Quantitative Analysis for Management, 13/e

Book Description

Foundational understanding of management science through real-world problems and solutions

Quantitative Analysis for Management helps readers to develop a real-world understanding of business analytics, quantitative methods, and management science by emphasizing model building, tangible examples, and computer applications. The authors offer an accessible introduction to mathematical models and then readers apply those models using step-by-step, how-to instructions. For more intricate mathematical procedures, the 13th Edition offers a flexible approach, allowing readers to omit specific sections without interrupting the flow of the material.

Table of Contents

  1. Quantitative Analysis for Management
  2. About the Authors
  3. Brief Contents
  4. Contents
  5. Preface
    1. Overview
    2. Special Features
    3. Significant Changes to the Thirteenth Edition
    4. Online Modules
    5. Software
    6. Companion Website
    7. Instructor Resources
  6. Chapter 1 Introduction to Quantitative Analysis
    1. Learning Objectives
    2. 1.1 What Is Quantitative Analysis?
    3. 1.2 Business Analytics
    4. 1.3 The Quantitative Analysis Approach
      1. Defining the Problem
      2. Developing a Model
      3. Acquiring Input Data
      4. Developing a Solution
      5. Testing the Solution
      6. Analyzing the Results and Sensitivity Analysis
      7. Implementing the Results
      8. The Quantitative Analysis Approach and Modeling in the Real World
    5. 1.4 How to Develop a Quantitative Analysis Model
      1. The Advantages of Mathematical Modeling
      2. Mathematical Models Categorized by Risk
    6. 1.5 The Role of Computers and Spreadsheet Models in the Quantitative Analysis Approach
    7. 1.6 Possible Problems in the Quantitative Analysis Approach
      1. Defining the Problem
        1. Conflicting Viewpoints
        2. Impact on Other Departments
        3. Beginning Assumptions
        4. Solution Outdated
      2. Developing a Model
        1. Fitting the Textbook Models
        2. Understanding the Model
      3. Acquiring Input Data
        1. Using Accounting Data
        2. Validity of Data
      4. Developing a Solution
        1. Hard-to-Understand Mathematics
        2. Only One Answer is Limiting
      5. Testing the Solution
      6. Analyzing the Results
    8. 1.7 Implementation—Not Just the Final StepImplementation—Not Just the Final Step
      1. Lack of Commitment and Resistance to Change
      2. Lack of Commitment by Quantitative Analysts
    9. Summary
    10. Glossary
    11. Key Equations
    12. Self-Test
    13. Discussion Questions and Problems
      1. Discussion Questions
      2. Problems
    14. Bibliography
  7. Chapter 2 Probability Concepts and Applications
    1. Learning Objectives
    2. 2.1 Fundamental Concepts
      1. Two Basic Rules of Probability
      2. Types of Probability
      3. Mutually Exclusive and Collectively Exhaustive Events
      4. Unions and Intersections of Events
      5. Probability Rules for Unions, Intersections, and Conditional Probabilities
    3. 2.2 Revising Probabilities with Bayes’ TheoremRevising Probabilities with Bayes’ Theorem
      1. General Form of Bayes’ Theorem
    4. 2.3 Further Probability Revisions
    5. 2.4 Random Variables
    6. 2.5 Probability Distributions
      1. Probability Distribution of a Discrete Random Variable
      2. Expected Value of a Discrete Probability Distribution
      3. Variance of a Discrete Probability Distribution
      4. Probability Distribution of a Continuous Random Variable
    7. 2.6 The Binomial Distribution
      1. Solving Problems with the Binomial Formula
      2. Solving Problems with Binomial Tables
    8. 2.7 The Normal Distribution
      1. Area Under the Normal Curve
      2. Using the Standard Normal Table
      3. Haynes Construction Company Example
      4. The Empirical Rule
    9. 2.8 The F Distribution
    10. 2.9 The Exponential Distribution
      1. Arnold’s Muffler Example
    11. 2.10 The Poisson Distribution
    12. Summary
    13. Glossary
    14. Key Equations
    15. Solved Problems
      1. Solution
      2. Solution
      3. Solution
      4. Solution
      5. Solution
      6. Solution
      7. Solution
      8. Solution
    16. Self-Test
    17. Discussion Questions and Problems
      1. Discussion Questions
      2. Problems
    18. Bibliography
    19. Appendix 2.1: Derivation of Bayes’ TheoremDerivation of Bayes’ Theorem
