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