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• Cover
• Title Page
• Copyright
• Dedication
• Acknowledgments
• About the Authors
• Contents
• Chapter 1: Introduction to Managerial Decision Modeling
• 1.1 What is Decision Modeling?
• 1.2 Types of Decision Models
• Deterministic Models
• Probabilistic Models
• Quantitative versus Qualitative Data
• Using Spreadsheets in Decision Modeling
• 1.3 Steps Involved in Decision Modeling
• Step 1: Formulation
• Step 2: Solution
• Step 3: Interpretation and Sensitivity Analysis
• 1.4 Spreadsheet Example of a Decision Model: Tax Computation
• 1.5 Spreadsheet Example of a Decision Model: Break-Even Analysis
• Using Goal Seek to Find the Break-Even Point
• 1.6 Possible Problems in Developing Decision Models
• Defining the Problem
• Developing a Model
• Acquiring Input Data
• Developing a Solution
• Testing the Solution
• Analyzing the Results
• 1.7 Implementation–Not Just the Final Step
• 1.8 Summary
• 1.9 Exercises
• Chapter 2: Linear Programming Models: Graphical and Computer Methods
• 2.1 Introduction
• 2.2 Developing a Linear Programming Model
• Formulation
• Solution
• Interpretation and Sensitivity Analysis
• Properties of a Linear Programming Model
• Basic Assumptions of a Linear Programming Model
• 2.3 Formulating a Linear Programming Problem
• Linear Programming Example: Flair Furniture Company
• Decision Variables
• The Objective Function
• Constraints
• Nonnegativity Constraints and Integer Values
• Guidelines for Developing a Correct LP Model
• 2.4 Graphical Solution of a Linear Programming Problem with Two Variables
• Graphical Representation of Constraints
• Painting Time Constraint
• Feasible Region
• Identifying an Optimal Solution by Using Level Lines
• Identifying an Optimal Solution by Using All Corner Points
• Comments on Flair Furniture’s Optimal Solution
• Extension to Flair Furniture’s LP Model
• 2.5 A Minimization Linear Programming Problem
• Holiday Meal Turkey Ranch
• Graphical Solution of the Holiday Meal Turkey Ranch Problem
• 2.6 Special Situations in Solving Linear Programming Problems
• Redundant Constraints
• Infeasibility
• Alternate Optimal Solutions
• Unbounded Solution
• 2.7 Setting Up and Solving Linear Programming Problems Using Excel’s Solver
• Using Solver to Solve the Flair Furniture Problem
• The Objective Cell
• Creating Cells for Constraint RHS Values
• Entering Information in Solver
• Using Solver to Solve Flair Furniture Company’s Modified Problem
• Using Solver to Solve the Holiday Meal Turkey Ranch Problem
• 2.8 Algorithmic Solution Procedures for Linear Programming Problems
• 2.9 Summary
• 2.10 Exercises
• Chapter 3: Linear Programming Modeling Applications with Computer Analyses in Excel
• 3.1 Using Linear Programming to Solve Real-World Problems
• 3.2 Manufacturing Applications
• Product Mix Problem
• Make-Buy Decision Problem
• 3.3 Marketing Applications
• Media Selection Problem
• Marketing Research Problem
• 3.4 Finance Applications
• Portfolio Selection Problem
• Alternate Formulations of the Portfolio Selection Problem
• 3.5 Employee Staffing Applications
• Labor Planning Problem
• Extensions to the Labor Planning Problem
• Assignment Problem
• 3.6 Transportation Applications
• Vehicle Loading Problem
• Expanded Vehicle Loading Problem–Allocation Problem
• Transportation Problem
• 3.7 Blending Applications
• Diet Problem
• Blending Problem
• 3.8 Multiperiod Applications
• Production Scheduling Problem
• Sinking Fund Problem
• 3.9 Summary
• 3.10 Exercises
• Chapter 4: Linear Programming Sensitivity Analysis
• 4.1 Importance of Sensitivity Analysis
• Why Do We Need Sensitivity Analysis?
