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" Practical management science / "
Wayne L. Winston, Kelley School of Business, Indiana University, S. Christian Albright, Kelley School of Business, Indiana University.
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BL
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| Record Number
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844580
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| Main Entry
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Winston, Wayne L.
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| Title & Author
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Practical management science /\ Wayne L. Winston, Kelley School of Business, Indiana University, S. Christian Albright, Kelley School of Business, Indiana University.
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| Edition Statement
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6th edition.
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| Publication Statement
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Australia ;Boston, MA :: Cengage,, [2019]
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| Page. NO
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xvi, 824 pages :: illustrations ;; 26 cm
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| ISBN
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1337406651
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: 9781337406659
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| Bibliographies/Indexes
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Includes bibliographical references (pages 809-813) and index.
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| Contents
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Machine generated contents note: ch. 1 Introduction to Modeling -- 1.1. Introduction -- 1.2.A Capital Budgeting Example -- 1.3. Modeling versus Models -- 1.4.A Seven-Step Modeling Process -- 1.5.A Great Source for Management Science Applications: Interfaces -- 1.6. Why Study Management Science? -- 1.7. Software Included with This Book -- 1.8. Conclusion -- ch. 2 Introduction to Spreadsheet Modeling -- 2.1. Introduction -- 2.2. Basic Spreadsheet Modeling: Concepts and Best Practices -- 2.3. Cost Projections -- 2.4. Breakeven Analysis -- 2.5. Ordering with Quantity Discounts and Demand Uncertainty -- 2.6. Estimating the Relationship between Price and Demand -- 2.7. Decisions Involving the Time Value of Money -- 2.8. Conclusion -- Appendix Tips for Editing and Documenting Spreadsheets -- Case 2.1 Project Selection at Ewing Natural Gas -- Case 2.2 New Product Introduction at eTech -- ch. 3 Introduction to Optimization Modeling -- 3.1. Introduction -- 3.2. Introduction to Optimization -- 3.3.A Two-Variable Product Mix Model -- 3.4. Sensitivity Analysis -- 3.5. Properties of Linear Models -- 3.6. Infeasibility and Unboundedness -- 3.7.A Larger Product Mix Model -- 3.8.A Multiperiod Production Model -- 3.9.A Comparison of Algebraic and Spreadsheet Models -- 3.10.A Decision Support System -- 3.11. Conclusion -- Appendix Information on Optimization Software -- Case 3.1 Shelby Shelving -- ch. 4 Linear Programming Models -- 4.1. Introduction -- 4.2. Advertising Models -- 4.3. Employee Scheduling Models -- 4.4. Aggregate Planning Models -- 4.5. Blending Models -- 4.6. Production Process Models -- 4.7. Financial Models -- 4.8. Data Envelopment Analysis (DEA) -- 4.9. Conclusion -- Case 4.1 Blending Aviation Gasoline at Jansen Gas -- Case 4.2 Delinquent Accounts at GE Capital -- Case 4.3 Foreign Currency Trading -- ch. 5 Network Models -- 5.1. Introduction -- 5.2. Transportation Models -- 5.3. Assignment Models -- 5.4. Other Logistics Models -- 5.5. Shortest Path Models -- 5.6.Network Models in the Airline Industry -- 5.7. Conclusion -- Case 5.1 Optimized Motor Carrier Selection at Westvaco -- ch. 6 Optimization Models with Integer Variables -- 6.1. Introduction -- 6.2. Overview of Optimization with Integer Variables -- 6.3. Capital Budgeting Models -- 6.4. Fixed-Cost Models -- 6.5. Set-Covering and Location-Assignment Models -- 6.6. Cutting Stock Models -- 6.7. Conclusion -- Case 6.1 Giant Motor Company -- Case 6.2 Selecting Telecommunication Carriers to Obtain Volume Discounts -- Case 6.3 Project Selection at Ewing Natural Gas -- ch. 7 Nonlinear Optimization Models -- 7.1. Introduction -- 7.2. Basic Ideas of Nonlinear Optimization -- 7.3. Pricing Models -- 7.4. Advertising Response and Selection Models -- 7.5. Facility Location Models -- 7.6. Models for Rating Sports Teams -- 7.7. Portfolio Optimization Models -- 7.8. Estimating the Beta of a Stock -- 7.9. Conclusion -- Case 7.1 GMS Stock Hedging -- ch. 8 Evolutionary Solver: An Alternative Optimization