1. Define Operations Research.
2. Identify a key characteristic of Linear Programming.
3. Select the correct step in formulating an LPP.
4. Write the general form of an LPP objective function.
5. State a limitation of Linear Programming.
6. Match the following: Big-M Method is used for...
7. Recognize a managerial application of Operations Research.
8. Define Duality in Linear Programming.
9. Identify an example of a constraint in LPP.
10. Select the graphical method’s primary limitation.
11. Write a key assumption in LPP.
12. State when multiple optimal solutions occur in LPP.
13. Match the following: Degeneracy occurs when...
14. Recognize an advantage of the Simplex Method.
15. Define sensitivity analysis in LPP.
16. Identify the main goal of Integer Programming.
17. Select the primary characteristic of Zero-One Programming.
18. Write a scenario where Goal Programming is used.
19. State a major application of Duality.
20. Match the following: Infeasibility in LPP means...
21. Recognize a special case in LPP.
22. Define an optimal solution in LPP.
23. Identify the purpose of the Big-M Method.
24. Select the correct statement about Two-Phase Method.
25. Write the significance of the objective function in LPP.
26. State when an unbounded solution occurs in LPP.
27. Match the following: The feasible region represents...
28. Recognize the role of constraints in LPP.
29. Define the purpose of Operations Research.
30. Indicate the primary objective of solving a transportation problem.
31. Identify the first step in the North-West Corner Rule.
32. Infer the limitation of the Least Cost Method in transportation problems.
33. Compute the earliest start time for an activity given its predecessor’s earliest finish time.
34. Predict the impact of an unbalanced transportation problem.
35. Relate the Vogel’s Approximation Method (VAM) to optimization.
36. Represent a special case in transportation problems.
37. Identify when the Hungarian method is used.
38. Infer the condition for multiple solutions in a transportation problem.
39. Predict the result of applying the Least Cost Method to an unbalanced problem.
40. Relate the Modified Distribution Method (MODI) to the transportation problem.
41. Represent a method that ensures fairness in assignment problems.
42. Indicate the reason for using a dummy row/column in unbalanced transportation problems.
43. Identify which method considers opportunity cost for optimality.
44. Infer the type of problem solved by the Hungarian method.
45. Indicate the best method to find an optimal solution for a transportation problem.
46. Predict the impact of a maximization case in a transportation problem.
47. Relate the function of the stepping stone method.
48. Represent a scenario that requires modifying a transportation table.
49. Indicate why the Vogel’s Approximation Method is preferred over the North-West Corner Rule.
50. Identify the main assumption in transportation models.
51. Infer what happens if an allocation leads to degeneracy.
52. Predict the number of allocations needed for a non-degenerate basic feasible solution with m rows and n columns.
53. Relate the Hungarian method to solving optimization problems.
54. Indicate how the MODI method determines an optimal solution.
55. Identify the key difference between the Least Cost Method and Vogel’s Approximation Method.
56. Infer why degeneracy occurs in transportation problems.
57. Predict how an assignment problem with maximization objective is solved.
58. Relate the role of opportunity cost in the Hungarian method.
59. Indicate the primary advantage of using Vogel’s Approximation Method.
60. Identify the correct condition for applying the Hungarian method.
61. Calculate the total project duration using the critical path method (CPM).
62. Choose the primary purpose of a Work Breakdown Structure (WBS).
63. Solve for the earliest start time of an activity without predecessors.
64. Determine the float for a non-critical activity.
65. Establish the relationship between CBS and WBS.
66. Predict the impact of crashing on the project schedule.
67. Solve for the total float of an activity given its early and late start times.
68. Write the formula for calculating slack time in project scheduling.
69. Calculate the project’s critical path using network analysis.
70. Choose the best method for analyzing the impact of uncertainty in project scheduling.
71. Compute the expected project duration using PERT.
72. Determine the purpose of a Cost Breakdown Structure (CBS).
73. Establish the connection between OBS and WBS in project management.
74. Predict the result of resource levelling on a project schedule.
75. Solve for the latest finish time of an activity given its latest start time and duration.
76. Write the objective of crashing a project schedule.
77. Calculate the total float for an activity with known earliest and latest finish times.
78. Choose the main advantage of using PERT over CPM.
79. Establish the function of resource smoothing in project management.