  8. Chapter 3 Decision Analysis
    1. Learning Objectives
    2. 3.1 The Six Steps in Decision Making
    3. 3.2 Types of Decision-Making Environments
    4. 3.3 Decision Making Under Uncertainty
      1. Optimistic
      2. Pessimistic
      3. Criterion of Realism (Hurwicz Criterion)
      4. Equally Likely (Laplace)
      5. Minimax Regret
    5. 3.4 Decision Making Under Risk
      1. Expected Monetary Value
      2. Expected Value of Perfect Information
      3. Expected Opportunity Loss
      4. Sensitivity Analysis
      5. A Minimization Example
    6. 3.5 Using Software for Payoff Table Problems
      1. QM for Windows
      2. Excel QM
    7. 3.6 Decision Trees
      1. Efficiency of Sample Information
      2. Sensitivity Analysis
    8. 3.7 How Probability Values Are Estimated by Bayesian Analysis
      1. Calculating Revised Probabilities
      2. Potential Problem in Using Survey Results
    9. 3.8 Utility Theory
      1. Measuring Utility and Constructing a Utility Curve
      2. Utility as a Decision-Making Criterion
    10. Summary
    11. Glossary
    12. Key Equations
    13. Solved Problems
      1. Solution
      2. Solution
      3. Solution
      4. Solution
    14. Self-Test
    15. Discussion Questions and Problems
      1. Discussion Questions
      2. Problems
    16. Bibliography
  9. Chapter 4 Regression Models
    1. Learning Objectives
    2. 4.1 Scatter Diagrams
    3. 4.2 Simple Linear Regression
    4. 4.3 Measuring the Fit of the Regression Model
      1. Coefficient of Determination
      2. Correlation Coefficient
    5. 4.4 Assumptions of the Regression Model
      1. Estimating the Variance
    6. 4.5 Testing the Model for Significance
      1. Triple A Construction Example
      2. The Analysis of Variance (ANOVA) Table
      3. Triple A Construction ANOVA Example
    7. 4.6 Using Computer Software for Regression
      1. Excel 2016
      2. Excel QM
      3. QM for Windows
    8. 4.7 Multiple Regression Analysis
      1. Evaluating the Multiple Regression Model
      2. Jenny Wilson Realty Example
    9. 4.8 Binary or Dummy Variables
    10. 4.9 Model Building
      1. Stepwise Regression
      2. Multicollinearity
    11. 4.10 Nonlinear Regression
    12. 4.11 Cautions and Pitfalls in Regression Analysis
    13. Summary
    14. Glossary
    15. Key Equations
    16. Solved Problems
      1. Solution
      2. Solution
    17. Self-Test
    18. Discussion Questions and Problems
      1. Discussion Questions
      2. Problems
    19. Bibliography
    20. Appendix 4.1: Formulas for Regression Calculations
  10. Chapter 5 Forecasting
    1. Learning Objectives
    2. 5.1 Types of Forecasting Models
      1. Qualitative Models
      2. Causal Models
      3. Time-Series Models
    3. 5.2 Components of a Time-Series
    4. 5.3 Measures of Forecast Accuracy
    5. 5.4 Forecasting Models—Random Variations OnlyForecasting Models—Random Variations Only
      1. Moving Averages
        1. Wallace Garden Supply Example
      2. Weighted Moving Averages
      3. Exponential Smoothing
        1. Getting Started
        2. Port of Baltimore Example
      4. Using Software for Forecasting Time Series
    6. 5.5 Forecasting Models—Trend and Random VariationsForecasting Models—Trend and Random Variations
      1. Exponential Smoothing with Trend
      2. Trend Projections
    7. 5.6 Adjusting for Seasonal Variations
      1. Seasonal Indices
      2. Calculating Seasonal Indices with No Trend
      3. Calculating Seasonal Indices with Trend
    8. 5.7 Forecasting Models—Trend, Seasonal, and Random VariationsForecasting Models—Trend, Seasonal, and Random Variations
      1. The Decomposition Method
      2. Software for Decomposition
      3. Using Regression with Trend and Seasonal Components
    9. 5.8 Monitoring and Controlling Forecasts
      1. Adaptive Smoothing
    10. Summary
    11. Glossary
    12. Key Equations
    13. Solved Problems
      1. Solution
      2. Solution
    14. Self-Test
    15. Discussion Questions and Problems
      1. Discussion Questions
      2. Problems
        1. Bibliography
  11. Chapter 6 Inventory Control Models
    1. Learning Objectives
    2. 6.1 Importance of Inventory Control
      1. Decoupling Function
      2. Storing Resources
      3. Irregular Supply and Demand
      4. Quantity Discounts
      5. Avoiding Stockouts and Shortages
    3. 6.2 Inventory Decisions
    4. 6.3 Economic Order Quantity: Determining How Much to Order
      1. Inventory Costs in the EOQ Situation
      2. Finding the EOQ
