3. Standard form-1 : Example-3
Find solution using Revised Simplex (BigM) method MAX Z = x1 + x2 + 3x3 subject to 3x1 + 2x2 + x3 <= 3 2x1 + x2 + 2x3 <= 2 and x1,x2,x3 >= 0
Solution: Problem is
Max `Z` | `=` | `` | `` | `x_1` | ` + ` | `` | `x_2` | ` + ` | `3` | `x_3` |
| subject to | `` | `3` | `x_1` | ` + ` | `2` | `x_2` | ` + ` | `` | `x_3` | ≤ | `3` | `` | `2` | `x_1` | ` + ` | `` | `x_2` | ` + ` | `2` | `x_3` | ≤ | `2` |
| and `x_1,x_2,x_3 >= 0; ` |
Step-1 : The problem is converted to canonical form by adding slack, surplus and artificial variables as appropiate
After introducing slack variables
`` | `3` | `x_1` | ` + ` | `2` | `x_2` | ` + ` | `` | `x_3` | ` + ` | `` | `S_1` | | | | = | `3` | `` | `2` | `x_1` | ` + ` | `` | `x_2` | ` + ` | `2` | `x_3` | | | | ` + ` | `` | `S_2` | = | `2` |
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The problem is converted to canonical form by adding slack, surplus and artificial variables as appropiate
After introducing slack variables
`` | `` | `Z'` | ` - ` | `` | `x_1` | ` - ` | `` | `x_2` | ` - ` | `3` | `x_3` | | | | | | | = | `0` | | | | `` | `3` | `x_1` | ` + ` | `2` | `x_2` | ` + ` | `` | `x_3` | ` + ` | `` | `S_1` | | | | = | `3` | | | | `` | `2` | `x_1` | ` + ` | `` | `x_2` | ` + ` | `2` | `x_3` | | | | ` + ` | `` | `S_2` | = | `2` |
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Now represent the new system of constraint equations in the matrix form `[[1,-1,-1,-3,0,0],[0,3,2,1,1,0],[0,2,1,2,0,1]][[Z'],[x_1],[x_2],[x_3],[S_1],[S_2]]=[[0],[3],[2]]`
or `[[1,-c],[0,A]][[Z],[x]]=[[0,b]]; x>=0`
where `e=beta_0,a_3=beta_1,a_4=beta_2`
Step-2 : The basis matrix `B_1` of order `(2+1)=3` can be expressed as
`B_1=[beta_0,beta_1,beta_2]=[[1,0,0],[0,1,0],[0,0,1]]`
Then, `B_1^(-1)=[[1,C_B B^(-1)],[0,B^(-1)]]=1; B=[[1,0],[0,1]]=[beta_1,beta_2]; C_B=[0,0]`
| | Basis Inverse `B_1^(-1)` | | | Additional table | `B` | `X_B` | `beta_0` `Z'` | `beta_1` `S_1` | `beta_2` `S_2` | `y_1` | Min Ratio `(X_B)/(y_1)` | `x_1` | `x_2` | `x_3` | `Z'` | `0` | `1` | `0` | `0` | | --- | `-1` | `-1` | `-3` | `S_1` | `3` | `0` | `1` | `0` | | --- | `3` | `2` | `1` | `S_2` | `2` | `0` | `0` | `1` | | --- | `2` | `1` | `2` |
Iteration=1 : Repeat steps 3 to 5 to get new solution Step-3: To select the vector corresponding to a non-basic variable to enter into the basis, we compute `z_k-c_k="Min" {(z_j-c_j)<0;}`
`="Min"{(1^(st)" row of " B_1^(-1)) ("Columns " a_j " not in basis")}`
`="Min"{[[1,0,0]] [[-1,-1,-3],[3,2,1],[2,1,2]]}`
`="Min"{[[-1,-1,-3]]}`
`=-3` (correspnds to `z_3-c_3`)
Thus, vector `x_3` is selected to enter into the basis, for `k=3`
Step-4: To select a basic variable to leave the basis, we compute `y_k` for k=3, as follows
