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

Decision Science MCQs

  

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