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9. Revised Simplex method example ( Enter your problem )
  1. Standard form-1 : Example-1
  2. Standard form-1 : Example-2
  3. Standard form-1 : Example-3
  4. Standard form-2 using Two-Phase method : Example-1
  5. Standard form-2 using Two-Phase method : Example-2
  6. Standard form-2 using Two-Phase method : Example-3
  7. Standard form-2 using Big M method : Example-1
  8. Standard form-2 using Big M method : Example-2
  9. Standard form-2 using Big M method : Example-3
Other related methods
  1. Formulate linear programming model
  2. Graphical method
  3. Simplex method (BigM method)
  4. Two-Phase method
  5. Primal to dual conversion
  6. Dual simplex method
  7. Integer simplex method
  8. Branch and Bound method
  9. 0-1 Integer programming problem
  10. Revised Simplex method

2. Standard form-1 : Example-2
(Previous example)
4. Standard form-2 using Two-Phase method : Example-1
(Next example)

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`


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`


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.
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2. Standard form-1 : Example-2
(Previous example)
4. Standard form-2 using Two-Phase method : Example-1
(Next example)





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