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2. Simplex method example ( Enter your problem )
  1. Structure of Linear programming problem
  2. Algorithm (using `Z`-row method)
  3. Maximization example-1 (using `Z`-row method)
  4. Maximization example-2 (using `Z`-row method)
  5. Maximization example-3 (using `Z`-row method)
  6. BigM method Algorithm (using `Z`-row method)
  7. Minimization example-1 (using `Z`-row method)
  8. Minimization example-2 (using `Z`-row method)
  9. Minimization example-3 (using `Z`-row method)
  10. Degeneracy example-1 (Tie for leaving basic variable) (using `Z`-row method)
  11. Degeneracy example-2 (Tie first Artificial variable removed) (using `Z`-row method)
  12. Unrestricted variable example (using `Z`-row method)
  13. Multiple optimal solution example (using `Z`-row method)
  14. Infeasible solution example (using `Z`-row method)
  15. Unbounded solution example (using `Z`-row method)
  16. Algorithm (using `Z_j-C_j` method)
  17. Maximization example-1 (using `Z_j-C_j` method)
  18. Maximization example-2 (using `Z_j-C_j` method)
  19. Maximization example-3 (using `Z_j-C_j` method)
  20. BigM method Algorithm (using `Z_j-C_j` method)
  21. Minimization example-1 (using `Z_j-C_j` method)
  22. Minimization example-2 (using `Z_j-C_j` method)
  23. Minimization example-3 (using `Z_j-C_j` method)
  24. Degeneracy example-1 (Tie for leaving basic variable) (using `Z_j-C_j` method)
  25. Degeneracy example-2 (Tie first Artificial variable removed) (using `Z_j-C_j` method)
  26. Unrestricted variable example (using `Z_j-C_j` method)
  27. Multiple optimal solution example (using `Z_j-C_j` method)
  28. Infeasible solution example (using `Z_j-C_j` method)
  29. Unbounded solution example (using `Z_j-C_j` method)
  30. Algorithm (using `C_j-Z_j`method)
  31. Maximization example-1 (using `C_j-Z_j`method)
  32. Maximization example-2 (using `C_j-Z_j`method)
  33. Maximization example-3 (using `C_j-Z_j`method)
  34. BigM method Algorithm (using `C_j-Z_j`method)
  35. Minimization example-1 (using `C_j-Z_j`method)
  36. Minimization example-2 (using `C_j-Z_j`method)
  37. Minimization example-3 (using `C_j-Z_j`method)
  38. Degeneracy example-1 (Tie for leaving basic variable) (using `C_j-Z_j`method)
  39. Degeneracy example-2 (Tie first Artificial variable removed) (using `C_j-Z_j`method)
  40. Unrestricted variable example (using `C_j-Z_j`method)
  41. Multiple optimal solution example (using `C_j-Z_j`method)
  42. Infeasible solution example (using `C_j-Z_j`method)
  43. Unbounded 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

28. Infeasible solution example (using `Z_j-C_j` method)
(Previous example)
30. Algorithm (using `C_j-Z_j`method)
(Next example)

29. Unbounded solution example (using `Z_j-C_j` method)





Unbounded solution
In simplex table, if a variable should enter into the basis, but all the coefficients in that column are negative or zero. So this variable can not be entered into the basis, because for minimum ratio, negative value in denominator can not be considered and zero value in denominator would result `oo`.
Hence, the solution to the given problem is unbounded.
Example
Find solution using Simplex method (BigM method)
MAX Z = 3x1 + 5x2
subject to
x1 - 2x2 <= 6
x1 <= 10
x2 >= 1
and x1,x2 >= 0;


Solution:
Problem is
Max `Z``=````3``x_1`` + ``5``x_2`
subject to
`````x_1`` - ``2``x_2``6`
`````x_1``10`
`````x_2``1`
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`

3. As the constraint-3 is of type '`>=`' we should subtract surplus variable `S_3` and add artificial variable `A_1`

After introducing slack,surplus,artificial variables
Max `Z``=````3``x_1`` + ``5``x_2`` + ``0``S_1`` + ``0``S_2`` + ``0``S_3`` - ``M``A_1`
subject to
`````x_1`` - ``2``x_2`` + ````S_1`=`6`
`````x_1`` + ````S_2`=`10`
`````x_2`` - ````S_3`` + ````A_1`=`1`
and `x_1,x_2,S_1,S_2,S_3,A_1 >= 0`


Tableau-1`C_j``3``5``0``0``0``-M`
`C_B``"Basis"``x_1``x_2``S_1``S_2``S_3``A_1``RHS``"Ratio"=(RHS)/(x_2)`
`R_1` `0``S_1``1``-2``1``0``0``0``6``(6)/(-2)` (ignore, denominator is -ve)
`R_2` `0``S_2``1``0``0``1``0``0``10``(10)/(0)` (ignore, denominator is 0)
`R_3` `-M``A_1``0``(1)``0``0``-1``1``1``(1)/(1)=1``->`
`Z_j``0``-M``0``0``M``-M``Z=-M`
`Z_j-C_j``-3``-M-5``uarr``0``0``M``0`


Most Negative `Z_j-C_j` is `-M-5`. So, the entering variable is `x_2`.

Minimum ratio is `1`. So, the leaving basis variable is `A_1`.

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

Entering `=x_2`, Departing `=A_1`, Key Element `=1`
`R_3`(new)`= R_3`(old)
`x_1``x_2``S_1``S_2``S_3``A_1``RHS`
`R_3`(old) = `0``1``0``0``-1``1``1`
`R_3`(new)`= R_3`(old)`0``1``0``0``-1``1``1`
`R_1`(new)`= R_1`(old) + `2 R_3`(new)
`x_1``x_2``S_1``S_2``S_3``A_1``RHS`
`R_1`(old) = `1``-2``1``0``0``0``6`
`R_3`(new) = `0``1``0``0``-1``1``1`
`2 xx R_3`(new) = `0``2``0``0``-2``2``2`
`R_1`(new)`= R_1`(old) + `2 R_3`(new)`1``0``1``0``-2``2``8`
`R_2`(new)`= R_2`(old)
`x_1``x_2``S_1``S_2``S_3``A_1``RHS`
`R_2`(old) = `1``0``0``1``0``0``10`
`R_2`(new)`= R_2`(old)`1``0``0``1``0``0``10`


Tableau-2`C_j``3``5``0``0``0``-M`
`C_B``"Basis"``x_1``x_2``S_1``S_2``S_3``A_1``RHS``"Ratio"=(RHS)/(S_3)`
`R_1` `0``S_1``1``0``1``0``-2``2``8``(8)/(-2)` (ignore, denominator is -ve)
`R_2` `0``S_2``1``0``0``1``0``0``10``(10)/(0)` (ignore, denominator is 0)
`R_3` `5``x_2``0``1``0``0``-1``1``1``(1)/(-1)` (ignore, denominator is -ve)
`Z_j``0``5``0``0``-5``5``Z=5`
`Z_j-C_j``-3``0``0``0``-5``uarr``M+5`


Variable `S_3` should enter into the basis, but all the coefficients in the `S_3` column are negative or zero. So `S_3` can not be entered into the basis.

Hence, the solution to the given problem is unbounded.




This material is intended as a summary. Use your textbook for detail explanation.
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28. Infeasible solution example (using `Z_j-C_j` method)
(Previous example)
30. Algorithm (using `C_j-Z_j`method)
(Next example)





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