McqMate
These multiple-choice questions (MCQs) are designed to enhance your knowledge and understanding in the following areas: Uncategorized topics .
| 101. |
Given the following table that presents the solution for a queuing problem with a constant service rate, the probability that the server is idle is |
| A. | 0.217 |
| B. | 0.643 |
| C. | 0.286 |
| D. | 0.714 |
| Answer» C. 0.286 | |
| 102. |
Markov analysis is a technique that deals with the probabilities of future occurrences by |
| A. | using Bayes' theorem. |
| B. | analyzing presently known probabilities. |
| C. | time series forecasting. |
| D. | the maximal flow technique. |
| Answer» B. analyzing presently known probabilities. | |
| 103. |
Decision makers in queuing situations attempt to balance |
| A. | operating characteristics against the arrival rate. |
| B. | service levels against service cost. |
| C. | the number of units in the system against the time in the system. |
| D. | the service rate against the arrival rate. |
| Answer» B. service levels against service cost. | |
| 104. |
The manner in which units receive their service, such as FCFS, is the |
| A. | queue discipline. |
| B. | channel. |
| C. | steady state. |
| D. | operating characteristic. |
| Answer» A. queue discipline. | |
| 105. |
What queue discipline is assumed by the waiting line models presented in the textbook? |
| A. | first-come first-served. |
| B. | last-in first-out. |
| C. | shortest processing time first. |
| D. | No discipline is assumed. |
| Answer» A. first-come first-served. | |
| 106. |
In Markov analysis, we are concerned with the probability that the |
| A. | state is part of a system. |
| B. | system is in a particular state at a given time. |
| C. | time has reached a steady state. |
| D. | tr |
| Answer» C. time has reached a steady state. | |
| 107. |
For a situation with weekly dining at either an Italian or Mexican restaurant, |
| A. | the weekly visit is the trial and the restaurant is the state. |
| B. | the weekly visit is the state and the restaurant is the trial. |
| C. | the weekly visit is the trend and the restaurant is the transition. |
| D. | the weekly visit is the tr |
| Answer» A. the weekly visit is the trial and the restaurant is the state. | |
| 108. |
A transition probability describes |
| A. | the probability of a success in repeated, independent trials. |
| B. | the probability a system in a particular state now will be in a specific state next period. |
| C. | the probability of reaching an absorbing state. |
| D. | None of the alternatives is correct. |
| Answer» B. the probability a system in a particular state now will be in a specific state next period. | |
| 109. |
Performance measures dealing with the number of units in line and the time spent waiting are called |
| A. | queuing facts. |
| B. | performance queues. |
| C. | system measures. |
| D. | operating characteristic. |
| Answer» D. operating characteristic. | |
| 110. |
The probability of going from state 1 in period 2 to state 4 in period 3 is |
| A. | p12 |
| B. | p23 |
| C. | p14 |
| D. | p43 |
| Answer» C. p14 | |
| 111. |
The probability that a system is in a particular state after a large number of periods is |
| A. | independent of the beginning state of the system. |
| B. | dependent on the beginning state of the system. |
| C. | equal to one half. |
| D. | the same for every ending system. |
| Answer» A. independent of the beginning state of the system. | |
| 112. |
Analysis of a Markov process |
| A. | describes future behavior of the system. |
| B. | optimizes the system. |
| C. | leads to higher order decision making. |
| D. | All of the alternatives are true. |
| Answer» A. describes future behavior of the system. | |
| 113. |
If the probability of making a transition from a state is 0, then that state is called a(n) |
| A. | steady state. |
| B. | final state. |
| C. | origin state. |
| D. | absorbing state. |
| Answer» D. absorbing state. | |
| 114. |
Absorbing state probabilities are the same as |
| A. | steady state probabilities. |
| B. | transition probabilities. |
| C. | fundamental probabilities. |
| D. | None of the alternatives is true. |
| Answer» D. None of the alternatives is true. | |
| 115. |
Markov analysis might be effectively used for |
| A. | technology transfer studies. |
| B. | university retention analysis. |
| C. | accounts receivable analysis. |
| D. | all of the above |
| Answer» D. all of the above | |
| 116. |
The following is not an assumption of Markov analysis. |
| A. | There is an infinite number of possible states. |
| B. | The probability of changing states remains the same over time. |
| C. | (a) and (d) |
| D. | We can predict any future state from the previous state and the matrix of tr |
| Answer» B. The probability of changing states remains the same over time. | |
| 117. |
The total cost for a waiting line does NOT specifically depend on |
| A. | the cost of waiting. |
| B. | the cost of service. |
| C. | the number of units in the system. |
| D. | the cost of a lost customer. |
| Answer» D. the cost of a lost customer. | |
| 118. |
Markov analysis assumes that conditions are both |
| A. | complementary and collectively exhaustive. |
| B. | collectively dependent and complementary. |
