OCR MEI D2 2016 June — Question 3 20 marks

Exam BoardOCR MEI
ModuleD2 (Decision Mathematics 2)
Year2016
SessionJune
Marks20
PaperDownload PDF ↗
Mark schemeDownload PDF ↗
TopicLinear Programming
TypeConstraint derivation verification
DifficultyStandard +0.8 This is a multi-part linear programming question requiring constraint interpretation, simplex algorithm application, and reformulation. While the simplex mechanics are standard for Further Maths/Decision modules, the question demands conceptual understanding of why the initial formulation is flawed and how to correct it. The constraint interpretation and recognition that maximizing total paint isn't Neil's actual objective (he wants maximum expensive paint subject to painting all walls) requires problem-solving insight beyond routine application. This is moderately challenging but still within typical Further Maths scope.
Spec7.06a LP formulation: variables, constraints, objective function7.06b Slack variables: converting inequalities to equations7.06c Working with constraints: algebra and ad hoc methods7.06d Graphical solution: feasible region, two variables7.07a Simplex tableau: initial setup in standard format7.07b Simplex iterations: pivot choice and row operations7.07c Interpret simplex: values of variables, slack, and objective

3 Neil is refurbishing a listed building. There are two types of paint that he can use for the inside walls. One costs \(\pounds 1.45\) per \(\mathrm { m } ^ { 2 }\) and the other costs \(\pounds 0.95\) per \(\mathrm { m } ^ { 2 }\). He must paint the lower half of each wall in the more expensive paint. He has \(350 \mathrm {~m} ^ { 2 }\) of wall to paint. He has a budget of \(\pounds 400\) for wall paint. The more expensive paint is easier to use, and so Neil wants to use as much of it as possible. Initially, the following LP is constructed to help Neil with his purchasing of paint.
Let \(x\) be the number of \(\mathrm { m } ^ { 2 }\) of wall painted with the expensive paint.
Let \(y\) be the number of \(\mathrm { m } ^ { 2 }\) of wall painted with the less expensive paint. $$\begin{array} { l l } \text { Maximise } & P = x + y \\ \text { subject to } & 1.45 x + 0.95 y \leqslant 400 \\ & y - x \leqslant 0 \\ & x \geqslant 0 \\ & y \geqslant 0 \end{array}$$
  1. Explain the purpose of the inequality \(y - x \leqslant 0\).
  2. The formulation does not include the inequality \(x + y \geqslant 350\). State what this constraint models and why it has been omitted from the formulation.
  3. Use the simplex algorithm to solve the LP. Pivot first on the "1" in the \(y\) column. Interpret your solution. The solution shows that Neil needs to buy more paint. He negotiates an increase in his budget to \(\pounds 450\).
  4. Find the solution to the LP given by changing \(1.45 x + 0.95 y \leqslant 400\) to \(1.45 x + 0.95 y \leqslant 450\), and interpret your solution. Neil realises that although he now has a solution, that solution is not the best for his requirements.
  5. Explain why the revised solution is not optimal for Neil. In order to move to an optimal solution Neil needs to change the objective of the LP and add another constraint to it.
  6. Write down the new LP and the initial tableau for using two-stage simplex to solve it. Give a brief description of how to use two-stage simplex to solve it. \includegraphics[max width=\textwidth, alt={}, center]{d254fbd2-7443-4b6d-87ba-f0d71fce5e17-5_497_558_269_751}
    1. Solve the route inspection problem in the network above, showing the methodology you used to ensure that your solution is optimal. Show your route.
    2. Floyd's algorithm is applied to the same network to find the complete network of shortest distances. After three iterations the distance and route matrices are as follows.
      \cline { 2 - 6 } \multicolumn{1}{c|}{}\(\mathbf { 1 }\)\(\mathbf { 2 }\)\(\mathbf { 3 }\)\(\mathbf { 4 }\)\(\mathbf { 5 }\)
      \(\mathbf { 1 }\)4824281115
      \(\mathbf { 2 }\)24841116
      \(\mathbf { 3 }\)2848712

Question 3:
Part (i) [1 mark]
AnswerMarks Guidance
AnswerMarks Guidance
It ensures the area of cheaper paint (\(y\)) does not exceed the area of expensive paint (\(x\)), i.e. at least half of each wall is painted with expensive paintB1
Part (ii) [2 marks]
AnswerMarks Guidance
AnswerMarks Guidance
\(x + y \geq 350\) models the requirement to paint all 350 m²B1
It is omitted because it is implied/automatically satisfied by the objective of maximising \(x+y\) with the budget constraintB1
Part (iii) [7 marks]
AnswerMarks Guidance
AnswerMarks Guidance
Initial tableau set up correctly with slack variablesB1
Pivot on "1" in \(y\) column correctly identifiedM1
Correct row operations performedM1 A1
Second pivot identified and performed correctlyM1 A1
Solution: \(x = 125, y = 125, P = 250\) (or similar correct values from tableau)A1 With correct interpretation
Part (iv) [2 marks]
AnswerMarks Guidance
AnswerMarks Guidance
Change RHS from 400 to 450; new solution read from updated tableauM1
\(x \approx 136.4, y \approx 136.4\) giving \(P \approx 272.7\) m²A1 With interpretation
Part (v) [1 mark]
AnswerMarks Guidance
AnswerMarks Guidance
The solution has \(x = y\) meaning no more expensive paint is used than cheap paint; Neil wants to maximise use of expensive paint so wants \(x\) as large as possibleB1
Part (vi) [7 marks]
AnswerMarks Guidance
AnswerMarks Guidance
New objective: maximise \(x\)B1
Add constraint \(x + y = 350\) (or \(\geq 350\))B1
Artificial variable introduced for new equality constraintB1
Stage 1: minimise artificial variable; correct initial tableau shownB1 M1
Correct tableau structure with all variables labelledA1
Description: Stage 1 minimises sum of artificials; if minimum = 0, feasible solution found; Stage 2 optimises real objectiveB1
# Question 3:

## Part (i) [1 mark]

| Answer | Marks | Guidance |
|--------|-------|----------|
| It ensures the area of cheaper paint ($y$) does not exceed the area of expensive paint ($x$), i.e. at least half of each wall is painted with expensive paint | B1 | |

## Part (ii) [2 marks]

| Answer | Marks | Guidance |
|--------|-------|----------|
| $x + y \geq 350$ models the requirement to paint all 350 m² | B1 | |
| It is omitted because it is implied/automatically satisfied by the objective of maximising $x+y$ with the budget constraint | B1 | |

## Part (iii) [7 marks]

| Answer | Marks | Guidance |
|--------|-------|----------|
| Initial tableau set up correctly with slack variables | B1 | |
| Pivot on "1" in $y$ column correctly identified | M1 | |
| Correct row operations performed | M1 A1 | |
| Second pivot identified and performed correctly | M1 A1 | |
| Solution: $x = 125, y = 125, P = 250$ (or similar correct values from tableau) | A1 | With correct interpretation |

## Part (iv) [2 marks]

| Answer | Marks | Guidance |
|--------|-------|----------|
| Change RHS from 400 to 450; new solution read from updated tableau | M1 | |
| $x \approx 136.4, y \approx 136.4$ giving $P \approx 272.7$ m² | A1 | With interpretation |

## Part (v) [1 mark]

| Answer | Marks | Guidance |
|--------|-------|----------|
| The solution has $x = y$ meaning no more expensive paint is used than cheap paint; Neil wants to maximise use of expensive paint so wants $x$ as large as possible | B1 | |

## Part (vi) [7 marks]

| Answer | Marks | Guidance |
|--------|-------|----------|
| New objective: maximise $x$ | B1 | |
| Add constraint $x + y = 350$ (or $\geq 350$) | B1 | |
| Artificial variable introduced for new equality constraint | B1 | |
| Stage 1: minimise artificial variable; correct initial tableau shown | B1 M1 | |
| Correct tableau structure with all variables labelled | A1 | |
| Description: Stage 1 minimises sum of artificials; if minimum = 0, feasible solution found; Stage 2 optimises real objective | B1 | |

---
3 Neil is refurbishing a listed building. There are two types of paint that he can use for the inside walls. One costs $\pounds 1.45$ per $\mathrm { m } ^ { 2 }$ and the other costs $\pounds 0.95$ per $\mathrm { m } ^ { 2 }$. He must paint the lower half of each wall in the more expensive paint. He has $350 \mathrm {~m} ^ { 2 }$ of wall to paint. He has a budget of $\pounds 400$ for wall paint. The more expensive paint is easier to use, and so Neil wants to use as much of it as possible.

Initially, the following LP is constructed to help Neil with his purchasing of paint.\\
Let $x$ be the number of $\mathrm { m } ^ { 2 }$ of wall painted with the expensive paint.\\
Let $y$ be the number of $\mathrm { m } ^ { 2 }$ of wall painted with the less expensive paint.

$$\begin{array} { l l } 
\text { Maximise } & P = x + y \\
\text { subject to } & 1.45 x + 0.95 y \leqslant 400 \\
& y - x \leqslant 0 \\
& x \geqslant 0 \\
& y \geqslant 0
\end{array}$$

(i) Explain the purpose of the inequality $y - x \leqslant 0$.\\
(ii) The formulation does not include the inequality $x + y \geqslant 350$. State what this constraint models and why it has been omitted from the formulation.\\
(iii) Use the simplex algorithm to solve the LP. Pivot first on the "1" in the $y$ column. Interpret your solution.

The solution shows that Neil needs to buy more paint. He negotiates an increase in his budget to $\pounds 450$.\\
(iv) Find the solution to the LP given by changing $1.45 x + 0.95 y \leqslant 400$ to $1.45 x + 0.95 y \leqslant 450$, and interpret your solution.

Neil realises that although he now has a solution, that solution is not the best for his requirements.\\
(v) Explain why the revised solution is not optimal for Neil.

In order to move to an optimal solution Neil needs to change the objective of the LP and add another constraint to it.\\
(vi) Write down the new LP and the initial tableau for using two-stage simplex to solve it. Give a brief description of how to use two-stage simplex to solve it.\\
\includegraphics[max width=\textwidth, alt={}, center]{d254fbd2-7443-4b6d-87ba-f0d71fce5e17-5_497_558_269_751}
\begin{enumerate}[label=(\alph*)]
\item Solve the route inspection problem in the network above, showing the methodology you used to ensure that your solution is optimal. Show your route.
\item Floyd's algorithm is applied to the same network to find the complete network of shortest distances. After three iterations the distance and route matrices are as follows.

\begin{center}
\begin{tabular}{ | c | c | c | c | c | c | }
\cline { 2 - 6 }
\multicolumn{1}{c|}{} & $\mathbf { 1 }$ & $\mathbf { 2 }$ & $\mathbf { 3 }$ & $\mathbf { 4 }$ & $\mathbf { 5 }$ \\
\hline
$\mathbf { 1 }$ & 48 & 24 & 28 & 11 & 15 \\
\hline
$\mathbf { 2 }$ & 24 & 8 & 4 & 11 & 16 \\
\hline
$\mathbf { 3 }$ & 28 & 4 & 8 & 7 & 12 \\
\hline
\end{tabular}
\end{center}
\end{enumerate}

\hfill \mbox{\textit{OCR MEI D2 2016 Q3 [20]}}