Spreadsheet Modeling and Decision Analysis

by Ragsdale, Cliff T.

• ISBN13: 9780324183993
• ISBN10: 0324183992
• Edition: CD
• Format: Hardcover
• Copyright: 2003-06-01
• Publisher: Cengage Learning

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