Practical Management Science 5th Edition Winston Solutions Manual

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Practical Management Science 5th Edition Winston Solutions Manual.

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Practical Management Science 5th Edition Winston Solutions Manual

Product details:

  • ISBN-10 ‏ : ‎ 1305631544
  • ISBN-13 ‏ : ‎ 978-1305631540
  • Author: Wayne L. Winston

Geared entirely to Excel 2013, PRACTICAL MANAGEMENT SCIENCE uses an active-learning approach and realistic problems to help you take full advantage of the power of spreadsheet modeling. The text presents just the right amount of theory to ensure you understand the foundation of the topic, followed by exercises that give you practical, hands-on experience with the methodologies. Drawing examples and problems from finance, marketing, operations management, and other areas, the text illustrates how management science applies to your chosen profession–and how you can use it on the job. The authors emphasize modeling over algebraic formulations and memorization of particular models. The text includes access to Palisade DecisionTools Suite (BigPicture, @RISK, PrecisionTree, StatTools, TopRank, NeuralTools, and Evolver) as well as SolverTable, which allows you to do sensitivity analysis on optimization models.

Table contents:

  1. Ch 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. Ch 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. Ch 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 Solvers
  35. Case 3.1: Shelby Shelving
  36. Case 3.2: Sonoma Valley Wines
  37. Ch 4: Linear Programming Models
  38. 4.1 Introduction
  39. 4.2 Advertising Models
  40. 4.3 Employee Scheduling Models
  41. 4.4 Aggregate Planning Models
  42. 4.5 Blending Models
  43. 4.6 Production Process Models
  44. 4.7 Financial Models
  45. 4.8 Data Envelopment Analysis (DEA)
  46. 4.9 Conclusion
  47. Case 4.1: Blending Aviation Gasoline at Jansen Gas
  48. Case 4.2: Delinquent Accounts at GE Capital
  49. Case 4.3: Foreign Currency Trading
  50. Ch 5: Network Models
  51. 5.1 Introduction
  52. 5.2 Transportation Models
  53. 5.3 Assignment Models
  54. 5.4 Other Logistics Models
  55. 5.5 Shortest Path Models
  56. 5.6 Network Models in the Airline Industry
  57. 5.7 Conclusion
  58. Case 5.1: International Textile Company, Ltd.
  59. Case 5.2: Optimized Motor Carrier Selection at Westvaco
  60. Ch 6: Optimization Models with Integer Variables
  61. 6.1 Introduction
  62. 6.2 Overview of Optimization with Integer Variables
  63. 6.3 Capital Budgeting Models
  64. 6.4 Fixed-Cost Models
  65. 6.5 Set-Covering and Location-Assignment Models
  66. 6.6 Cutting Stock Models
  67. 6.7 Conclusion
  68. Case 6.1: Giant Motor Company
  69. Case 6.2: Selecting Telecommunication Carriers to Obtain Volume Discounts
  70. Case 6.3: Project Selection at Ewing Natural Gas
  71. Ch 7: Nonlinear Optimization Models
  72. 7.1 Introduction
  73. 7.2 Basic Ideas of Nonlinear Optimization
  74. 7.3 Pricing Models
  75. 7.4 Advertising Response and Selection Models
  76. 7.5 Facility Location Models
  77. 7.6 Models for Rating Sports Teams
  78. 7.7 Portfolio Optimization Models
  79. 7.8 Estimating the Beta of a Stock
  80. 7.9 Conclusion
  81. Case 7.1: GMS Stock Hedging
  82. Ch 8: Evolutionary Solver: An Alternative Optimization Procedure
  83. 8.1 Introduction
  84. 8.2 Introduction to Genetic Algorithms
  85. 8.3 Introduction to Evolutionary Solver
  86. 8.4 Nonlinear Pricing Models
  87. 8.5 Combinatorial Models
  88. 8.6 Fitting an S-Shaped Curve
  89. 8.7 Portfolio Optimization
  90. 8.8 Cluster Analysis
  91. 8.9 Discriminant Analysis
  92. 8.10 The Traveling Salesperson Problem
  93. 8.11 Conclusion
  94. Case 8.1: Assigning MBA Students to Teams
  95. Case 8.2: Project Selection at Ewing Natural Gas
  96. Ch 9: Decision Making under Uncertainty
  97. 9.1 Introduction
  98. 9.2 Elements of Decision Analysis
  99. 9.3 One-Stage Decision Problems
  100. 9.4 The Precisiontree Add-In
  101. 9.5 Multistage Decision Problems
  102. 9.6 The Role of Risk Aversion
  103. 9.7 Conclusion
  104. Case 9.1: Jogger Shoe Company
  105. Case 9.2: Westhouser Paper Company
  106. Case 9.3: Electronic Timing System for Olympics
  107. Case 9.4: Developing a Helicopter Component for the Army
  108. Ch 10: Introduction to Simulation Modeling
  109. 10.1 Introduction
  110. 10.2 Probability Distributions for Input Variables
  111. 10.3 Simulation and the Flaw of Averages
  112. 10.4 Simulation with Built-In Excel Tools
  113. 10.5 Introduction to @Risk
  114. 10.6 The Effects of Input Distributions on Results
  115. 10.7 Conclusion
  116. Appendix Learning More about @Risk
  117. Case 10.1: Ski Jacket Production
  118. Case 10.2: Ebony Bath Soap
  119. Case 10.3: Advertising Effectiveness
  120. Case 10.4: New Product Introduction at eTech
  121. Ch 11: Simulation Models
  122. 11.1 Introduction
  123. 11.2 Operations Models
  124. 11.3 Financial Models
  125. 11.4 Marketing Models
  126. 11.5 Simulating Games of Chance
  127. 11.6 Conclusion
  128. Appendix Other Palisade Tools for Simulation
  129. Case 11.1: College Fund Investment
  130. Case 11.2: Bond Investment Strategy
  131. Case 11.3: Project Selection at Ewing Natural Gas
  132. Ch 12: Inventory and Supply Chain Models
  133. 12.1 Introduction
  134. 12.2 Categories of Inventory and Supply Chain Models
  135. 12.3 Types of Costs in Inventory and Supply Chain Models
  136. 12.4 Economic Order Quantity (EOQ) Models
  137. 12.5 Probabilistic Inventory Models
  138. 12.6 Ordering Simulation Models
  139. 12.7 Supply Chain Models
  140. 12.8 Conclusion
  141. Case 12.1: Subway Token Hoarding
  142. Ch 13: Queueing Models
  143. 13.1 Introduction
  144. 13.2 Elements of Queueing Models
  145. 13.3 The Exponential Distribution
  146. 13.4 Important Queueing Relationships
  147. 13.5 Analytic Steady-State Queueing Models
  148. 13.6 Queueing Simulation Models
  149. 13.7 Conclusion
  150. Case 13.1: Catalog Company Phone Orders
  151. Case 13.2: Pacific National Bank
  152. Ch 14: Regression and Forecasting Models
  153. 14.1 Introduction
  154. 14.2 Overview of Regression Models
  155. 14.3 Simple Regression Models
  156. 14.4 Multiple Regression Models
  157. 14.5 Overview of Time Series Models
  158. 14.6 Moving Averages Models
  159. 14.7 Exponential Smoothing Models
  160. 14.8 Conclusion
  161. Case 14.1: Demand for French Bread at Howie’s Bakery
  162. Case 14.2: Forecasting Overhead at Wagner Printers
  163. Case 14.3: Arrivals at the Credit Union
  164. References
  165. Index

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