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Test Bank for Practical Management Science 6th by Winston

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Test Bank for Practical Management Science 6th by Winston

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Product Details:

  • ISBN-10 ‏ : ‎ 1337406651
  • ISBN-13 ‏ : ‎ 978-1337406659
  • Author:  Wayne L. Winston

Learn to take full advantage of the power of spreadsheet modeling with PRACTICAL MANAGEMENT SCIENCE, 6E, geared entirely to Excel 2016. This edition uses an active-learning approach and realistic problems with the right amount of theory to ensure you establish a strong foundation. Exercises offer practical, hands-on experience with the methodologies. Examples and problems from finance, marketing, and operations management, and other areas of business illustrate how management science applies to your chosen profession — and how you can use these skills on the job. The authors emphasize modeling rather than algebraic formulations and memorization of particular models. This edition also includes access to Palisade DecisionTools Suite (BigPicture, @RISK, PrecisionTree, StatTools, TopRank, NeuralTools, and Evolver) as well as SolverTable, for sensitivity analysis on optimization models. Chapters 15-17 are available online via MindTap.

 

Table of Content:

  1. Chapter 1: Introduction to Modeling
  2. 1.1 Introduction
  3. 1.2 A Capital Budgeting Example
  4. 1.3 Modeling versus Models
  5. 1.4 A Seven-Step Modeling Process
  6. 1.5 A Great Source for Management Science Applications: Interfaces
  7. 1.6 Why Study Management Science?
  8. 1.7 Software Included with This Book
  9. 1.8 Conclusion
  10. Chapter 2: Introduction to Spreadsheet Modeling
  11. 2.1 Introduction
  12. 2.2 Basic Spreadsheet Modeling: Concepts and Best Practices
  13. 2.3 Cost Projections
  14. 2.4 Breakeven Analysis
  15. 2.5 Ordering with Quantity Discounts and Demand Uncertainty
  16. 2.6 Estimating the Relationship between Price and Demand
  17. 2.7 Decisions Involving the Time Value of Money
  18. 2.8 Conclusion
  19. Appendix Tips for Editing and Documenting Spreadsheets
  20. Case 2.1 Project Selection at Ewing Natural Gas
  21. Case 2.2 New Product Introduction at eTech
  22. Chapter 3: Introduction to Optimization Modeling
  23. 3.1 Introduction
  24. 3.2 Introduction to Optimization
  25. 3.3 A Two-Variable Product Mix Model
  26. 3.4 Sensitivity Analysis
  27. 3.5 Properties of Linear Models
  28. 3.6 Infeasibility and Unboundedness
  29. 3.7 A Larger Product Mix Model
  30. 3.8 A Multiperiod Production Model
  31. 3.9 A Comparison of Algebraic and Spreadsheet Models
  32. 3.10 A Decision Support System
  33. 3.11 Conclusion
  34. Appendix Information on Optimization Software
  35. Case 3.1 Shelby Shelving
  36. Chapter 4: Linear Programming Models
  37. 4.1 Introduction
  38. 4.2 Advertising Models
  39. 4.3 Employee Scheduling Models
  40. 4.4 Aggregate Planning Models
  41. 4.5 Blending Models
  42. 4.6 Production Process Models
  43. 4.7 Financial Models
  44. 4.8 Data Envelopment Analysis (DEA)
  45. 4.9 Conclusion
  46. Case 4.1 Blending Aviation Gasoline at Jansen Gas
  47. Case 4.2 Delinquent Accounts at GE Capital
  48. Case 4.3 Foreign Currency Trading
  49. Chapter 5: Network Models
  50. 5.1 Introduction
  51. 5.2 Transportation Models
  52. 5.3 Assignment Models
  53. 5.4 Other Logistics Models
  54. 5.5 Shortest Path Models
  55. 5.6 Network Models in the Airline Industry
  56. 5.7 Conclusion
  57. Case 5.1 Optimized Motor Carrier Selection at Westvaco
  58. Chapter 6: Optimization Models with Integer Variables
  59. 6.1 Introduction
  60. 6.2 Overview of Optimization with Integer Variables
  61. 6.3 Capital Budgeting Models