      3. Sumco Pump Company Example
        1. Using Excel QM for Basic EOQ Inventory Problems
      4. Purchase Cost of Inventory Items
      5. Sensitivity Analysis with the EOQ Model
    5. 6.4 Reorder Point: Determining When to Order
    6. 6.5 EOQ Without the Instantaneous Receipt Assumption
      1. Annual Carrying Cost for Production Run Model
      2. Annual Setup Cost or Annual Ordering Cost
      3. Determining the Optimal Production Quantity
      4. Brown Manufacturing Example
        1. Using Excel QM for Production Run Models
    7. 6.6 Quantity Discount Models
      1. Brass Department Store Example
        1. Using Excel QM for Quantity Discount Problems
    8. 6.7 Use of Safety Stock
    9. 6.8 Single-Period Inventory Models
      1. Marginal Analysis with Discrete Distributions
      2. Café du Donut Example
      3. Marginal Analysis with the Normal Distribution
      4. Newspaper Example
    10. 6.9 ABC Analysis
    11. 6.10 Dependent Demand: The Case for Material Requirements Planning
      1. Material Structure Tree
      2. Gross and Net Material Requirements Plans
      3. Two or More End Products
    12. 6.11 Just-In-Time Inventory Control
    13. 6.12 Enterprise Resource Planning
    14. Summary
    15. Glossary
    16. Key Equations
    17. Solved Problems
      1. Solution
      2. Solution
      3. Solution
      4. Solution
      5. Solution
    18. Self-Test
    19. Discussion Questions and Problems
      1. Discussion Questions
      2. Problems
      3. Bibliography
      4. Appendix 6.1: Inventory Control with QM for Windows
  12. Chapter 7 Linear Programming Models: Graphical and Computer Methods
    1. Learning Objectives
    2. 7.1 Requirements of a Linear Programming Problem
    3. 7.2 Formulating LP Problems
      1. Flair Furniture Company
    4. 7.3 Graphical Solution to an LP Problem
      1. Graphical Representation of Constraints
      2. Isoprofit Line Solution Method
    5. Corner Point Solution Method
    6. Slack and Surplus
    7. 7.4 Solving Flair Furniture’s LP Problem Using QM for Windows, Excel 2016, and Excel QMSolving Flair Furniture’s LP Problem Using QM for Windows, Excel 2016, and Excel QM
      1. Using QM for Windows
      2. Using Excel’s Solver Command to Solve LP Problems
        1. Preparing the Spreadsheet For Solver
        2. Using Solver
      3. Using Excel QM
    8. 7.5 Solving Minimization Problems
      1. Holiday Meal Turkey Ranch
        1. Using The Corner Point Method On A Minimization Problem
        2. Isocost Line Approach
    9. 7.6 Four Special Cases in LP
      1. No Feasible Solution
      2. Unboundedness
      3. Redundancy
      4. Alternate Optimal Solutions
    10. 7.7 Sensitivity Analysis
      1. High Note Sound Company
      2. Changes in the Objective Function Coefficient
      3. QM for Windows and Changes in Objective Function Coefficients
      4. Excel Solver and Changes in Objective Function Coefficients
      5. Changes in the Technological Coefficients
      6. Changes in the Resources or Right-Hand-Side Values
      7. QM for Windows and Changes in Right-Hand-Side Values
      8. Excel Solver and Changes in Right-Hand-Side Values
    11. Summary
    12. Glossary
    13. Solved Problems
      1. Solution
      2. Solution
      3. Solution
      4. Solution
    14. Self-Test
    15. Discussion Questions and Problems
      1. Discussion Questions
        1. Problems
      2. Discussion Questions
        1. Bibliography
  13. Chapter 8 Linear Programming Applications
    1. Learning Objectives
    2. 8.1 Marketing Applications
      1. Media Selection
      2. Marketing Research
    3. 8.2 Manufacturing Applications
      1. Production Mix
      2. Production Scheduling
    4. 8.3 Employee Scheduling Applications
      1. Labor Planning
    5. 8.4 Financial Applications
      1. Portfolio Selection
      2. Truck Loading Problem
    6. 8.5 Ingredient Blending Applications
      1. Diet Problems
      2. Ingredient Mix and Blending Problems
    7. 8.6 Other Linear Programming Applications
    8. Summary
    9. Self-Test
    10. Problems
    11. Bibliography
  14. Chapter 9 Transportation, Assignment, and Network Models
    1. Learning Objectives
    2. 9.1 The Transportation Problem
      1. Linear Program for the Transportation Example
      2. Solving Transportation Problems Using Computer Software
      3. A General LP Model for Transportation Problems
      4. Facility Location Analysis
    3. 9.2 The Assignment Problem
      1. Linear Program for Assignment Example