| C. | collectively dependent and mutually exclusive. |
| D. | collectively exhaustive and mutually exclusive. |
| Answer» D. collectively exhaustive and mutually exclusive. | |
| 119. |
Occasionally, a state is entered which will not allow going to another state in the future. This is called |
| A. | an equilibrium state. |
| B. | stable mobility. |
| C. | market saturation. |
| D. | none of the above |
| Answer» D. none of the above | |
| 120. |
In Markov analysis, the likelihood that any system will change from one period to the next is revealed by the |
| A. | identity matrix. |
| B. | transition-elasticities. |
| C. | matrix of state probabilities. |
| D. | matrix of tr |
| Answer» B. transition-elasticities. | |
| 121. |
The condition that a system can be in only one state at any point in time is known as |
| A. | Transient state. |
| B. | Absorbent condition. |
| C. | Mutually exclusive condition. |
| D. | Collectively exhaustive condition. |
| Answer» C. Mutually exclusive condition. | |
| 122. |
At any period n, the state probabilities for the next period n+1 is given by the following formula: |
| A. | n(n+1)=n(n)Pn |
| B. | n(n+1)=n(0)P |
| C. | n(n+1)=(n+1)P |
| D. | n(n+1)=n(n)P |
| Answer» D. n(n+1)=n(n)P | |
| 123. |
If we decide to use Markov analysis to study the transfer of technology, |
| A. | our study will be methodologically flawed. |
| B. | our study will have only limited value because the Markov analysis tells us "what" will happen, but not "why." |
| C. | we can only study the transitions among three different technologies. |
| D. | only constant changes in the matrix of tr |
| Answer» B. our study will have only limited value because the Markov analysis tells us "what" will happen, but not "why." | |
| 124. |
The following data consists of a matrix of transition probabilities (P) of three competing companies, the initial market share state 16_10.gif(1), and the equilibrium probability states. Assume that each state represents a firm (Company 1, Company 2, and Company 3, respectively) and the transition probabilities represent changes from one month to the next. The market share of Company 1 in the next period is |
| A. | 0.10 |
| B. | 0.20 |
| C. | 0.42 |
| D. | 0.47 |
| Answer» D. 0.47 | |
| 125. |
Markov analysis assumes that the states are both __________ and __________. |
| A. | finite, recurrent |
| B. | infinite, absorbing |
| C. | generally inclusive, always independent |
| D. | collectively exhaustive, mutually exclusive |
| Answer» D. collectively exhaustive, mutually exclusive | |
| 126. |
A simulation model uses the mathematical expressions and logical relationships of the |
| A. | real system. |
| B. | computer model. |
| C. | performance measures. |
| D. | estimated inferences. |
| Answer» A. real system. | |
| 127. |
The ________ determine(s) the equilibrium of a Markov process. |
| A. | original state probabilities |
| B. | state vector |
| C. | transition matrix |
| D. | fundamental matrix F |
| Answer» C. transition matrix | |
| 128. |
Values for the probabilistic inputs to a simulation |
| A. | are selected by the decision maker. |
| B. | are controlled by the decision maker. |
| C. | are randomly generated based on historical information. |
| D. | are calculated by fixed mathematical formulas. |
| Answer» C. are randomly generated based on historical information. | |
| 129. |
A quantity that is difficult to measure with certainty is called a |
| A. | risk analysis. |
| B. | project determinant. |
| C. | probabilistic input. |
| D. | profit/loss process. |
| Answer» C. probabilistic input. | |
| 130. |
A value for probabilistic input from a discrete probability distribution |
| A. | is the value given by the RAND() function. |
| B. | is given by matching the probabilistic input with an interval of random numbers. |
| C. | is between 0 and 1. |
| D. | must be non-negative. |
| Answer» B. is given by matching the probabilistic input with an interval of random numbers. | |
| 131. |
The number of units expected to be sold is uniformly distributed between 300 and 500. If r is a random number between 0 and 1, then the proper expression for sales is |
| A. | 200(r) |
| B. | r + 300 |
| C. | 300 + 500(r) |
| D. | 300 + r(200) |
| Answer» D. 300 + r(200) | |
| 132. |
Common features of simulations--generating values from probability distributions, maintaining records, recording data and summarizing results--led to the development of |
| A. | Excel and Lotus. |
| B. | BASIC, FORTRAN, PASCAL, and C. |
| C. | GPSS, SIMSCRIPT, SLAM, and Arena |
| D. | LINDO and The Management Scientist |
| Answer» C. GPSS, SIMSCRIPT, SLAM, and Arena | |
| 133. |
In order to verify a simulation model |
| A. | compare results from several simulation languages. |
| B. | be sure that the procedures for calculations are logically correct. |
| C. | confirm that the model accurately represents the real system. |
| D. | run the model long enough to overcome initial start-up results. |
| Answer» B. be sure that the procedures for calculations are logically correct. | |
| 134. |
Simulation |
| A. | does not guarantee optimality. |
| B. | is flexible and does not require the assumptions of theoretical models. |
| C. | allows testing of the system without affecting the real system. |
| D. | All of the alternatives are correct. |