Procedure -- 8.1. Introduction -- 8.2. Introduction to Genetic Algorithms -- 8.3. Introduction to Evolutionary Solver -- 8.4. Nonlinear Pricing Models -- 8.5.Combinatorial Models -- 8.6. Fitting an S-Shaped Curve -- 8.7. Portfolio Optimization -- 8.8. Optimal Permutation Models -- 8.9. Conclusion -- Case 8.1 Assigning MBA Students to Teams -- Case 8.2 Project Selection at Ewing Natural Gas -- ch. 9 Decision Making under Uncertainty -- 9.1. Introduction -- 9.2. Elements of Decision Analysis -- 9.3. Single-Stage Decision Problems -- 9.4. The PrecisionTree Add-In -- 9.5. Multistage Decision Problems -- 9.6. The Role of Risk Aversion -- 9.7. Conclusion -- Case 9.1 Jogger Shoe Company -- Case 9.2 Westhouser Paper Company -- Case 9.3 Electronic Timing System for Olympics -- Case 9.4 Developing a Helicopter Component for the Army -- ch. 10 Introduction to Simulation Modeling -- 10.1. Introduction -- 10.2. Probability Distributions for Input Variables -- 10.3. Simulation and the Flaw of Averages -- 10.4. Simulation with Built-in Excel Tools -- 10.5. Introduction to @RISK -- 10.6. The Effects of Input Distributions on Results -- 10.7. Conclusion -- Appendix Learning More About @RISK -- Case 10.1 Ski Iacket Production -- Case 10.2 Ebony Bath Soap -- Case 10.3 Advertising Effectiveness -- Case 10.4 New Project Introduction at eTech -- ch. 11 Simulation Models -- 11.1. Introduction -- 11.2. Operations Models -- 11.3. Financial Models -- 11.4. Marketing Models -- 11.5. Simulating Games of Chance -- 11.6. Conclusion -- Appendix Other Palisade Tools for Simulation -- Case 11.1 College Fund Investment -- Case 11.2 Bond Investment Strategy -- Case 11.3 Project Selection Ewing Natural Gas -- ch. 12 Queueing Models -- 12.1. Introduction -- 12.2. Elements of Queueing Models -- 12.3. The Exponential Distribution -- 12.4. Important Queueing Relationships -- 12.5. Analytic Steady-State Queueing Models -- 12.6. Queueing Simulation Models -- 12.7. Conclusion -- Case 12.1 Catalog Company Phone Orders -- ch. 13 Regression and Forecasting Models -- 13.1. Introduction -- 13.2. Overview of Regression Models -- 13.3. Simple Regression Models -- 13.4. Multiple Regression Models -- 13.5. Overview of Time Series Models -- 13.6. Moving Averages Models -- 13.7. Exponential Smoothing Models -- 13.8. Conclusion -- Case 13.1 Demand for French Bread at Howie's Bakery -- Case 13.2 Forecasting Overhead at Wagner Printers -- Case 13.3 Arrivals at the Credit Union -- ch. 14 Data Mining -- 14.1. Introduction -- 14.2. Classification Methods -- 14.3. Clustering Methods -- 14.4. Conclusion -- Case 14.1 Houston Area Survey -- References -- Index -- MindTap Chapters -- ch. 15 Project Management -- 15.1. Introduction -- 15.2. The Basic CPM Model -- 15.3. Modeling Allocation of Resources -- 15.4. Models with Uncertain Activity Times -- 15.5.A Brief Look at Microsoft Project -- 15.6. Conclusion -- ch. 16 Multiobjective Decision Making -- 16.1. Introduction -- 16.2. Goal Programming -- 16.3. Pareto Optimality and Trade-Off Curves -- 16.4. The Analytic Hierarchy Process (AHP) -- 16.5. Conclusion -- ch. 17 Inventory and Supply Chain Models -- 17.1. Introduction -- 17.2. Categories of Inventory and Supply Chain Models -- 17.3. Types of Costs in Inventory and Supply Chain Models -- 17.4. Economic Order Quantity (EOQ) Models -- 17.5. Probabilistic Inventory Models -- 17.6. Ordering Simulation Models -- 17.7. Supply Chain Models -- 17.8. Conclusion.
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| Subject
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Electronic spreadsheets.
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| Subject
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Management science, Software.
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Management science-- Computer simulation.
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Management science-- Mathematical models.
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Electronic spreadsheets.
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Management science-- Computer simulation.
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Management science-- Mathematical models.
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| Subject
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Management science.
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| Dewey Classification
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001.434
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| LC Classification
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T57.62.W55 2019
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| Added Entry
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Albright, S. Christian
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