    4. 9.3 The Transshipment Problem
      1. Linear Program for Transshipment Example
    5. 9.4 Maximal-Flow Problem
    6. 9.5 Shortest-Route Problem
    7. 9.6 Minimal-Spanning Tree Problem
    8. Summary
    9. Glossary
    10. Solved Problems
      1. Solution
    11. Self-Test
    12. Discussion Questions and Problems
      1. Discussion Questions
      2. Problems*
      3. Bibliography
      4. Appendix 9.1: Using QM for Windows
  15. Chapter10Integer Programming, Goal Programming, and Nonlinear Programming
    1. Learning Objectives
    2. 10.1 Integer Programming
      1. Harrison Electric Company Example of Integer Programming
      2. Using Software to Solve the Harrison Integer Programming Problem
      3. Mixed-Integer Programming Problem Example
        1. Using QM For Windows And Excel to Solve Bagwell’s Integer Programming Model
    3. 10.2 Modeling with 0–1 (Binary) VariablesModeling with 0–1 (Binary) Variables
      1. Capital Budgeting Example
      2. Limiting the Number of Alternatives Selected
      3. Dependent Selections
      4. Fixed-Charge Problem Example
      5. Financial Investment Example
    4. 10.3 Goal Programming
      1. Extension to Equally Important Multiple Goals
      2. Ranking Goals with Priority Levels
      3. Goal Programming with Weighted Goals
        1. Using QM For Windows To Solve Harrison’s Problem
    5. 10.4 Nonlinear Programming
      1. Nonlinear Objective Function and Linear Constraints
      2. Both Nonlinear Objective Function and Nonlinear Constraints
      3. Linear Objective Function with Nonlinear Constraints
    6. Summary
    7. Glossary
    8. Solved Problems
      1. Solution
      2. Solution
      3. Solution
    9. Self-Test
    10. Discussion Questions and Problems
      1. Discussion Questions
      2. Problems
      3. Bibliography
  16. Chapter 11 Project Management
    1. Learning Objectives
    2. 11.1 PERT/CPM
      1. General Foundry Example of PERT/CPM
      2. Drawing the PERT/CPM Network
      3. Activity Times
      4. How to Find the Critical Path
        1. Earliest Times
        2. Latest Times
        3. Concept Of Slack In Critical Path Computations
      5. Probability of Project Completion
      6. What PERT Was Able to Provide
      7. Using Excel QM for the General Foundry Example
      8. Sensitivity Analysis and Project Management
    3. 11.2 PERT/Cost
      1. Planning and Scheduling Project Costs: Budgeting Process
        1. Budgeting For General Foundry
      2. Monitoring and Controlling Project Costs
    4. 11.3 Project Crashing
      1. General Foundry Example
      2. Project Crashing with Linear Programming
        1. Objective Function
        2. Crash Time Constraints
        3. Project Completion Constraint
        4. Constraints Describing The Network
    5. 11.4 Other Topics in Project Management
      1. Subprojects
      2. Milestones
      3. Resource Leveling
      4. Software
    6. Summary
    7. Glossary
    8. Key Equations
    9. Solved Problems
      1. Solution
      2. Solution
    10. Self-Test
    11. Discussion Questions and Problems
      1. Discussion Questions
      2. Problems
        1. Discussion Question
    12. Bibliography
    13. Appendix 11.1: Project Management with QM for Windows
  17. Chapter 12 Waiting Lines and Queuing Theory Models
    1. Learning Objectives
    2. 12.1 Waiting Line Costs
      1. Three Rivers Shipping Company Example
    3. 12.2 Characteristics of a Queuing System
      1. Arrival Characteristics
        1. Size Of The Calling Population
        2. Pattern Of Arrivals At The System
        3. Behavior Of The Arrivals
      2. Waiting Line Characteristics
      3. Service Facility Characteristics
        1. Basic Queuing System Configurations
        2. Service Time Distribution
      4. Identifying Models Using Kendall Notation
    4. 12.3 Single-Channel Queuing Model with Poisson Arrivals and Exponential Service Times (M/M /1)
      1. Assumptions of the Model
      2. Queuing Equations
      3. Arnold’s Muffler Shop Case
        1. Using Excel QM On The Arnold’s Muffler Shop Queue
        2. Introducing Costs Into The Model
      4. Enhancing the Queuing Environment
    5. 12.4 Multichannel Queuing Model with Poisson Arrivals and Exponential Service Times (M ∕ ∕M ∕ ∕m)
      1. Equations for the Multichannel Queuing Model
      2. Arnold’s Muffler Shop Revisited
        1. Using Excel QM For Analysis Of Arnold’s Multichannel Queuing Model
    6. 12.5 Constant Service Time Model (M /D / 1)