| Answer» D. All of the alternatives are correct. | |
| 135. |
A simulation model used in situations where the state of the system at one point in time does not affect the state of the system at future points in time is called a |
| A. | dynamic simulation model. |
| B. | static simulation model. |
| C. | steady-state simulation model. |
| D. | discrete-event simulation model. |
| Answer» B. static simulation model. | |
| 136. |
When events occur at discrete points in time |
| A. | a simulation clock is required. |
| B. | the simulation advances to the next event. |
| C. | the model is a discrete-event simulation. |
| D. | All of the alternatives are correct. |
| Answer» D. All of the alternatives are correct. | |
| 137. |
The process of determining that the computer procedure that performs the simulation calculations is logically correct is called |
| A. | implementation. |
| B. | validation. |
| C. | verification. |
| D. | repetition. |
| Answer» C. verification. | |
| 138. |
Numerical values that appear in the mathematical relationships of a model and are considered known and remain constant over all trials of a simulation are |
| A. | parameters. |
| B. | probabilistic input. |
| C. | controllable input. |
| D. | events. |
| Answer» A. parameters. | |
| 139. |
The word "uniform" in the term "uniform random numbers" means |
| A. | all the numbers have the same number of digits. |
| B. | if one number is, say, 10 units above the mean, the next number will be 10 units below the mean. |
| C. | all the numbers are odd or all are even. |
| D. | each number has an equal probability of being drawn. |
| Answer» D. each number has an equal probability of being drawn. | |
| 140. |
The first step in simulation is to |
| A. | set up possible courses of action for testing. |
| B. | construct a numerical model. |
| C. | validate the model. |
| D. | define the problem. |
| Answer» D. define the problem. | |
| 141. |
Which of the following are disadvantages of simulation? |
| A. | inability to analyze large and complex real-world situations |
| B. | "time compression" capability |
| C. | could be disruptive by interfering with the real-world system |
| D. | is not usually easily tr |
| Answer» A. inability to analyze large and complex real-world situations | |
| 142. |
Cumulative probabilities are found by |
| A. | summing all the probabilities associated with a variable. |
| B. | simulating the initial probability distribution. |
| C. | summing all the previous probabilities up to the current value of the variable. |
| D. | any method one chooses. |
| Answer» C. summing all the previous probabilities up to the current value of the variable. | |
| 143. |
Which of the following statements is INCORRECT regarding the advantages of simulation? |
| A. | Simulation is relatively easy to explain and understand. |
| B. | Simulation guarantees an optimal solution. |
| C. | Simulation models are flexible. |
| D. | A simulation model provides a convenient experimental laboratory for the real system. |
| Answer» B. Simulation guarantees an optimal solution. | |
| 144. |
If we are going to simulate an inventory problem, we must |
| A. | Run the simulation for many days. |
| B. | Run the simulation for many days many times, i.e., using multiple sets of random numbers. |
| C. | Run the simulation many times, i.e., using multiple sets of random numbers. |
| D. | Run the simulation once, for a relative short period of time. |
| Answer» B. Run the simulation for many days many times, i.e., using multiple sets of random numbers. | |
| 145. |
Simulation should be thought of as a technique for |
| A. | obtaining a relatively inexpensive solution to a problem. |
| B. | increasing one's understanding of a problem. |
| C. | obtaining an optimal solution to a problem. |
| D. | providing quick and dirty |
| Answer» C. obtaining an optimal solution to a problem. | |
| 146. |
In assigning random numbers in a Monte Carlo simulation, it is important to ________. |
| A. | develop cumulative probability distributions |
| B. | use random numbers from a random number table |
| C. | use only a single set of random numbers |
| D. | use Excel spreadsheets |
| Answer» A. develop cumulative probability distributions | |
| 147. |
To simulate is to try to __________ the features, appearance, and characteristics of a real system. |
| A. | Develop |
| B. | Analyze |
| C. | Multiply |
| D. | Duplicate |
| Answer» D. Duplicate | |
| 148. |
The three types of mathematical simulation models are |
| A. | operational gaming, Monte Carlo, systems simulation |
| B. | Monte Carlo, queuing, maintenance policy. |
| C. | Monte Carlo, systems simulation, computer gaming. |
| D. | system simulation, operational gaming, weather forecasting. |
| Answer» A. operational gaming, Monte Carlo, systems simulation | |
| 149. |
Which of the following as an assumption of an LP model |
| A. | Divisibility |
| B. | Proportionality |
| C. | Additively |
| D. | all of the above |
| Answer» D. all of the above | |
| 150. |
Most of the constraints in the linear programming problem are expressed as ………. |
| A. | Equality |
| B. | Inequality |
| C. | Uncertain |
| D. | all of the above |
| Answer» B. Inequality | |
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