1 1 Slide © 2005 Thomson/South-Western Linear Programming: The Simplex Method n An Overview of the Simplex Method n Standard Form n Tableau Form n Setting Up the Initial Simplex Tableau n Improving the Solution n Calculating the Next Tableau n Solving a Minimization Problem n Special Cases

2 2 Slide © 2005 Thomson/South-Western Overview of the Simplex Method n Steps Leading to the Simplex Method FormulateProblem as LP FormulateProblem Put In StandardForm StandardForm TableauForm TableauFormExecuteSimplexMethodExecuteSimplexMethod

3 3 Slide © 2005 Thomson/South-Western Simplex Tableau n The simplex tableau is a convenient means for performing the calculations required by the simplex method.

4 4 Slide © 2005 Thomson/South-Western Setting Up Initial Simplex Tableau n Step 1: If the problem is a minimization problem, multiply the objective function by -1. multiply the objective function by -1. n Step 2: If the problem formulation contains any constraints with negative right-hand sides, constraints with negative right-hand sides, multiply each constraint by -1. multiply each constraint by -1. n Step 3: Add a slack variable to each < constraint. n Step 4: Subtract a surplus variable and add an artificial variable to each > constraint. artificial variable to each > constraint.

5 5 Slide © 2005 Thomson/South-Western Setting Up Initial Simplex Tableau n Step 5: Add an artificial variable to each = constraint. n Step 6: Set each slack and surplus variable's coefficient in the objective function equal to coefficient in the objective function equal to zero. zero. n Step 7: Set each artificial variable's coefficient in the objective function equal to - M, where M is a objective function equal to - M, where M is a very large number. very large number. n Step 8: Each slack and artificial variable becomes one of the basic variables in the initial basic of the basic variables in the initial basic feasible solution. feasible solution.

6 6 Slide © 2005 Thomson/South-Western Simplex Method n Step 1: Determine Entering Variable Identify the variable with the most positive value in the c j - z j row. (The entering column is called the pivot column.) Identify the variable with the most positive value in the c j - z j row. (The entering column is called the pivot column.) n Step 2: Determine Leaving Variable For each positive number in the entering column, compute the ratio of the right-hand side values divided by these entering column values. For each positive number in the entering column, compute the ratio of the right-hand side values divided by these entering column values. If there are no positive values in the entering column, STOP; the problem is unbounded. If there are no positive values in the entering column, STOP; the problem is unbounded. Otherwise, select the variable with the minimal ratio. (The leaving row is called the pivot row.) Otherwise, select the variable with the minimal ratio. (The leaving row is called the pivot row.)

7 7 Slide © 2005 Thomson/South-Western Simplex Method n Step 3: Generate Next Tableau Divide the pivot row by the pivot element (the entry at the intersection of the pivot row and pivot column) to get a new row. We denote this new row as (row *). Divide the pivot row by the pivot element (the entry at the intersection of the pivot row and pivot column) to get a new row. We denote this new row as (row *). Replace each non-pivot row i with: Replace each non-pivot row i with: [new row i ] = [current row i ] - [( a ij ) x (row *)], [new row i ] = [current row i ] - [( a ij ) x (row *)], where a ij is the value in entering column j of row i where a ij is the value in entering column j of row i

8 8 Slide © 2005 Thomson/South-Western Simplex Method n Step 4: Calculate z j Row for New Tableau For each column j, multiply the objective function coefficients of the basic variables by the corresponding numbers in column j and sum them. For each column j, multiply the objective function coefficients of the basic variables by the corresponding numbers in column j and sum them.

