Decision Science | Chapter 2 | Part 1 | MBA MCQs | DS

Decision Science MCQs

  

 

1. The basis for the transportation model is
   1.   a way to provide a map for people to see results
   2.   to provide data for use in other areas
   3.   so delivery drivers know where to go
   4.   a method to arrive at the lowest total shipping cost
2. The following transportation model is a programming model
   1.   analytical
   2.   linear
   3.   variable
   4.   non-linear
3. Before the analyst of the transportation model can begin, what data would they need to collect?
   1.   A list of destinations
   2.   Unit cost to ship
   3.   A list of origins
   4.   All of the above
4. What does the transportation problem involve finding:
   1.   highest cost-plan
   2.   lowest cost-plan
   3.   closest destinations
   4.   farthest destinations
5. Transportation problems be solved
   1.   manually
   2.   with a table
   3.   with excel
   4.   with software packages
   5.   all of the above
6. The objective function of the transportation model is to
   1.   reduce shipping costs
   2.   maximize costs
   3.   minimize costs
   4.   none of the above
7. Goods are not sent from
   1.   warehouses.
   2.   grocery stores
   3.   department stores
   4.   goods are sent from all of these locations
8. Goods are received at all of the following except
   1.   docks
   2.   departments
   3.   factories
   4.   warehouses
9. The method for finding the lowest-cost plan for distributing stocks of goods or supplies from multiple origins to multiple destinations that demand the goods is
   1.   transportation model analysis
   2.   MODI analysis
   3.   linear regression analysis
   4.   cost-volume analysis
10. Except to be used to minimized the costs associated with distributing good, transportation model can also be used in
    1.   production planning
    2.   all of the above
    3.   transshipment problem
    4.   comparison of location alternative
    5.   capacity planning
11. Which one of the following is a linear programming model ?
    1.   Cost-volume analysis
    2.   Transportation model analysis
    3.   MODI analysis
    4.   Linear regression analysis
12. Destination points are
    1.   points that receive goods from factories, warehouses, and departments.
    2.   supply points
    3.   points where goods are sent from factories, warehouses, and departments
    4.   none of the above
13. Transportation problems can be solved manually in a straightforward manner except for
    1.   medium problems
    2.   large problems
    3.   all of the above
    4.   very small, but time consuming problems
14. 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
    1.   0.10
    2.   0.20
    3.   0.42
    4.   0.47
15. Markov analysis assumes that the states are both __________ and __________.
    1.   finite, recurrent
    2.   collectively exhaustive, mutually exclusive
    3.   generally inclusive, always independent
    4.   infinite, absorbing
16. A simulation model uses the mathematical expressions and logical relationships of the
    1.   real system.
    2.   computer model.
    3.   performance measures.
    4.   estimated inferences.
17. The ________ determine(s) the equilibrium of a Markov process.
    1.   original state probabilities
    2.   transition matrix
    3.   fundamental matrix F
    4.   state vector
18. Values for the probabilistic inputs to a simulation
    1.   are selected by the decision maker.
    2.   are calculated by fixed mathematical formulas.
    3.   are randomly generated based on historical information.
    4.   are controlled by the decision maker.
19. A quantity that is difficult to measure with certainty is called a
    1.   risk analysis.
    2.   project determinant.
    3.   probabilistic input.
    4.   profit/loss process.
20. value for probabilistic input from a discrete probability distribution
    1.   is the value given by the RAND() function.
    2.   is given by matching the probabilistic input with an interval of random numbers.
    3.   is between 0 and 1.
    4.   must be non-negative.