  62. 6.4 Fixed-Cost Models
  63. 6.5 Set-Covering and Location-Assignment Models
  64. 6.6 Cutting Stock Models
  65. 6.7 Conclusion
  66. Case 6.1 Giant Motor Company
  67. Case 6.2 Selecting Telecommunication Carriers to Obtain Volume Discounts
  68. Case 6.3 Project Selection at Ewing Natural Gas
  69. Chapter 7: Nonlinear Optimization Models
  70. 7.1 Introduction
  71. 7.2 Basic Ideas of Nonlinear Optimization
  72. 7.3 Pricing Models
  73. 7.4 Advertising Response and Selection Models
  74. 7.5 Facility Location Models
  75. 7.6 Models for Rating Sports Teams
  76. 7.7 Portfolio Optimization Models
  77. 7.8 Estimating the Beta of a Stock
  78. 7.9 Conclusion
  79. Case 7.1 GMS Stock Hedging
  80. Chapter 8: Evolutionary Solver: An Alternative Optimization Procedure
  81. 8.1 Introduction
  82. 8.2 Introduction to Genetic Algorithms
  83. 8.3 Introduction to Evolutionary Solver
  84. 8.4 Nonlinear Pricing Models
  85. 8.5 Combinatorial Models
  86. 8.6 Fitting an S-Shaped Curve
  87. 8.7 Portfolio Optimization
  88. 8.8 Optimal Permutation Models
  89. 8.9 Conclusion
  90. Case 8.1 Assigning MBA Students to Teams
  91. Case 8.2 Project Selection at Ewing Natural Gas
  92. Chapter 9: Decision Making under Uncertainty
  93. 9.1 Introduction
  94. 9.2 Elements of Decision Analysis
  95. 9.3 Single-Stage Decision Problems
  96. 9.4 The PrecisionTree Add-In
  97. 9.5 Multistage Decision Problems
  98. 9.6 The Role of Risk Aversion
  99. 9.7 Conclusion
  100. Case 9.1 Jogger Shoe Company
  101. Case 9.2 Westhouser Paper Company
  102. Case 9.3 Electronic Timing System for Olympics
  103. Case 9.4 Developing a Helicopter Component for the Army
  104. Chapter 10: Introduction to Simulation Modeling
  105. 10.1 Introduction
  106. 10.2 Probability Distributions for Input Variables
  107. 10.3 Simulation and the Flaw of Averages
  108. 10.4 Simulation with Built-in Excel Tools
  109. 10.5 Introduction to @RISK
  110. 10.6 The Effects of Input Distributions on Results
  111. 10.7 Conclusion
  112. Appendix Learning More About @RISK
  113. Case 10.1 Ski Jacket Production
  114. Case 10.2 Ebony Bath Soap
  115. Case 10.3 Advertising Effectiveness
  116. Case 10.4 New Product Introduction at eTech
  117. Chapter 11: Simulation Models
  118. 11.1 Introduction
  119. 11.2 Operations Models
  120. 11.3 Financial Models
  121. 11.4 Marketing Models
  122. 11.5 Simulating Games of Chance
  123. 11.6 Conclusion
  124. Appendix Other Palisade Tools for Simulation
  125. Case 11.1 College Fund Investment
  126. Case 11.2 Bond Investment Strategy
  127. Case 11.3 Project Selection Ewing Natural Gas
  128. Chapter 12: Queueing Models
  129. 12.1 Introduction
  130. 12.2 Elements of Queueing Models
  131. 12.3 The Exponential Distribution
  132. 12.4 Important Queueing Relationships
  133. 12.5 Analytic Steady-State Queueing Models
  134. 12.6 Queueing Simulation Models
  135. 12.7 Conclusion
  136. Case 12.1 Catalog Company Phone Orders
  137. Chapter 13: Regression and Forecasting Models
  138. 13.1 Introduction
  139. 13.2 Overview of Regression Models
  140. 13.3 Simple Regression Models
  141. 13.4 Multiple Regression Models
  142. 13.5 Overview of Time Series Models
  143. 13.6 Moving Averages Models
  144. 13.7 Exponential Smoothing Models
  145. 13.8 Conclusion
  146. Case 13.1 Demand for French Bread at Howie’s Bakery
  147. Case 13.2 Forecasting Overhead at Wagner Printers
  148. Case 13.3 Arrivals at the Credit Union
  149. Chapter 14: Data Mining
  150. 14.1 Introduction
  151. 14.2 Classification Methods
  152. 14.3 Clustering Methods
  153. 14.4 Conclusion
  154. Case 14.1 Houston Area Survey
  155. References
  156. Index

 

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