      1. Equations for the Constant Service Time Model
      2. Garcia-Golding Recycling, Inc.
        1. Using Excel QM For Garcia-Golding’s Constant Service Time Model
    7. 12.6 Finite Population Model (M / M / 1 with Finite Source)
      1. Equations for the Finite Population Model
      2. Department of Commerce Example
        1. Solving The Department Of Commerce Finite Population Model With Excel QM
    8. 12.7 Some General Operating Characteristic Relationships
    9. 12.8 More Complex Queuing Models and the Use of Simulation
    10. Summary
    11. Glossary
    12. Key Equations
    13. Solved Problems
      1. Solution
      2. Solution
      3. Solution
      4. Solution
    14. Self-Test
    15. Discussion Questions and Problems
      1. Discussion Questions
      2. Problems
    16. Bibliography
    17. Appendix 12.1: Using QM for Windows
  18. Chapter 13 Simulation Modeling
    1. Learning Objectives
    2. 13.1 Advantages and Disadvantages of Simulation
    3. 13.2 Monte Carlo Simulation
      1. Harry’s Auto Tire Example
        1. Step 1: Establishing Probability Distributions.
        2. Step 2: Building a Cumulative Probability Distribution for Each Variable.
        3. Step 3: Setting Random Number Intervals.
        4. Step 4: Generating Random Numbers.
        5. Step 5: Simulating the Experiment.
      2. Using QM for Windows for Simulation
      3. Simulation with Excel Spreadsheets
    4. 13.3 Simulation and Inventory Analysis
      1. Simkin’s Hardware Store
      2. Analyzing Simkin’s Inventory Costs
    5. 13.4 Simulation of a Queuing Problem
      1. Port of New Orleans
      2. Using Excel to Simulate the Port of New Orleans Queuing Problem
    6. 13.5 Simulation Model for a Maintenance Policy
      1. Three Hills Power Company
        1. Column 1: Breakdown Number
        2. Column 2: Random Number for Breakdowns
        3. Column 3: Time Between Breakdowns
        4. Column 4: Time of Breakdown
        5. Column 5: Time Repairperson Is Free to Begin Repair
        6. Column 6: Random Number for Repair Time
        7. Column 7: Repair Time Required
        8. Column 8: Time Repair Ends
        9. Column 9: Number of Hours the Machine Is Down
      2. Cost Analysis of the Simulation
        1. Building An Excel Simulation Model For Three Hills Power Company
    7. 13.6 Other Simulation Issues
      1. Two Other Types of Simulation Models
        1. Operational Gaming
        2. Systems Simulation
      2. Verification and Validation
      3. Role of Computers in Simulation
    8. Summary
    9. Glossary
    10. Solved Problems
      1. Solution
      2. Solution
    11. Self-Test
    12. Discussion Questions and Problems
      1. Discussion Questions
      2. Problems
    13. Bibliography
  19. Chapter 14 Markov Analysis
    1. Learning Objectives
    2. 14.1 States and State Probabilities
      1. The Vector of State Probabilities for Grocery Store Example
    3. 14.2 Matrix of Transition Probabilities
      1. Transition Probabilities for Grocery Store Example
    4. 14.3 Predicting Future Market Shares
    5. 14.4 Markov Analysis of Machine Operations
    6. 14.5 Equilibrium Conditions
    7. 14.6 Absorbing States and the Fundamental Matrix: Accounts Receivable Application
    8. Summary
    9. Glossary
    10. Key Equations
    11. Solved Problems
      1. Solution
      2. Solution
    12. Self-Test
    13. Discussion Questions and Problems
      1. Discussion Questions
      2. Problems
    14. Bibliography
    15. Appendix 14.1: Markov Analysis with QM for Windows
    16. Appendix 14.2: Markov Analysis with Excel