• 4.2 Sensitivity Analysis Using Graphs
• Types of Sensitivity Analysis
• Impact of Changes in an Objective Function Coefficient
• Impact of Changes in a Constraint’s Right-Hand-Side Value
• 4.3 Sensitivity Analysis Using Solver Reports
• Solver Reports
• Sensitivity Report
• Impact of Changes in a Constraint’s RHS Value
• Impact of Changes in an Objective Function Coefficient
• 4.4 Sensitivity Analysis for a Larger Maximization Example
• Anderson Home Electronics Example
• Some Questions We Want Answered
• Alternate Optimal Solutions
• 4.5 Analyzing Simultaneous Changes by Using the 100% Rule
• Simultaneous Changes in Constraint RHS Values
• Simultaneous Changes in OFC Values
• 4.6 Pricing Out New Variables
• Anderson’s Proposed New Product
• 4.7 Sensitivity Analysis for a Minimization Example
• Burn-Off Diet Drink Example
• Burn-Off’s Excel Solution
• Answering Sensitivity Analysis Questions for Burn-Off
• 4.8 Summary
• 4.9 Exercises
• Chapter 5: Transportation, Assignment, and Network Models
• 5.1 Types of Network Models
• Transportation Model
• Transshipment Model
• Assignment Model
• Maximal-Flow Model
• Shortest-Path Model
• Minimal-Spanning Tree Model
• Implementation Issues
• 5.2 Characteristics of Network Models
• Types of Arcs
• Types of Nodes
• Common Characteristics
• 5.3 Transportation Model
• LP Formulation for Executive Furniture’s Transportation Model
• Solving the Transportation Model Using Excel
• Unbalanced Transportation Models
• Alternate Optimal Solutions
• An Application of the Transportation Model: Facility Location
• 5.4 Transportation Models with Max-Min and Min-Max Objectives
• 5.5 Transshipment Model
• Executive Furniture Company Example–Revisited
• LP Formulation for Executive Furniture’s Transshipment Model
• Lopez Custom Outfits–A Larger Transshipment Example
• LP Formulation for Lopez Custom Outfits Transshipment Model
• 5.6 Assignment Model
• Fix-It Shop Example
• Solving Assignment Models
• LP Formulation for Fix-It Shop’s Assignment Model
• 5.7 Maximal-Flow Model
• Road System in Waukesha, Wisconsin
• LP Formulation for Waukesha Road System’s Maximal-Flow Model
• 5.8 Shortest-Path Model
• Ray Design Inc. Example
• LP Formulation for Ray Design Inc.’s Shortest-Path Model
• 5.9 Minimal-Spanning Tree Model
• Lauderdale Construction Company Example
• 5.10 Summary
• 5.11 Exercises
• Chapter 6: Integer, Goal, and Nonlinear Programming Models
• 6.1 Models That Relax Linear Programming Conditions
• Integer Programming Models
• Goal Programming Models
• Nonlinear Programming Models
• 6.2 Models with General Integer Variables
• Harrison Electric Company
• Using Solver to Solve Models with General Integer Variables
• Solver Options
• Should We Include Integer Requirements in a Model?
• 6.3 Models with Binary Variables
• Portfolio Selection at Simkin and Steinberg
• Set-Covering Problem at Sussex County
• 6.4 Mixed Integer Models: Fixed-Charge Problems
• Locating a New Factory for Hardgrave Machine Company
• 6.5 Goal Programming Models
• Goal Programming Example: Wilson Doors Company
• Solving Goal Programming Models with Weighted Goals
• Solving Goal Programming Models with Ranked Goals
• Comparing the Two Approaches for Solving GP Models
• 6.6 Nonlinear Programming Models
• Why Are NLP Models Difficult to Solve?
• Solving Nonlinear Programming Models Using Solver
• Computational Procedures for Nonlinear Programming Problems
• 6.7 Summary
• 6.8 Exercises
• Chapter 7: Project Management
• 7.1 Planning, Scheduling, and Controlling Projects
• Phases in Project Management
• Use of Software Packages in Project Management
• 7.2 Project Networks
• Identifying Activities
• Identifying Activity Times and Other Resources
• Project Management Techniques: PERT and CPM
• Project Management Example: General Foundry, Inc.
