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3. Two-Phase method example ( Enter your problem )
  1. Algorithm & Example-1 (using `Z`-row method)
  2. Example-2 (using `Z`-row method)
  3. Example-3 (using `Z`-row method)
  4. Infeasible solution example (using `Z`-row method)
  5. Algorithm & Example-1 (using `Z_j-C_j` method)
  6. Example-2 (using `Z_j-C_j` method)
  7. Example-3 (using `Z_j-C_j` method)
  8. Infeasible solution example (using `Z_j-C_j` method)
  9. Algorithm & Example-1 (using `C_j-Z_j`method)
  10. Example-2 (using `C_j-Z_j`method)
  11. Example-3 (using `C_j-Z_j`method)
  12. Infeasible solution example (using `C_j-Z_j`method)
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

5. Algorithm & Example-1 (using `Z_j-C_j` method)
(Previous example)
7. Example-3 (using `Z_j-C_j` method)
(Next example)

6. Example-2 (using `Z_j-C_j` method)





Find solution using Two-Phase method
MIN Z = 5x1 + 2x2 + 10x3
subject to
x1 - x3 <= 10
x2 + x3 >= 10
and x1,x2,x3 >= 0;


Solution:
Problem is
Min `Z``=````5``x_1`` + ``2``x_2`` + ``10``x_3`
subject to
`````x_1`` - ````x_3``10`
`````x_2`` + ````x_3``10`
and `x_1,x_2,x_3 >= 0; `


-->Phase-1<--

The problem is converted to canonical form by adding slack, surplus and artificial variables as appropiate

1. As the constraint-1 is of type '`<=`' we should add slack variable `S_1`

2. As the constraint-2 is of type '`>=`' we should subtract surplus variable `S_2` and add artificial variable `A_1`

After introducing slack,surplus,artificial variables
Min `Z``=``````A_1`
subject to
`````x_1`` - ````x_3`` + ````S_1`=`10`
`````x_2`` + ````x_3`` - ````S_2`` + ````A_1`=`10`
and `x_1,x_2,x_3,S_1,S_2,A_1 >= 0`


Tableau-1`C_j``0``0``0``0``0``1`
`C_B``"Basis"``x_1``x_2``x_3``S_1``S_2``A_1``RHS``"Ratio"=(RHS)/(x_2)`
`R_1` `0``S_1``1``0``-1``1``0``0``10``(10)/(0)` (ignore, denominator is 0)
`R_2` `1``A_1``0``(1)``1``0``-1``1``10``(10)/(1)=10``->`
`Z_j``0``1``1``0``-1``1``Z=10`
`Z_j-C_j``0``1``uarr``1``0``-1``0`


Most Positive `Z_j-C_j` is `1`. So, the entering variable is `x_2`.

Minimum ratio is `10`. So, the leaving basis variable is `A_1`.

`:.` The pivot element is `1`.

Entering `=x_2`, Departing `=A_1`, Key Element `=1`
`R_2`(new)`= R_2`(old)
`x_1``x_2``x_3``S_1``S_2``A_1``RHS`
`R_2`(old) = `0``1``1``0``-1``1``10`
`R_2`(new)`= R_2`(old)`0``1``1``0``-1``1``10`
`R_1`(new)`= R_1`(old)
`x_1``x_2``x_3``S_1``S_2``A_1``RHS`
`R_1`(old) = `1``0``-1``1``0``0``10`
`R_1`(new)`= R_1`(old)`1``0``-1``1``0``0``10`


Tableau-2`C_j``0``0``0``0``0``1`
`C_B``"Basis"``x_1``x_2``x_3``S_1``S_2``A_1``RHS``"Ratio"`
`R_1` `0``S_1``1``0``-1``1``0``0``10`
`R_2` `0``x_2``0``1``1``0``-1``1``10`
`Z_j``0``0``0``0``0``0``Z=0`
`Z_j-C_j``0``0``0``0``0``-1`


Since all `Z_j-C_j <= 0`

Hence, optimal solution is arrived with value of variables as :
`x_1=0,x_2=10,x_3=0`

Min `Z=0`

-->Phase-2<--

we eliminate the artificial variables and change the objective function for the original,
Min `Z=5x_1 + 2x_2 + 10x_3 + 0S_1 + 0S_2`

Tableau-1`C_j``5``2``10``0``0`
`C_B``"Basis"``x_1``x_2``x_3``S_1``S_2``RHS``"Ratio"`
`R_1` `0``S_1``1``0``-1``1``0``10`
`R_2` `2``x_2``0``1``1``0``-1``10`
`Z_j``0``2``2``0``-2``Z=20`
`Z_j-C_j``-5``0``-8``0``-2`


Since all `Z_j-C_j <= 0`

Hence, optimal solution is arrived with value of variables as :
`x_1=0,x_2=10,x_3=0`

Min `Z=20`




This material is intended as a summary. Use your textbook for detail explanation.
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5. Algorithm & Example-1 (using `Z_j-C_j` method)
(Previous example)
7. Example-3 (using `Z_j-C_j` method)
(Next example)





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