Optimisation

1. Calculate C

2. Calculate X
3. Calculate D

4. Calculate XD the revenue.


The keystrokes are,


CQ(C)Q(=)aQ(A)pQ(F)RQ(B)
pQ(E)$Q(:)


Q(X)Q(=)aJ(C)J(B)pJ(A)R2J
(C)$Q(:)


J(C)(J(X)pJ(B))+J(A)Q(:)


J(X)M


We can now test this general model on the previous

data r.




A? 600= average number of tickets sold

F? 812= trial number of tickets sold
B? 10= original ticket price

E? 8.5= trial ticket price



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n =n






Critical price of ticket £

=n =






New ticket Demand Revenue from sale of

tickets £



You can now change the input values to suit the

appropriate you may have available. You will also
notice a slight discrepancy arising from rounding off in

the first set of calculations.




Example 10

Littleport United are playing at home

and the average number of tickets sold
is 380 at a cost of £7.50 (revenue

£2,850). For a trial period the ticket

price is reduced to £6.70 and the

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number of tickets sold increased to 450 (revenue

£3,015). Determine the optimum ticket price, the
expected number of tickets to be sold and the

revenue from the match.

Using the calculator as set up for the previous

example, r


A? 380= average number of tickets sold

F? 450= trial number of tickets sold
B? 7.5= original ticket price

E? 6.7= trial ticket price



n =n






Critical price of ticket £

=n =n







New ticket Demand Revenue from sale of

tickets £
The original revenue £2.850 compared to £3,068.

47

This completes this flipbook on optimisation using
your fx-991ES calculator. I hope you have found the

examples useful and you now have a better

understanding of how to create optimisation models.

You may be interested in other titles by the same

author.






















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