Managerial-Decision-Modeling-with-Spreadsheets

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Cover Title Page Copyright Page ABOUT THE AUTHORS ACKNOWLEDGMENTS CONTENTS PREFACE 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 Summary Glossary Discussion Questions and Problems 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 to Developing a Correct LP Model 2.4 Graphical Solution of a Linear Programming Problem with Two Variables Graphical Representation of Constraints 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 Changing Variable Cells The Objective Cell Constraints 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 Simplex Method Karmarkar’s Algorithm Summary Glossary Solved Problems Discussion Questions and Problems Case Study: Mexicana Wire Winding, Inc. Case Study: Golding Landscaping and Plants, Inc. CHAPTER 3 Linear Programming Modeling Applications with Computer Analyses in Excel 3.1 Introduction 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 Summary Solved Problem Problems Case Study: Chase Manhattan Bank CHAPTER 4 Linear Programming Sensitivity Analysis 4.1 Introduction Why Do We Need to Study 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 Summary Glossary Solved Problem Discussion Questions and Problems Case Study: Coastal States Chemicals and Fertilizers CHAPTER 5 Transportation, Assignment, and Network Models 5.1 Introduction Transportation Model Transshipment Model Assignment Model Maximal-Flow Model Shortest-Path Model Minimal-Spanning Tree Model 5.2 Characteristics of Network Models Types of Arcs Types of Nodes 5.3 Transportation Model LP Formulation for Executive Furniture’s Transportation Model Solving the Transportation Model Using Excel Alternate Excel Layout for the Transportation Model Unbalanced Transportation Models Use of a Dummy Location to Balance an Unbalanced Model 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 Summary Glossary Solved Problems Discussion Questions and Problems Case Study: Old Oregon Wood Store Case Study: Custom Vans Inc. Case Study: Binder’s Beverage CHAPTER 6 Integer, Goal, and Nonlinear Programming Models 6.1 Introduction 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 How Are IP Models Solved? 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 Summary Glossary Solved Problems Discussion Questions and Problems Case Study: Schank Marketing Research Case Study: Oakton River Bridge Case Study: Easley Shopping Center CHAPTER 7 Project Management 7.1 Introduction 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 Using Microsoft Project to Manage Projects Creating a Project Schedule Using Microsoft Project Tracking Progress and Managing Costs Using Microsoft Project Summary Glossary Solved Problems Discussion Questions and Problems Case Study: Haygood Brothers Construction Company Case Study: Family Planning Research Center of Nigeria CHAPTER 8 Decision Analysis 8.1 Introduction 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 Using TreePlan to Solve Decision Tree Problems with Excel Loading TreePlan Creating a Decision Tree Using TreePlan 8.8 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 8.9 Estimating Probability Values Using Bayesian Analysis Calculating Revised Probabilities Potential Problems in Using Survey Results 8.10 Utility Theory Measuring Utility and Constructing a Utility Curve Utility as a Decision-Making Criterion Summary Glossary Solved Problems Discussion Questions and Problems Case Study: Ski Right Case Study: Blake Electronics CHAPTER 9 Queuing Models 9.1 Introduction 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 Single-Server Queuing System with Poisson Arrivals and Exponential Service Times (M/M/1 Model) 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 Multiple-Server Queuing System with Poisson Arrivals and Exponential Service Times (M/M/s Model) Operating Characteristic Equations for an M/M/s Queuing System ArnoldŁfs Muffler Shop Revisited Cost Analysis of the Queuing System 9.6 Single-Server Queuing System with Poisson Arrivals and Constant Service Times (M/D/1 Model) Operating Characteristic Equations for an M/D/1 Queuing System Garcia-Golding Recycling, Inc. Cost Analysis of the Queuing System 9.7 Single-Server Queuing System with Poisson Arrivals and General Service Times (M/G/1 Model) Operating Characteristic Equations for an M/G/1 Queuing System Meetings with Professor Crino Using ExcelŁfs Goal Seek to Identify Required Model Parameters 9.8 Multiple-Server Queuing System with Poisson Arrivals, Exponential Service Times, and Finite Population Size (M/M/S/∞/N Model) Operating Characteristic Equations for the Finite Population Queuing System Department of Commerce Example Cost Analysis of the Queuing System 9.9 More Complex Queuing Systems Summary Glossary Solved Problems Discussion Questions and Problems Case Study: New England Foundry Case Study: Winter Park Hotel CHAPTER 10 Simulation Modeling 10.1 Introduction What Is Simulation? 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 Simulation Model of an Inventory Problem Using Crystal Ball Reasons for Using Add-in Programs Simulation of Simkin’s Hardware Store Using Crystal Ball Replicating the Model Using Decision Table in Crystal Ball 10.9 Simulation Model of a Revenue Management Problem Using Crystal Ball Setting Up the Model 10.10 Other Types of Simulation Models Operational Gaming Systems Simulation Summary Glossary Solved Problems Discussion Questions and Problems Case Study: Alabama Airlines Case Study: Abjar Transport Company CHAPTER 11 Forecasting Models 11.1 Introduction 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) Summary Glossary Solved Problems Discussion Questions and Problems Case Study: North-South Airline Case Study: Forecasting Football Game Attendance at Southwestern University APPENDIX A: Probability Concepts and Applications APPENDIX B: Useful Excel 2010 Commands and Procedures for Installing ExcelModules APPENDIX C: Areas Under the Standard Normal Curve APPENDIX D: Brief Solutions to All Odd-Numbered End-of-Chapter Problems INDEX A B C D E F G H I J K L M N O P Q R S T U V W X Y Z