80. Predict the effect of increasing activity duration on the project schedule.
81. Represent an issue that arises when using the North-West Corner Rule.
82. Write the formula for calculating variance in PERT estimation.
83. Calculate the latest start time of an activity given its latest finish and duration.
84. Choose the best method for reducing project duration while minimizing cost.
85. Identify the key factor affecting decision making under uncertainty.
86. Determine the total slack for an activity given its latest and earliest start times.
87. Calculate the variance of a PERT activity duration estimate.
88. Choose the correct method to handle uncertainty in project scheduling.
89. Indicate the first phase of decision making.
90. Identify the type of decision made under structured conditions with clear rules.
91. Establish the relationship between slack and the critical path.
92. Predict the result of not considering resource constraints in scheduling.
93. Infer the main characteristic of decision making under uncertainty.
94. Predict the impact of incorrect probability estimation in decision making.
95. Relate payoff table to decision making.
96. Represent the purpose of an opportunity loss table.
97. Indicate which decision phase involves evaluating different alternatives.
98. Infer why non-programmed decisions require more analysis.
99. Solve for the probability of project completion within a given time using PERT.
100. Write the formula for total project cost given normal and crash costs.
101. Predict the result of ignoring opportunity loss in decision making.
102. Relate tactical decisions to operational strategy.
103. Represent how decision trees assist in uncertain decision making.
104. Indicate the main use of a payoff table.
105. Identify a key limitation of decision making under risk.
106. Compute the earliest finish time of an activity given its earliest start and duration.
107. Infer the difference between programmed and non-programmed decisions.
108. Predict the consequence of poor data quality in decision making.
109. Relate decision trees to expected value calculations.
110. Represent a decision-making scenario with complete knowledge of outcomes.
111. Determine the critical path in a network diagram.
112. Indicate why decision making under uncertainty is challenging.
113. Identify the main component of an opportunity loss table.
114. Infer the importance of sensitivity analysis in decision making.
115. Predict the advantage of using decision trees.
116. Relate strategic decisions to organizational growth.
117. Represent the purpose of maximin decision criterion.
118. Indicate which decision rule is most conservative.
119. Identify when a decision maker uses the Hurwicz criterion.
120. Infer why expected monetary value is useful in decision making.
121. Predict the impact of misinterpreting opportunity loss tables.
122. Relate the maximax rule to risk-taking behavior.
123. Represent why probability assessment is crucial in decision making under risk.
124. Indicate the key factor distinguishing decisions under risk and uncertainty.
125. Identify a drawback of using the minimax regret criterion.
126. Infer when a decision maker would use the Laplace criterion.
127. Identify the meaning of \'M\' in M/M/1 queue.
128. Infer the impact of increasing the arrival rate in an M/M/1 queue.
129. Predict what happens if the service rate is lower than the arrival rate in an M/M/1 queue.
130. Indicate the queue discipline typically assumed in M/M/1 models.
131. Identify the key difference between M/M/1 and M/M/C queues.
132. Compute the standard deviation of a PERT activity duration.
133. Infer the role of the Poisson process in queuing models.
134. Predict the effect of increasing the number of servers in an M/M/C queue.
135. Represent a key characteristic of an M/M/C/K queue.
136. Indicate the primary goal of game theory.
137. Infer the condition for a saddle point in a two-person zero-sum game.
138. Predict the outcome of a game with a saddle point.
139. Relate the MinMax principle to decision-making.
140. Represent a scenario where dominance rule applies.
141. Indicate the main characteristic of a zero-sum game.
142. Identify the best strategy in a strictly determined game.
143. Infer the implication of the dominance rule.
144. Predict the impact of a mixed strategy in game theory.
145. Relate game theory to queuing models.
146. Represent the difference between pure and mixed strategies.
147. Indicate the significance of Nash equilibrium in game theory.
148. Identify the condition when a game has no saddle point.
149. Infer the importance of the payoff matrix.
150. Predict the impact of an additional server in an M/M/C queue.
151. Relate queuing theory to real-life applications.
152. Represent a key assumption of M/M/1 queues.
153. Indicate a real-world example of an M/M/C/K queue.
154. Identify the significance of the arrival rate in queuing models.
155. Infer why game theory is important in business strategy.