Efnisyfirlit

• 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

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