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Two-stage Simplex and big-M methodsEdexcel A-Level Further Maths: Revision notes

Section 1

Why artificial variables cause trouble

For a ≤\le constraint the slack variable gives a ready-made starting solution. A ≥\ge or == constraint does not: for x+2y≥6x+2y\ge6 the equation x+2y−s2=6x+2y-s_2=6 at x=y=0x=y=0 gives s2=−6s_2=-6, which is not allowed. So we add an artificial variable tt: x+2y−s2+t=6x+2y-s_2+t=6, which starts with t=6t=6. But tt has no meaning in the real problem. A genuine solution must have t=0t=0. The big-M and two-stage methods are two ways of forcing artificial variables out of the solution. Summary: ≤\le gives a slack variable; ≥\ge gives a surplus and an artificial variable; == gives an artificial variable.

Key termsartificial variablesurplus variable
Common mistake

Adding a surplus variable and nothing else: the origin is then infeasible so the Simplex algorithm cannot start.

Section 2

The big-M method

Use one objective that penalises artificial variables. For a maximising problem subtract MtMt, where MM is a very large positive number: P=3x+4y−Mt,i.e.P−3x−4y+Mt=0.P=3x+4y-Mt,\quad\text{i.e.}\quad P-3x-4y+Mt=0. Since tt is basic, its column must be a unit column, so eliminate tt from the PP row: subtract M×M\times the tt row. For x+y+s1=12x+y+s_1=12 and x+2y−s2+t=6x+2y-s_2+t=6: P row: −3−M, −4−2M, 0, M, 0∣−6M.P\text{ row: }-3-M,\ -4-2M,\ 0,\ M,\ 0\mid-6M. Choose the pivot column by comparing the coefficients of MM first (the most negative for large MM): here yy. Ratios: 121=12\frac{12}{1}=12 and 62=3\frac62=3, so the tt row is the pivot row.

Key termsbig-M method
Common mistake

Forgetting to eliminate tt from the PP row before the first iteration.

Exam tip

Keep MM as a symbol, and compare the coefficients of MM first when choosing the pivot column.

Section 3

Completing the big-M example

After the first pivot (tt row, yy column) the yy row is 12,1,0,−12,12∣3\frac12,1,0,-\frac12,\frac12\mid3 and the PP row is −1,0,0,−2,2+M∣12-1,0,0,-2,2+M\mid12. Now tt has left the basis and MM appears only in the tt column. The most negative entry is −2-2 (the s2s_2 column). Only the s1s_1 row has a positive entry (12\frac12, ratio 18), so it is the pivot row. The result is the PP row 1,0,4,0,M∣481,0,4,0,M\mid48, with no negatives. So x=0x=0, y=12y=12, P=48P=48 (check: x+y=12x+y=12, x+2y=24≥6x+2y=24\ge6). If the final tableau still has t>0t>0, the problem has no feasible solution.

Exam tip

Once tt is non-basic you can ignore its column; MM in the tt column no longer affects the pivot choice.

Section 4

The two-stage method

Stage 1 ignores profit and just finds a feasible start. Define I=tI=t (the sum of the artificial variables) and minimise it, i.e. maximise Q=−tQ=-t. Eliminate tt to get the QQ row. For x+y+s1=10x+y+s_1=10 and 3x+y−s2+t=63x+y-s_2+t=6: Q−3x−y+s2=−6Q-3x-y+s_2=-6. Carry the PP row (written as usual) through stage 1 so it stays correct.

  • If the minimum of II is 0: a basic feasible solution exists, so go to stage 2.
  • If the minimum of II is positive: no feasible solution. Stage 2: delete the QQ row and the artificial column(s), and continue Simplex on the original PP row until no negatives remain.
Key termstwo-stage methodstage 1
Common mistake

Dropping the PP row in stage 1: it must be updated by the same row operations.

Exam tip

In stage 1 the objective row to pivot on is the QQ row, not the PP row.

Section 5

Two-stage worked example and minimising

Maximise P=2x+5yP=2x+5y subject to x+y≤10x+y\le10 and 3x+y≥63x+y\ge6. Stage 1: pivot on the 3 in the tt row (xx column; ratios 10 and 2), giving x=2x=2 and Q=0Q=0. The PP row becomes 0,−133,0,−23∣40,-\frac{13}{3},0,-\frac23\mid4. Stage 2: yy enters (ratios 6 and 12, so the xx row leaves), then s2s_2 enters (the s1s_1 row leaves). Final PP row 3,0,5,0∣503,0,5,0\mid50: x=0x=0, y=10y=10, P=50P=50. Minimising: maximise P=−CP=-C. For minimise C=2x+3yC=2x+3y with x+y≥4x+y\ge4 and x+3y≤12x+3y\le12, use P=−2x−3y−MtP=-2x-3y-Mt (big-M) or Q=−tQ=-t then P=−CP=-C (two-stage). The result is x=4x=4, y=0y=0 and C=8C=8.

Exam tip

Big-M keeps one tableau with a symbolic MM; two-stage uses only numbers, which is easier to calculate by hand.

That's the notes covered.

Carry on to the next subtopic.

Exam questions on Two-stage Simplex and big-M methods

  1. A maximising problem has the constraint 2x+y≥82x+y\ge8, to which a surplus variable ss and an artificial variable tt are added. The objective is P=3x+2yP=3x+2y and the big-M method is used, where MM is a very large positive number.
    Explain the purpose of the term −Mt-Mt, and what it means if t>0t>0 in the final optimal tableau.2 marks
  2. A maximising problem has the constraints x+y≤6x+y\le6 and x+y≥9x+y\ge9, with x,y≥0x,y\ge0. It is solved using the two-stage Simplex method, with a slack variable s1s_1 in the first constraint and a surplus variable s2s_2 and an artificial variable tt in the second.
    Explain, without using the Simplex algorithm, why the stage 1 result is correct.2 marks
  3. A company makes xx tonnes of product X and yy tonnes of product Y. It wishes to maximise the profit P=3x+4yP=3x+4y subject to x+y≤12x+y\le12 and x+2y≥6x+2y\ge6, with x≥0x\ge0 and y≥0y\ge0. The big-M method is used, with slack variable s1s_1, surplus variable s2s_2 and artificial variable tt. Tableau columns are in the order xx, yy, s1s_1, s2s_2, tt, then the value.
    Write the constraints as equations and write down the objective function for the big-M method.3 marks
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Written by the Exaim team, led by Shaun Daswani (Head of Upper Secondary, Improve ME Institute; MSc Financial Mathematics, Imperial College London; BSc, UCL) and Jason Daswani (operational lead, Improve ME Institute; LSE).