9 9 Slide © 2005 Thomson/South-Western Simplex Method n Step 5: Calculate c j - z j Row for New Tableau For each column j, subtract the z j row from the c j row. For each column j, subtract the z j row from the c j row. If none of the values in the c j - z j row are positive, GO TO STEP 1. If none of the values in the c j - z j row are positive, GO TO STEP 1. If there is an artificial variable in the basis with a positive value, the problem is infeasible. STOP. If there is an artificial variable in the basis with a positive value, the problem is infeasible. STOP. Otherwise, an optimal solution has been found. The current values of the basic variables are optimal. The optimal values of the non-basic variables are all zero. Otherwise, an optimal solution has been found. The current values of the basic variables are optimal. The optimal values of the non-basic variables are all zero. If any non-basic variable's c j - z j value is 0, alternate optimal solutions might exist. STOP. If any non-basic variable's c j - z j value is 0, alternate optimal solutions might exist. STOP.

10 Slide © 2005 Thomson/South-Western Example: Simplex Method n Solve the following problem by the simplex method: Max 12 x x x 3 Max 12 x x x 3 s.t. 2 x x x 3 < 50 s.t. 2 x x x 3 < 50 x 1 - x 2 - x 3 > 0 x 1 - x 2 - x 3 > 0 x x 3 > 0 x x 3 > 0 x 1, x 2, x 3 > 0 x 1, x 2, x 3 > 0

11 Slide © 2005 Thomson/South-Western n Writing the Problem in Tableau Form We can avoid introducing artificial variables to the second and third constraints by multiplying each by -1 (making them < constraints). Thus, slack variables s 1, s 2, and s 3 are added to the three constraints. Max 12 x x x s s s 3 Max 12 x x x s s s 3 s.t. 2 x x x 3 + s 1 = 50 s.t. 2 x x x 3 + s 1 = 50 - x 1 + x 2 + x 3 + s 2 = 0 - x 1 + x 2 + x 3 + s 2 = 0 - x x 3 + s 3 = 0 - x x 3 + s 3 = 0 x 1, x 2, x 3, s 1, s 2, s 3 > 0 x 1, x 2, x 3, s 1, s 2, s 3 > 0 Example: Simplex Method

12 Slide © 2005 Thomson/South-Western Example: Simplex Method n Initial Simplex Tableau x 1 x 2 x 3 s 1 s 2 s 3 x 1 x 2 x 3 s 1 s 2 s 3 Basis c B Basis c B s s s (* row) s (* row) s s z j z j c j - z j c j - z j

13 Slide © 2005 Thomson/South-Western Example: Simplex Method n Iteration 1 Step 1: Determine the Entering Variable Step 1: Determine the Entering Variable The most positive c j - z j = 18. Thus x 2 is the entering variable. Step 2: Determine the Leaving Variable Step 2: Determine the Leaving Variable Take the ratio between the right hand side and positive numbers in the x 2 column: 50/3 = 16 2/3 50/3 = 16 2/3 0/1 = 0 minimum 0/1 = 0 minimum s 2 is the leaving variable and the 1 is the pivot element. s 2 is the leaving variable and the 1 is the pivot element.

14 Slide © 2005 Thomson/South-Western Example: Simplex Method n Iteration 1 (continued) Step 3: Generate New Tableau Step 3: Generate New Tableau Divide the second row by 1, the pivot element. Call the "new" (in this case, unchanged) row the "* row". Divide the second row by 1, the pivot element. Call the "new" (in this case, unchanged) row the "* row". Subtract 3 x (* row) from row 1. Subtract 3 x (* row) from row 1. Subtract -1 x (* row) from row 3. Subtract -1 x (* row) from row 3. New rows 1, 2, and 3 are shown in the upcoming tableau. New rows 1, 2, and 3 are shown in the upcoming tableau.

15 Slide © 2005 Thomson/South-Western Example: Simplex Method n Iteration 1 (continued) Step 4: Calculate z j Row for New Tableau Step 4: Calculate z j Row for New Tableau The new z j row values are obtained by multiplying the c B column by each column, element by element and summing. For example, z 1 = 5(0) + -1(18) + -1(0) = -18. For example, z 1 = 5(0) + -1(18) + -1(0) = -18.

