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

7. Example-2 (using `Z_j-C_j` method)
(Previous example)
9. Algorithm (using `C_j-Z_j`method)
(Next example)

8. Example-3 (using `Z_j-C_j` method)





Find solution using dual simplex method
MAX Z = -15x1 - 10x2
subject to
-3x1 - 5x2 <= -5
-5x1 - 2x2 <= -3
and x1,x2 >= 0;


Solution:

Note: This is dual simplex method, which is different from primal problem to dual problem conversion method
Calculator link to find Primal problem to dual problem conversion method





Problem is
Max `Z``=`` - ``15``x_1`` - ``10``x_2`
subject to
` - ``3``x_1`` - ``5``x_2``-5`
` - ``5``x_1`` - ``2``x_2``-3`
and `x_1,x_2 >= 0; `


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 add slack variable `S_2`

After introducing slack variables
Max `Z``=`` - ``15``x_1`` - ``10``x_2`` + ``0``S_1`` + ``0``S_2`
subject to
` - ``3``x_1`` - ``5``x_2`` + ````S_1`=`-5`
` - ``5``x_1`` - ``2``x_2`` + ````S_2`=`-3`
and `x_1,x_2,S_1,S_2 >= 0`


Tableau-1`C_j``-15``-10``0``0`
`C_B``"Basis"``x_1``x_2``S_1``S_2``RHS`
`R_1` `0``S_1``-3``(-5)``1``0``-5``->`
`R_2` `0``S_2``-5``-2``0``1``-3`
`Z_j``0``0``0``0``Z=0`
`Z_j-C_j``15``10``0``0`
`"Ratio"=(Z_j-C_j)/(S_1,j)`
and `S_1,j<0`
`(15)/(-3)`
`=-5`
`(10)/(-5)`
`=-2``uarr`
`(0)/(1)`
`=`---
`(0)/(0)`
`=`---


Most negative `RHS` is `-5` and its row index is `1`. So, the leaving basis variable is `S_1`.

Least negative ratio is `-2` and its column index is `2`. So, the entering variable is `x_2`.

`:.` The pivot element is `-5`.

Entering `=x_2`, Departing `=S_1`, Key Element `=-5`
`R_1`(new)`= R_1`(old) `-: -5`
`x_1``x_2``S_1``S_2``RHS`
`R_1`(old) = `-3``-5``1``0``-5`
`R_1`(new)`= R_1`(old) `-: -5``0.6``1``-0.2``0``1`
`R_2`(new)`= R_2`(old) + `2 R_1`(new)
`x_1``x_2``S_1``S_2``RHS`
`R_2`(old) = `-5``-2``0``1``-3`
`R_1`(new) = `0.6``1``-0.2``0``1`
`2 xx R_1`(new) = `1.2``2``-0.4``0``2`
`R_2`(new)`= R_2`(old) + `2 R_1`(new)`-3.8``0``-0.4``1``-1`


Tableau-2`C_j``-15``-10``0``0`
`C_B``"Basis"``x_1``x_2``S_1``S_2``RHS`
`R_1` `-10``x_2``0.6``1``-0.2``0``1`
`R_2` `0``S_2``(-3.8)``0``-0.4``1``-1``->`
`Z_j``-6``-10``2``0``Z=-10`
`Z_j-C_j``9``0``2``0`
`"Ratio"=(Z_j-C_j)/(S_2,j)`
and `S_2,j<0`
`(9)/(-3.8)`
`=-2.3684``uarr`
`(0)/(0)`
`=`---
`(2)/(-0.4)`
`=-5`
`(0)/(1)`
`=`---


Most negative `RHS` is `-1` and its row index is `2`. So, the leaving basis variable is `S_2`.

Least negative ratio is `-2.3684` and its column index is `1`. So, the entering variable is `x_1`.

`:.` The pivot element is `-3.8`.

Entering `=x_1`, Departing `=S_2`, Key Element `=-3.8`
`R_2`(new)`= R_2`(old) `-: -3.8`
`x_1``x_2``S_1``S_2``RHS`
`R_2`(old) = `-3.8``0``-0.4``1``-1`
`R_2`(new)`= R_2`(old) `-: -3.8``1``0``0.1053``-0.2632``0.2632`
`R_1`(new)`= R_1`(old) - `0.6 R_2`(new)
`x_1``x_2``S_1``S_2``RHS`
`R_1`(old) = `0.6``1``-0.2``0``1`
`R_2`(new) = `1``0``0.1053``-0.2632``0.2632`
`0.6 xx R_2`(new) = `0.6``0``0.0632``-0.1579``0.1579`
`R_1`(new)`= R_1`(old) - `0.6 R_2`(new)`0``1``-0.2632``0.1579``0.8421`


Tableau-3`C_j``-15``-10``0``0`
`C_B``"Basis"``x_1``x_2``S_1``S_2``RHS`
`R_1` `-10``x_2``0``1``-0.2632``0.1579``0.8421`
`R_2` `-15``x_1``1``0``0.1053``-0.2632``0.2632`
`Z_j``-15``-10``1.0526``2.3684``Z=-12.3684`
`Z_j-C_j``0``0``1.0526``2.3684`
Ratio


Since all `Z_j-C_j >= 0` and all `RHS >= 0`, thus the current solution is the optimal solution.

Hence, optimal solution is arrived with value of variables as :
`x_1=0.2632,x_2=0.8421`

Max `Z=-12.3684`




This material is intended as a summary. Use your textbook for detail explanation.
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7. Example-2 (using `Z_j-C_j` method)
(Previous example)
9. Algorithm (using `C_j-Z_j`method)
(Next example)





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