An Introduction to Management Science Quantitative Approaches to Decision Making

Description

Equip students with a conceptual understanding of management science's role in the decision-making process with Camm/Cochran/Fry/Ohlmann/Anderson/Sweeney/Williams' market-leading AN INTRODUCTION TO MANAGEMENT SCIENCE: QUANTITATIVE APPROACHES TO DECISION MAKING, 16E. This edition uses a hallmark problem-scenario approach in a new full-color design as the authors introduce each quantitative technique within an application setting. Students learn to apply the management science model to generate solutions and make recommendations for management. Updates clarify concept explanations while new vignettes and problems demonstrate concepts at work. Mathematical methods are presented using graphical solutions with chapter appendices that show the steps for using Microsoft® Office Excel® 365. In addition, WebAssign courseware puts techniques and models into practice with randomized problems from the book and instant feedback as well as problem walk-throughs and step-by-step tutorials. Cover Brief Contents Contents Preface About the Authors Chapter 1: Introduction 1.1 Problem Solving and Decision Making 1.2 Quantitative Analysis and Decision Making 1.3 Quantitative Analysis 1.4 Models of Cost, Revenue, and Profit 1.5 Management Science Techniques Summary Glossary Problems Case Problem: Scheduling a Youth Soccer League Appendix 1.1: Using Excel for Breakeven Analysis Chapter 2: An Introduction to Linear Programming 2.1 A Simple Maximization Problem 2.2 Graphical Solution Procedure 2.3 Extreme Points and the Optimal Solution 2.4 Computer Solution of the Par, Inc., Problem 2.5 A Simple Minimization Problem 2.6 Special Cases 2.7 General Linear Programming Notation Summary Glossary Problems Case Problem 1: Workload Balancing Case Problem 2: Production Strategy Case Problem 3: Hart Venture Capital Appendix 2.1: Solving Linear Programs with Excel Solver Chapter 3: Linear Programming: Sensitivity Analysis and Interpretation of Solution 3.1 Introduction to Sensitivity Analysis 3.2 Graphical Sensitivity Analysis 3.3 Sensitivity Analysis: Computer Solution 3.4 Limitations of Classical Sensitivity Analysis 3.5 The Electronic Communications Problem Summary Glossary Problems Case Problem 1: Product Mix Case Problem 2: Investment Strategy Appendix 3.1: Sensitivity Analysis with Excel Solver Chapter 4: Linear Programming Applications in Marketing, Finance, and Operations Management 4.1 Marketing Applications 4.2 Financial Applications 4.3 Operations Management Applications Summary Problems Case Problem 1: Planning an Advertising Campaign Case Problem 2: Schneider's Sweet Shop Case Problem 3: Textile Mill Planning Case Problem 4: Workforce Scheduling Case Problem 5: Duke Energy Coal Allocation Appendix 4.1: Excel Solution of Hewlitt Corporation Financial Planning Problem Chapter 5: Advanced Linear Programming Applications 5.1 Data Envelopment Analysis 5.2 Revenue Management 5.3 Portfolio Models and Asset Allocation 5.4 Game Theory Summary Glossary Problems Chapter 6: Distribution and Network Models 6.1 Supply Chain Models 6.2 Assignment Problem 6.3 Shortest-Route Problem 6.4 Maximal Flow Problem 6.5 A Production and Inventory Application Summary Glossary Problems Case Problem 1: Solutions Plus Case Problem 2: Supply Chain Design Appendix 6.1: Excel Solution of Transportation, Transshipment, and Assignment Problems Chapter 7: Integer Linear Programming 7.1 Types of Integer Linear Programming Models 7.2 Graphical and Computer Solutions for an All-Integer Linear Program 7.3 Applications Involving 0-1 Variables 7.4 Modeling Flexibility Provided by 0-1 Integer Variables Summary Glossary Problems Case Problem 1: Textbook Publishing Case Problem 2: Yeager National Bank Case Problem 3: Production Scheduling with Changeover Costs Case Problem 4: Applecore Children's Clothing Appendix 7.1: Excel Solution of Integer Linear Programs Chapter 8: Nonlinear Optimization Models 8.1 A Production Application - Par, Inc., Revisited 8.2 Constructing an Index Fund 8.3 Markowitz Portfolio Model 8.4 Blending: The Pooling Problem 8.5 Forecasting