MATH170-lecture-20211028

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Sensitivity analysis cont. #

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Recall previous example #

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So how can we identify the shadow price when we have artificial variables by looking at the last row of the simplex tableau?

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Notice that moving the upper constraint up does not change the optimal solution at point C C . But if you move the lower constraint up the optimal point will change.

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Correction: x5 x_5 replaced x2 x_2 , so the left hand side BV is labeled incorrect.

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We can ignore the artificial variable columns, and still consider z z optimal, because the w w equation equals 0 0 , and w=x5+x7 w = x_5 + x_7 .

So, the optimal solution to the original minimization LP problem is x1=10,x2=20 x_1 = 10, x_2 = 20 with z=50 z = 50 . Now let’s check the shadow prices and how they are related to the coefficients in the last row ( z z row), of the last simplex tableau.

Recall,

  • if it is a slack variable, we can simply multiply by 1 -1 to obtain shadow price for corresponding constraint

Since the coefficients of x3 x_3 and x8 x_8 are both 0, their shadow price is 0.

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Surplus variables in c2 c_2 and c3 c_3 : shadow price is the negative of the coefficients of the corresponding surplus variables.

  • So, for c2 c_2 the shadow price of a 1 unit increase is 12 \frac{1}{2} . This is obtained by multiplying the slack variable x4 x_4 by 1 -1 .
  • Similarly, for c3 c_3 , the shadow price of a 1 unit increase is 52 \frac{5}{2} .

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