A buyer for a large department store chain must place orders with an athletic shoe manufacturer 6 months prior to the time the shoes will be sold in the department stores. In particular, the buyer must decide on November 1 how many pairs of the manufacturer's newest model of tennis shoes to order for sale during the upcoming summer season.

Assume that each pair of this new brand of tennis shoes costs the department store chain $45 per pair. Furthermore, assume that each pair of these shoes can then be sold to the chain's customers for $70 per pair. Any pairs of these shoes remaining unsold at the end of the summer season will be sold in a closeout sale next fall for $35 each. Finally, assume that the department store chain must purchase these tennis shoes from the manufacturer in lots of 100 pairs.

The probability distribution of consumer demand for these tennis shoes (in hundreds of pairs) during the upcoming summer season has been assessed by market research specialists and is provided in the table below.

Consumer Demand	Probability
1	0.05
2	0.15
3	0.25
4	0.30
5	0.15
6	0.10

a) Formulate a payoff table that specifies the contribution to profit (in dollars) from the sale of the tennis shoes by this department store chain for each possible purchase decision (in hundreds of pairs) and each outcome with respect to consumer demand

Instead of considering possible demands of 100 to 1000, limit your model to 500, 600, 700 or 800, with probabilities .2, .3, .4, .1, respectively. When you have a large number of payoffs to compute using thesame model, it can save time to develop a value model formula which can be copied, including usingabsolute addressing where appropriate. Try to use such a formula to take advantage of this.

b) After building the payoff matrix model in part (a):

• What is the optimal decision using the EMV criterion?
• Compute the opportunity cost matrix.
• Find the optimal strategy using the minimax regret criterion.d) What is the optimal strategy using the EOL criterion?
• Compute the EVPI.