16 Slide © 2005 Thomson/South-Western Example: Simplex Method n Iteration 1 (continued) Step 5: Calculate c j - z j Row for New Tableau Step 5: Calculate c j - z j Row for New Tableau The new c j - z j row values are obtained by subtracting z j value in a column from the c j value in the same column. For example, c 1 - z 1 = 12 - (-18) = 30.

17 Slide © 2005 Thomson/South-Western Example: Simplex Method n Iteration 1 (continued) - New Tableau x 1 x 2 x 3 s 1 s 2 s 3 x 1 x 2 x 3 s 1 s 2 s 3 Basis c B Basis c B s (* row) s (* row) x x s s z j z j c j - z j c j - z j

18 Slide © 2005 Thomson/South-Western Example: Simplex Method n Iteration 2 Step 1: Determine the Entering Variable Step 1: Determine the Entering Variable The most positive c j - z j = 30. x 1 is the entering variable. Step 2: Determine the Leaving Variable Step 2: Determine the Leaving Variable Take the ratio between the right hand side and positive numbers in the x 1 column: 10/5 = 2 minimum 10/5 = 2 minimum There are no ratios for the second and third rows because their column elements (-1) are negative. There are no ratios for the second and third rows because their column elements (-1) are negative. Thus, s 1 (corresponding to row 1) is the leaving Thus, s 1 (corresponding to row 1) is the leaving variable and 5 is the pivot element. variable and 5 is the pivot element.

19 Slide © 2005 Thomson/South-Western Example: Simplex Method n Iteration 2 (continued) Step 3: Generate New Tableau Step 3: Generate New Tableau Divide row 1 by 5, the pivot element. (Call this new row 1 the "* row"). Subtract (-1) x (* row) from the second row. Subtract (-1) x (* row) from the second row. Subtract (-1) x (* row) from the third row. Subtract (-1) x (* row) from the third row. Step 4: Calculate z j Row for New Tableau Step 4: Calculate z j Row for New Tableau The new z j row values are obtained by multiplying the c B column by each column, element by element and summing. The new z j row values are obtained by multiplying the c B column by each column, element by element and summing. For example, z 3 =.2(12) + 1.2(18) +.2(0) = 24. For example, z 3 =.2(12) + 1.2(18) +.2(0) = 24.

20 Slide © 2005 Thomson/South-Western Example: Simplex Method n Iteration 2 (continued) Step 5: Calculate c j - z j Row for New Tableau Step 5: Calculate c j - z j Row for New Tableau The new c j - z j row values are obtained by subtracting z j value in a column from the c j value in the same column. For example, c 3 -z 3 = 10 - (24) = -14. For example, c 3 -z 3 = 10 - (24) = -14. Since there are no positive numbers in the c j - z j row, this tableau is optimal. The optimal solution is: x 1 = 10; x 2 = 10; x 3 = 0; s 1 = 0; s 2 = 0 s 3 = 10, and the optimal value of the objective function is 300. Since there are no positive numbers in the c j - z j row, this tableau is optimal. The optimal solution is: x 1 = 10; x 2 = 10; x 3 = 0; s 1 = 0; s 2 = 0 s 3 = 10, and the optimal value of the objective function is 300.

21 Slide © 2005 Thomson/South-Western Example: Simplex Method n Iteration 2 (continued) – Final Tableau x 1 x 2 x 3 s 1 s 2 s 3 x 1 x 2 x 3 s 1 s 2 s 3 Basis c B Basis c B x (* row) x (* row) x x s s z j z j c j - z j c j - z j

22 Slide © 2005 Thomson/South-Western Special Cases n Infeasibility n Unboundedness n Alternative Optimal Solution n Degeneracy

23 Slide © 2005 Thomson/South-Western Infeasibility n Infeasibility is detected in the simplex method when an artificial variable remains positive in the final tableau.

