00000cam a22000003a 4500 UP-8027390931312542629 Buklod 20210428112514.0 o--- | || || ta 210428s2019 xx d r |||| u| 9789814834476 (iLib)UPCEB-00012051436 oclc (emz) eng T 57.62 W56 2019 Winston, Wayne L. Practical management science Wayne L. Winston, Kelley School of Business, Indiana University, S. Christian Albright, Kelley School of Business, Indiana University. 6th edition. Australia Boston, MA Cengage c2019 xvi, 824 pages illustrations 26 cm Prof. Jesus Cinco (Recommending faculty) - Bookfair 2019 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. Management science Computer simulation. Management science Mathematical models. Management science Software. Electronic spreadsheets. Management science. Software. Albright, S. Christian author. FO UPCEB CEBU-LAHUG T 57.62 W56 2019 Book