Posted: September 16th, 2017

decision tree

decision tree

The City of Vancouver is considering whether or not to replace its fleet of gasoline-powered automobiles with
electric cars (true!!!). The manufacturer of electric cars claims that the city will experience significant cost
savings over the life of the fleet if it chooses to pursue the conversion. If the manufacturer is correct, the city
will save an estimated $1.2 million dollars. If the new technology within the electric cars is faulty, as some
critics suggest, the conversion to electric cars will cost the city $725,000. A third possibility is that less serious
problems will arise and the city will break even with the conversion. A consultant hired by the city estimates
the probabilities of these 3 outcomes are 0.40, 0.30 and 0.30 respectively. The city has an opportunity to
implement a pilot program that would indicate the potential cost or savings resulting from a switch to electric
cars. The pilot program involves renting a small number of electric cars for 3 months and running them under
typical conditions. The pilot program would cost the city $60,000. The city’s consultant believes that the
results of the pilot program would be significant but not conclusive; she provides the city with the following
compilation of probabilities based on her past experience consulting with other cities under the same
conditions. According to the consultant, the reliability for the pilot program in the past has been:

Actual City Outcomes of Electric Car Conversions
Savings Loss Breakeven
Pilot Predicted: Savings 0.601 0.10 0.40
Pilot Predicted: Loss 0.102 0.40 0.20
Pilot Predicted: Breakeven 0.30 0.50 0.40
1
For example, 0.60 in the table above represents the probability of the pilot predicting a savings, given that a conversion
to electric cars actually resulted in a savings of $1.2 million in other cities. 2Likewise, 0.10 represents the probability of
the pilot predicting a loss, given that a conversion to electric cars actually resulted in a savings of $1.2 million in other
cities. etc

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a) Develop a decision tree for the City of Vancouver given the information above. Draw this decision tree by
hand and evaluate it using the EMV decision criterion. Provide a concluding statement with respect to the
optimal decision.
b) Use Treeplan.xla to evaluate the decision tree in part a). Print out your decision tree and include it with
your submission.
c) Would the optimal decision change if the pilot program only costs the city $35,000? If so, provide a
concluding statement with respect to the new optimal decision.