24 Slide © 2005 Thomson/South-Western Example: Infeasibility n LP Formulation MAX 2 x x 2 MAX 2 x x 2 s. t. 4 x x 2 < 12 s. t. 4 x x 2 < 12 2 x 1 + x 2 > 8 2 x 1 + x 2 > 8 x 1, x 2 > 0 x 1, x 2 > 0

25 Slide © 2005 Thomson/South-Western Example: Infeasibility n Final Tableau x 1 x 2 s 1 s 2 a 2 x 1 x 2 s 1 s 2 a 2 BasisC B M BasisC B M x /4 1/4 003 a 2 - M 0 -1/2 -1/ z j 2 (1/2) M (1/2) M M - M -2 M +3/2 +1/2 +6 c j - z j 0 -(1/2) M -(1/2) M - M 0 +9/2 -1/2 c j - z j 0 -(1/2) M -(1/2) M - M 0 +9/2 -1/2

26 Slide © 2005 Thomson/South-Western Example: Infeasibility In the previous slide we see that the tableau is the final tableau because all c j - z j < 0. However, an artificial variable is still positive, so the problem is infeasible.

27 Slide © 2005 Thomson/South-Western Unboundedness n A linear program has an unbounded solution if all entries in an entering column are non-positive.

28 Slide © 2005 Thomson/South-Western Example: Unboundedness n LP Formulation MAX 2 x x 2 MAX 2 x x 2 s. t. 4 x x 2 > 12 s. t. 4 x x 2 > 12 2 x 1 + x 2 > 8 2 x 1 + x 2 > 8 x 1, x 2 > 0 x 1, x 2 > 0

29 Slide © 2005 Thomson/South-Western Example: Unboundedness n Final Tableau x 1 x 2 s 1 s 2 Basis c B Basis c B x s z j c j - z j c j - z j

30 Slide © 2005 Thomson/South-Western Example: Unboundedness In the previous slide we see that c 4 - z 4 = 4 (is positive), but its column is all non-positive. This indicates that the problem is unbounded.

31 Slide © 2005 Thomson/South-Western Alternative Optimal Solution n A linear program has alternate optimal solutions if the final tableau has a c j - z j value equal to 0 for a non- basic variable.

32 Slide © 2005 Thomson/South-Western Example: Alternative Optimal Solution n Final Tableau x 1 x 2 x 3 s 1 s 2 s 3 s 4 x 1 x 2 x 3 s 1 s 2 s 3 s 4 Basis c B Basis c B s s x x x x s s z j c j – z j c j – z j

33 Slide © 2005 Thomson/South-Western In the previous slide we see that the optimal solution is: x 1 = 4, x 2 = 6, x 3 = 0, and z = 32 x 1 = 4, x 2 = 6, x 3 = 0, and z = 32 Note that x 3 is non-basic and its c 3 - z 3 = 0. This 0 indicates that if x 3 were increased, the value of the objective function would not change. Another optimal solution can be found by choosing x 3 as the entering variable and performing one iteration of the simplex method. The new tableau on the next slide shows an alternative optimal solution is: x 1 = 7, x 2 = 0, x 3 = 3, and z = 32 x 1 = 7, x 2 = 0, x 3 = 3, and z = 32 Example: Alternative Optimal Solution

34 Slide © 2005 Thomson/South-Western Example: Alternative Optimal Solution n New Tableau x 1 x 2 x 3 s 1 s 2 s 3 s 4 Basis c B Basis c B s s x x x x s s z j z j c j - z j c j - z j

35 Slide © 2005 Thomson/South-Western Degeneracy n A degenerate solution to a linear program is one in which at least one of the basic variables equals 0. n This can occur at formulation or if there is a tie for the minimizing value in the ratio test to determine the leaving variable. n When degeneracy occurs, an optimal solution may have been attained even though some c j – z j > 0. n Thus, the condition that c j – z j < 0 is sufficient for optimality, but not necessary.

36 Slide © 2005 Thomson/South-Western End of Chapter 5