`y_3= B_1^(-1) a_3=[[1,0,0],[0,1,0],[0,0,1]] [[-3],[1],[2]]=[[-3],[1],[2]]`
and `X_B = [[0],[3],[2]]`
Now, calculate the minimum ratio to select the basic variable to leave the basis `x_(Br)/y_(rk)= "Min" {x_(Bi)/y_(ik), y_(ik)>0}`
`="Min"{(3)/(1),(2)/(2)}`
`="Min"{3,1}`
`=1 ("correspnds to " x_(B2)/y_(23))`
Thus, vector `S_2` is selected to leave the basis, for `r=2`
The table with new entries in column `y_3` and the minimum ratio
| | Basis Inverse `B_1^(-1)` | | | Additional table | `B` | `X_B` | `beta_0` `Z'` | `beta_1` `S_1` | `beta_2` `S_2` | `y_3` | Min Ratio `(X_B)/(y_3)` | `x_1` | `x_2` | `x_3` | `Z'` | `0` | `1` | `0` | `0` | `-3` | --- | `-1` | `-1` | `-3` | `S_1` | `3` | `0` | `1` | `0` | `1` | `3` | `3` | `2` | `1` | `S_2` | `2` | `0` | `0` | `1` | `2` | `1` | `2` | `1` | `2` |
The table solution is now updated by replacing variable `S_2` with the variable `x_3` into the basis.
For this we apply the following row operations in the same way as in the simplex method
| `X_B` | `beta_1` | `beta_2` | `y_3` | `R_1` | `0` | `0` | `0` | `-3` | `R_2` | `3` | `1` | `0` | `1` | `R_3` | `2` | `0` | `1` | `2` |
`R_3`(new)`= R_3`(old)` -: 2``R_3`(old) = | `2` | | `0` | `1` | `R_3`(new)`= R_3`(old)` -: 2` | `1` | | `0` | `1/2` |
`R_1`(new)`= R_1`(old) + `3 R_3`(new)`R_1`(old) = | `0` | | `0` | `0` | `R_3`(new) = | `1` | | `0` | `1/2` | `3 xx R_3`(new) = | `3` | | `0` | `3/2` | `R_1`(new)`= R_1`(old) + `3 R_3`(new) | `3` | | `0` | `3/2` |
`R_2`(new)`= R_2`(old) - `R_3`(new)`R_2`(old) = | `3` | | `1` | `0` | `R_3`(new) = | `1` | | `0` | `1/2` | `R_2`(new)`= R_2`(old) - `R_3`(new) | `2` | | `1` | `-1/2` |
The improved solution is
| | Basis Inverse `B_1^(-1)` | | | Additional table | `B` | `X_B` | `beta_0` `Z'` | `beta_1` `S_1` | `beta_2` `x_3` | `y_3` | Min Ratio `(X_B)/(y_3)` | `x_1` | `x_2` | `S_2` | `Z'` | `3` | `1` | `0` | `3/2` | | --- | `-1` | `-1` | `0` | `S_1` | `2` | `0` | `1` | `-1/2` | | --- | `3` | `2` | `0` | `x_3` | `1` | `0` | `0` | `1/2` | | --- | `2` | `1` | `1` |
Iteration=2 : Repeat steps 3 to 5 to get new solution Step-3: To select the vector corresponding to a non-basic variable to enter into the basis, we compute `z_k-c_k="Min" {(z_j-c_j)<0;}`
`="Min"{(1^(st)" row of " B_1^(-1)) ("Columns " a_j " not in basis")}`
`="Min"{[[1,0,3/2]] [[-1,-1,0],[3,2,0],[2,1,1]]}`
`="Min"{[[2,1/2,3/2]]}`
Since all `Z_j-C_j >= 0`
Hence, optimal solution is arrived with value of variables as : `x_1=0,x_2=0,x_3=1`
Max `Z=3`
This material is intended as a summary. Use your textbook for detail explanation. Any bug, improvement, feedback then
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