Adoption of a New Product Summary Glossary Problems Case Problem 1: Portfolio Optimization with Transaction Costs Case Problem 2: Cafe Compliance in the Auto Industry Appendix 8.1: Solving Nonlinear Optimization Problems with Excel Solver Chapter 9: Project Scheduling: PERT/CPM 9.1 Project Scheduling Based on Expected Activity Times 9.2 Project Scheduling Considering Uncertain Activity Times 9.3 Considering Time-Cost Trade-Offs Summary Glossary Problems Case Problem 1: R. C. Coleman Appendix 9.1: Finding Cumulative Probabilities for Normally Distributed Random Variables Chapter 10: Inventory Models 10.1 Economic Order Quantity (EOQ) Model 10.2 Economic Production Lot Size Model 10.3 Inventory Model with Planned Shortages 10.4 Quantity Discounts for the EOQ Model 10.5 Single-Period Inventory Model with Probabilistic Demand 10.6 Order-Quantity, Reorder Point Model with Probabilistic Demand 10.7 Periodic Review Model with Probabilistic Demand Summary Glossary Problems Case Problem 1: Wagner Fabricating Company Case Problem 2: River City Fire Department Appendix 10.1: Development of the Optimal Order Quantity (Q) Formula for the EOQ Model Appendix 10.2: Development of the Optimal Lot Size (Q*) Formula for the Production Lot Size Model Chapter 11: Waiting Line Models 11.1 Structure of a Waiting Line System 11.2 Single-Server Waiting Line Model with Poisson Arrivals and Exponential Service Times 11.3 Multiple-Server Waiting Line Model with Poisson Arrivals and Exponential Service Times 11.4 Some General Relationships for Waiting Line Models 11.5 Economic Analysis of Waiting Lines 11.6 Kendall's Notation for Classifying Queueing Models 11.7 Single-Server Waiting Line Model with Poisson Arrivals and General Service Times 11.8 Multiple-Server Model with Poisson Arrivals, General Service Times, and No Waiting Line 11.9 Waiting Line Models with Finite Calling Populations Summary Glossary Problems Case Problem 1: Regional Airlines Case Problem 2: Olympus Equipment, Inc. Chapter 12: Simulation 12.1 What-If Analysis 12.2 Simulation of Sanotronics Problem 12.3 Inventory Simulation 12.4 Waiting Line Simulation 12.5 Simulation Considerations Summary Summary of Steps for Conducting a Simulation Analysis Glossary Problems Case Problem 1: Four Corners Case Problem 2: Harbor Dunes Golf Course Case Problem 3: County Beverage Drive-Thru Appendix 12.1: Common Probability Distributions for Simulation Chapter 13: Decision Analysis 13.1 Problem Formulation 13.2 Decision Making without Probabilities 13.3 Decision Making with Probabilities 13.4 Risk Analysis and Sensitivity Analysis 13.5 Decision Analysis with Sample Information 13.6 Computing Branch Probabilities with Bayes' Theorem 13.7 Utility Theory Summary Glossary Problems Case Problem 1: Property Purchase Strategy Case Problem 2: Lawsuit Defense Strategy Case Problem 3: Rob's Market Case Problem 4: College Softball Recruiting Chapter 14: Multicriteria Decisions 14.1 Goal Programming: Formulation and Graphical Solution 14.2 Goal Programming: Solving More Complex Problems 14.3 Scoring Models 14.4 Analytic Hierarchy Process 14.5 Establishing Priorities Using AHP 14.6 Using AHP to Develop an Overall Priority Ranking Summary Glossary Problems Case Problem 1: Banh Trailers, Inc. Appendix 14.1: Scoring Models with Excel Chapter 15: Time Series Analysis and Forecasting 15.1 Time Series Patterns 15.2 Forecast Accuracy 15.3 Moving Averages and Exponential Smoothing 15.4 Linear Trend Projection 15.5 Seasonality Summary Glossary Problems Case Problem 1: Forecasting Food and Beverage Sales Case Problem 2: Forecasting Lost Sales Appendix 15.1: Forecasting with Excel Data Analysis Tools Appendix 15.2: Using the Excel Forecast Sheet Chapter 16: Markov Processes 16.1 Market Share Analysis 16.2 Accounts Receivable Analysis Summary Glossary Problems Case Problem 1: Dealer's Absorbing State Probabilities in Blackjack Appendix 16.1: Matrix Notation and Operations Appendix 16.2: Matrix Inversion with Excel Appendices Appendix A: Building Spreadsheet Models Appendix B: Areas for the Standard Normal Distribution Appendix C: Values of e-Lambda Appendix D: References and Bibliography Index