• Drawing the Project Network
• 7.3 Determining the Project Schedule
• Forward Pass
• Backward Pass
• Calculating Slack Time and Identifying the Critical Path(s)
• Total Slack Time versus Free Slack Time
• 7.4 Variability in Activity Times
• PERT Analysis
• Probability of Project Completion
• Determining Project Completion Time for a Given Probability
• Variability in Completion Time of Noncritical Paths
• 7.5 Managing Project Costs and Other Resources
• Planning and Scheduling Project Costs: Budgeting Process
• Monitoring and Controlling Project Costs
• Managing Other Resources
• 7.6 Project Crashing
• Crashing General Foundry’s Project (Hand Calculations)
• Crashing General Foundry’s Project Using Linear Programming
• Using Linear Programming to Determine Earliest and Latest Starting Times
• 7.7 Summary
• 7.8 Exercises
• Chapter 8: Decision Analysis
• 8.1 What is Decision Analysis?
• 8.2 The Five Steps in Decision Analysis
• Thompson Lumber Company Example
• 8.3 Types of Decision-Making Environments
• 8.4 Decision Making Under Uncertainty
• Maximax Criterion
• Maximin Criterion
• Criterion of Realism (Hurwicz)
• Equally Likely (Laplace) Criterion
• Minimax Regret Criterion
• Using Excel to Solve Decision-Making Problems under Uncertainty
• 8.5 Decision Making Under Risk
• Expected Monetary Value
• Expected Opportunity Loss
• Expected Value of Perfect Information
• Using Excel to Solve Decision-Making Problems under Risk
• 8.6 Decision Trees
• Folding Back a Decision Tree
• 8.7 Decision Trees for Multistage Decision-Making Problems
• A Multistage Decision-Making Problem for Thompson Lumber
• Expanded Decision Tree for Thompson Lumber
• Folding Back the Expanded Decision Tree for Thompson Lumber
• Expected Value of Sample Information
• 8.8 Estimating Probability Values Using Bayesian Analysis
• Calculating Revised Probabilities
• Potential Problems in Using Survey Results
• 8.9 Utility Theory
• Measuring Utility and Constructing a Utility Curve
• Utility as a Decision-Making Criterion
• 8.10 Summary
• 8.11 Exercises
• Chapter 9: Queuing Models
• 9.1 The Importance of Queuing Theory
• Approaches for Analyzing Queues
• 9.2 Queuing System Costs
• 9.3 Characteristics of a Queuing System
• Arrival Characteristics
• Queue Characteristics
• Service Facility Characteristics
• Measuring the Queue’s Performance
• Kendall’s Notation for Queuing Systems
• Variety of Queuing Models Studied Here
• 9.4 M/M/1 Queuing System
• Assumptions of the M/M/1 Queuing Model
• Operating Characteristic Equations for an M/M/1 Queuing System
• Arnold’s Muffler Shop Example
• Using ExcelModules for Queuing Model Computations
• Cost Analysis of the Queuing System
• Increasing the Service Rate
• 9.5 M/M/s Queuing System
• Operating Characteristic Equations for an M/M/s Queuing System
• Arnold’s Muffler Shop Revisited
• Cost Analysis of the Queuing System
• 9.6 M/D/1 Queuing System
• Operating Characteristic Equations for an M/D/1 Queuing System
• Garcia-Golding Recycling, Inc.
• Cost Analysis of the Queuing System
• 9.7 M/G/1 Queuing System
• Operating Characteristic Equations for an M/G/1 Queuing System
• Meetings with Professor Crino
• Using Excel’s Goal Seek to Identify Required Model Parameters
• 9.8 M/M/S/∞/N Queuing System
• Operating Characteristic Equations for the Finite Population QueuingSystem
• Department of Commerce Example
• Cost Analysis of the Queuing System
• 9.9 More Complex Queuing Systems
• 9.10 Summary
• 9.11 Exercises
• Chapter 10: Simulation Modeling
• 10.1 Why Create a Simulation?
