative Seasonal Effects
534(4)
Forecasting with the Model
537(1)
Trend Models
538(1)
An Example
538(1)
Double Moving Average
539(3)
Forecasting with the Model
541(1)
Double Exponential Smoothing (Holt's Method)
542(3)
Forecasting with Holt's Method
544(1)
Holt-Winter's Method for Additive Seasonal Effects
545(5)
Forecasting with Holt-Winter's Additive Method
549(1)
Holt-Winter's Method for Multiplicative Seasonal Effects
550(4)
Forecasting with Holt-Winter's Multiplicative Method
554(1)
Modeling Time Series Trends Using Regression
554(1)
Linear Trend Model
554(4)
Forecasting with the Linear Trend Model
555(3)
Quadratic Trend Model
558(2)
Forecasting with the Quadratic Trend Model
559(1)
Modeling Seasonality with Regression Models
560(1)
Adjusting Trend Predictions with Seasonal Indices
561(5)
Computing Seasonal Indices
562(1)
Forecasting with Seasonal Indices
563(1)
Refining the Seasonal Indices
564(2)
Seasonal Regression Models
566(5)
The Seasonal Model
567(3)
Forecasting with the Seasonal Regression Model
570(1)
Crystal Ball Predictor
571(5)
Using CB Predictor
571(5)
Combining Forecasts
576(1)
Summary
577(1)
References
577(1)
The World of Management Science
578(1)
Questions and Problems
579(10)
Cases
589(6)
Introduction to Simulation Using Crystal Ball
595(73)
Introduction
595(1)
Random Variables and Risk
596(1)
Why Analyze Risk?
596(1)
Methods of Risk Analysis
597(2)
Best-Case/Worst-Case Analysis
597(1)
What-If Analysis
598(1)
Simulation
599(1)
A Corporate Health Insurance Example
599(3)
A Critique of the Base Case Model
601(1)
Spreadsheet Simulation Using Crystal Ball
602(1)
Starting Crystal Ball
602(1)
Random Number Generators
603(4)
Discrete vs. Continuos Random Variables
606(1)
Preparing the Model for Simulation
607(3)
Alternate RNG Entry
609(1)
Running the Simulation
610(3)
Selecting the Output Cells to Track
610(1)
Selecting the Number of Iterations
611(1)
Determining the Sample Size
612(1)
Running the Simulation
612(1)
Data Analysis
613(4)
The Best Case and the Worst Case
613(1)
Viewing the Distribution of the Output Cells
614(1)
Viewing the Cumulative Distribution of the Output Cells
615(1)
Obtaining Other Cumulative Probabilities
616(1)
Incorporating Graphs and Statistics into a Spreadsheet
617(1)
The Uncertainty of Sampling
618(3)
Constructing a Confidence Interval for the True Population Mean
618(2)
Constructing a Confidence Interval for a Population Proportion
620(1)
Sample Sizes and Confidence Interval Widths
621(1)
The Benefits of Simulation
621(1)
Additional Uses of Simulation
621(1)
A Reservation Management Example
622(7)
Implementing the Model
623(2)
Using the Decision Table Tool
625(4)
An Inventory Control Example
629(9)
Creating the RNGs
630(1)
Implementing the Model
631(3)
Replicating the Model
634(1)
Optimizing the Model
634(3)
Comment on Using OptQuest
637(1)
A Project Selection Example
638(8)
A Spreadsheet Model
639(1)
Solving the Problem with OptQuest
640(2)
Considering Other Solutions
642(4)
Summary
646(1)
References
646(1)
The World of Management Science
647(1)
Questions and Problems
647(13)
Cases
660(8)
Queuing Theory
668(35)
Introduction
668(1)
The Purpose of Queuing Models
669(1)
Queuing System Configurations
670(1)
Characteristics of Queuing Systems
671(4)
Arrival Rate
671(2)
Service Rate
673(2)
Kendall Notation
675(1)
Queuing Models
675(2)
The M/M/s Model
677(3)
An Example
677(1)
The Current Situation
677(1)