  20. Chapter 15 Statistical Quality Control
    1. Learning Objectives
    2. 15.1 Defining Quality and TQM
    3. 15.2 Statistical Process Control
      1. Variability in the Process
        1. Building Control Charts
        2. Natural Variations
        3. Assignable Variations
    4. 15.3 Control Charts for Variables
      1. The Central Limit Theorem
      2. Setting X -Chart Limits
        1. Box-Filling Example
        2. Using Excel QM For Box-Filling Example
        3. Super Cola Example
        4. Using Excel QM For Super Cola Example
      3. Setting Range Chart Limits
        1. Range Example
    5. 15.4 Control Charts for Attributes
      1. p-Charts
        1. ARCO p-Chart Example
        2. Using Excel QM For p-Charts
      2. c-Charts
        1. Red Top Cab Company c-Chart Example
        2. Using Excel QM For c-Charts
    6. Summary
    7. Glossary
    8. Key Equations
    9. Solved Problems
      1. Solution
      2. Solution
      3. Solution
    10. Self-Test
    11. Discussion Questions and Problems
      1. Discussion Questions
      2. Problems
    12. Bibliography
    13. Appendix 15.1: Using QM for Windows for SPC
  21. Appendices
    1. Appendix A: Areas Under the Standard Normal Curve
  22. Appendix B: Binomial Probabilities
  23. Appendix C: Values of e - λ for Use in the Poisson Distribution
  24. Appendix D: F Distribution Values
  25. Appendix E: Using POM-QM for Windows
  26. Appendix F: Using Excel QM and Excel Add-Ins
  27. Appendix G: Solutions to Selected Problems
  28. Appendix H: Solutions to Self-Tests
  29. Index
    1. A
    2. B
    3. C
    4. D
    5. E
    6. F
    7. G
    8. H
    9. I
    10. J
    11. K
    12. L
    13. M
    14. N
    15. O
    16. P
    17. Q
    18. R
    19. S
    20. T
    21. U
    22. V
    23. W
    24. X
    25. Z
  30. Module 1 Analytic Hierarchy Process
  31. Module 2 Dynamic Programming
  32. Module 3 Decision Theory and the Normal Distribution
    1. Learning Objectives
    2. M3.1 Break-Even Analysis and the Normal Distribution
      1. Barclay Brothers Company’s New Product Decision
      2. Probability Distribution of Demand
      3. Using Expected Monetary Value to Make a Decision
    3. M3.2 Expected Value of Perfect Information and the Normal Distribution
      1. Opportunity Loss Function
      2. Expected Opportunity Loss
    4. Summary
    5. Glossary
    6. Key Equations
    7. Solved Problems
      1. Solution
      2. Solution
    8. Self-Test
    9. Discussion Questions and Problems
      1. Discussion Questions
      2. Problems
    10. Bibliography
    11. Appendix M3.1: Derivation of the Break-Even Point
    12. Appendix M3.2: Unit Normal Loss Integral
  33. Module 4 Game Theory
    1. Learning Objectives
    2. M4.1 Language of Games
    3. M4.2 The Minimax Criterion
    4. M4.3 Pure Strategy Games
    5. M4.4 Mixed Strategy Games
    6. M4.5 Dominance
    7. Summary
    8. Glossary
    9. Solved Problems
      1. Solution
      2. Solution
    10. Self-Test
    11. Discussion Questions and Problems
      1. Discussion Questions
        1. Problems
      2. Bibliography
  34. Module 5Mathematical Tools: Determinants and Matrices
  35. Module 6 Calculus-Based Optimization
  36. Module 7 Linear Programming: The Simplex Method
  37. Module 8 Transportation, Assignment, and Network Algorithms

Product Information

  • Title: Quantitative Analysis for Management, 13/e
  • Author(s): Barry Render, Ralph M. Stair, Michael E. Hanna, Trevor S. Hale
  • Release date: January 2017
  • Publisher(s): Pearson
  • ISBN: 9780134543161