Simplex algorithm execution

7 Consider the linear programming problem: $$\begin{array} { l l } \text { maximise } & P = 4 y - x , \\ \text { subject to } & x + 4 y \leqslant 22 , \\ & x + y \leqslant 10 , \\ & - x + 2 y \leqslant 8 , \\ \text { and } & x \geqslant 0 , y \geqslant 0 . \end{array}$$

1. Represent the constraints graphically, shading out the regions where the inequalities are not satisfied. Calculate the value of \(x\) and the value of \(y\) at each of the vertices of the feasible region. Hence find the maximum value of \(P\), clearly indicating where it occurs.
2. By introducing slack variables, represent the problem as an initial Simplex tableau and use the Simplex algorithm to solve the problem.
3. Indicate on your diagram for part (i) the points that correspond to each stage of the Simplex algorithm carried out in part (ii). \section*{OXFORD CAMBRIDGE AND RSA EXAMINATIONS} Advanced Subsidiary General Certificate of Education Advanced General Certificate of Education MATHEMATICS
   4736
   Decision Mathematics 1
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6. \includegraphics[max width=\textwidth, alt={}, center]{b1227633-913e-41a9-8bf8-1f064056963e-7_661_1004_285_557}
7. Upper bound = \(\_\_\_\_\) km Lower bound = \(\_\_\_\_\) km
8. \(\_\_\_\_\) Best upper bound = \(\_\_\_\_\) km 6
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   Least travel time = \(\_\_\_\_\) minutes Route: A- \(\_\_\_\_\) \(- D\)

7. A fitness centre runs introductory courses aimed at the following groups of customers: Pensioners, who will be charged \(\pounds 4\) for a 2 -hour session.
Other adults, who will be charged \(\pounds 10\) for a 4 -hour session.
Children, who will be charged \(\pounds 2\) for a 1 -hour session.
Let the number of pensioners, other adults, and children be \(x , y\) and \(z\) respectively.
Regulations state that the number of pensioners, \(x\), must be at most 5 more than the number of adults, \(y\). There must also be at least twice as many adults, \(y\), as there are children, \(z\). The centre is able to supervise up to 40 person-hours each day at the centre and wishes to maximise the revenue \(( \pounds R )\) that can be earned each day from these sessions. You may assume that the places on any courses that the centre runs will be filled.

1. Modelling this situation as a linear programming problem, write down the constraints and objective function in terms of \(x , y\) and \(z\). Using the Simplex algorithm, the following initial tableau is obtained.
2. \(\_\_\_\_\)

3 A farmer has 40 acres of land. Four crops, A, B, C and D are available.
Crop A will return a profit of \(\pounds 50\) per acre. Crop B will return a profit of \(\pounds 40\) per acre.
Crop C will return a profit of \(\pounds 40\) per acre. Crop D will return a profit of \(\pounds 30\) per acre.
The total number of acres used for crops A and B must not be greater than the total number used for crops C and D. The farmer formulates this problem as:
Maximise \(\quad 50 a + 40 b + 40 c + 30 d\),
subject to \(\quad a + b \leqslant 20\), \(a + b + c + d \leqslant 40\).

1. Explain what the variables \(a , b , c\) and \(d\) represent. Explain how the first inequality was obtained.
   Explain why expressing the constraint on the total area of land as an inequality will lead to a solution in which all of the land is used.
2. Solve the problem using the simplex algorithm. Suppose now that the farmer had formulated the problem as:
   Maximise \(\quad 50 a + 40 b + 40 c + 30 d\),
   subject to \(\quad a + b \leqslant 20\), \(a + b + c + d = 40\).
3. Show how to adapt this problem for solution either by the two-stage simplex method or the big-M method. In either case you should show the initial tableau and describe what has to be done next. You should not attempt to solve the problem.

The tableau below is the initial tableau for a maximising linear programming problem.

1. Write down the four equations represented in the initial tableau above. [4]
2. Taking the most negative number in the profit row to indicate the pivot column at each stage, solve this linear programming problem. State the row operations that you use. [9]
3. State the values of the objective function and each variable. [3]