Adding a Server
678(2)
Economic Analysis
680(1)
The M/M/s Model with Finite Queue Length
680(2)
The Current Situation
681(1)
Adding a Server
682(1)
The M/M/s Model with Finite Population
682(4)
An Example
684(1)
The Current Situation
684(2)
Adding Servers
686(1)
The M/G/1 Model
686(4)
The Current Situation
689(1)
Adding the Automated Dispensing Device
689(1)
The M/D/1 Model
690(2)
Simulating Queues and the Steady-State Assumption
692(1)
Summary
693(1)
References
693(1)
The World of Management Science
694(1)
Questions and Problems
695(6)
Cases
701(2)
Project Management
703(51)
Introduction
703(1)
An Example
704(1)
Creating the Project Network
704(3)
A Note on Start and Finish Points
706(1)
CPM: An Overview
707(1)
The Forward Pass
708(3)
The Backward Pass
711(2)
Determining the Critical Path
713(2)
A Note on Slack
714(1)
Project Management Using Spreadsheets
715(6)
Project Crashing
721(8)
An LP Approach to Crashing
722(1)
Determining the Earliest Crash Completion Time
723(2)
Implementing the Model
725(1)
Solving the Model
726(1)
Determining a Least Costly Crash Schedule
727(1)
Crashing as an MOLP
727(2)
Certainty vs. Uncertainty
729(1)
PERT: An Overview
730(3)
The Problems with PERT
731(2)
Implications
733(1)
Simulating Project Networks
733(5)
An Example
733(1)
Generating Random Activity Times
733(2)
Implementing the Model
735(1)
Running the Simulation
735(2)
Analyzing the Results
737(1)
Microsoft Project
738(3)
Summary
741(1)
References
742(1)
The World of Management Science
742(1)
Questions and Problems
743(8)
Cases
751(3)
Decision Analysis
754(79)
Introduction
754(1)
Good Decisions vs. Good Outcomes
755(1)
Characteristics of Decision Problems
755(1)
An Example
756(1)
The Payoff Matrix
757(2)
Decision Alternatives
757(1)
States of Nature
757(1)
The Payoff Values
758(1)
Decision Rules
759(1)
Nonprobabilistic Methods
760(4)
The Maximax Decision Rule
760(1)
The Maximin Decision Rule
761(1)
The Minimax Regret Decision Rule
761(3)
Probabilistic Methods
764(6)
Expected Monetary Value
765(1)
Expected Regret
766(2)
Sensitivity Analysis
768(2)
The Expected Value of Perfect Information
770(1)
Decision Trees
771(4)
Rolling Back a Decision Tree
773(2)
Using TreePlan
775(9)
Adding Branches
775(1)
Adding Event Nodes
776(4)
Adding the Cash Flows
780(2)
Determining the Payoffs and EMVs
782(1)
Other Features
782(2)
Multistage Decision Problems
784(2)
A Multistage Decision Tree
785(1)
Analyzing Risk in a Decision Tree
786(5)
Risk Profiles
788(1)
Strategy Tables
789(2)
Using Sample Information in Decision Making
791(3)
Conditional Probabilities
793(1)
The Expected Value of Sample Information
793(1)
Computing Conditional Probabilities
794(3)
Bayes's Theorem
796(1)
Utility Theory
797(8)
Utility Functions
798(1)
Constructing Utility Functions
799(2)
Using Utilities to Make Decisions
801(1)
The Exponential Utility Function
802(1)
Incorporating Utilities in TreePlan
803(2)
Multicriteria Decision Making
805(1)
The Multicriteria Scoring Model
806(3)
The Analytic Hierarchy Process
809(8)
Pairwise Comparisons
809(1)
Normalizing the Comparisons
810(1)
Consistency
811(3)
Obtaining Scores for the Remaining Criteria
814(1)
Obtaining Criterion Weights
814(3)
Implementing the Scoring Model
817(1)
Summary
817(1)
References
817(1)
The World of Management Science
818(1)
Questions and Problems
819(11)
Cases
830(3)
Index 833


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