• Simulation Basics
• Advantages and Disadvantages of Simulation
• 10.2 Monte Carlo Simulation
• Step 1: Establish a Probability Distribution for Each Variable
• Step 2: Simulate Values from the Probability Distributions
• Step 3: Repeat the Process for a Series of Replications
• 10.3 Role of Computers in Simulation
• Types of Simulation Software Packages
• Random Generation from Some Common Probability Distributions Using Excel
• 10.4 Simulation Model to Compute Expected Profit
• Setting Up the Model
• Replication by Copying the Model
• Replication Using Data Table
• Analyzing the Results
• 10.5 Simulation Model of an Inventory Problem
• Simkin’s Hardware Store
• Setting Up the Model
• Computation of Costs
• Replication Using Data Table
• Analyzing the Results
• Using Scenario Manager to Include Decisions in a Simulation Model
• Analyzing the Results
• 10.6 Simulation Model of a Queuing Problem
• Denton Savings Bank
• Setting Up the Model
• Replication Using Data Table
• Analyzing the Results
• 10.7 Simulation Model of a Revenue Management Problem
• Judith’s Airport Limousine Service
• Setting Up the Model
• Replicating the Model Using Data Table and Scenario Manager
• Analyzing the Results
• 10.8 Other Types of Simulation Models
• Operational Gaming
• Systems Simulation
• 10.9 Summary
• 10.10 Exercises
• Chapter 11: Forecasting Models
• 11.1 What is Forecasting?
• 11.2 Types of Forecasts
• Qualitative Models
• Time-Series Models
• Causal Models
• 11.3 Qualitative Forecasting Models
• 11.4 Measuring Forecast Error
• 11.5 Basic Time-Series Forecasting Models
• Components of a Time Series
• Stationary and Nonstationary Time-Series Data
• Moving Averages
• Using ExcelModules for Forecasting Model Computations
• Weighted Moving Averages
• Exponential Smoothing
• 11.6 Trend and Seasonality in Time-Series Data
• Linear Trend Analysis
• Scatter Chart
• Least-Squares Procedure for Developing a Linear Trend Line
• Seasonality Analysis
• 11.7 Decomposition of a Time Series
• Multiplicative Decomposition Example: Sawyer Piano House
• Using ExcelModules for Multiplicative Decomposition
• 11.8 Causal Forecasting Models: Simple and Multiple Regression
• Causal Simple Regression Model
• Causal Simple Regression Using ExcelModules
• Causal Simple Regression Using Excel’s Analysis ToolPak (Data Analysis)
• Causal Multiple Regression Model
• Causal Multiple Regression Using ExcelModules
• Causal Multiple Regression Using Excel’s Analysis ToolPak (Data Analysis)
• 11.9 Summary
• 11.10 Exercises
• Appendix A: Probability Concepts and Applications
• A.1 Fundamental Concepts
• Types of Probability
• A.2 Mutually Exclusive and Collectively Exhaustive Events
• Adding Mutually Exclusive Events
• Law of Addition for Events that Are Not Mutually Exclusive
• A.3 Statistically Independent Events
• A.4 Statistically Dependent Events
• A.5 Revising Probabilities with Bayes’ Theorem
• General Form of Bayes’ Theorem
• A.6 Further Probability Revisions
• A.7 Random Variables
• A.8 Probability Distributions
• Probability Distribution of a Discrete Random Variable
• Expected Value of a Discrete Probability Distribution
• Variance of a Discrete Probability Distribution
• Probability Distribution of a Continuous Random Variable
• A.9 The Normal Distribution
• Area under the Normal Curve
• Using the Standard Normal Table
• Haynes Construction Company Example
• A.10 The Exponential Distribution
• A.11 The Poisson Distribution
• A.12 Summary
• A.13 Exercises
• Appendix B: Useful Excel 2016 Commands and Procedures for Installing ExcelModules
• 1B.1 Introduction
• B.2 Getting Started
• Organization of a Worksheet
• Navigating through a Worksheet
• B.3 The Ribbon, Toolbars, and Tabs
• Excel Help
• B.4 Working with Worksheets
• B.5 Using Formulas and Functions
• Copying Formulas
• Errors in Using Formulas and Functions
• B.6 Printing Worksheets
• B.7 Excel Options and Add-Ins
• B.8 ExcelModules
• Installing ExcelModules
• Running ExcelModules
• ExcelModules Help and Options
• Appendix C: Areas Under The Standard Normal Curve
• Appendix D: Brief Solutions to All Odd-Numbered End-Of-Chapter Problems
• Index