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2. Simplex method example ( Enter your problem )
  1. Structure of Linear programming problem
  2. Algorithm
  3. Maximization example-1
  4. Maximization example-2
  5. Maximization example-3
  6. BigM method Algorithm
  7. Minimization example-1
  8. Minimization example-2
  9. Minimization example-3
  10. Degeneracy example-1 (Tie for leaving basic variable)
  11. Degeneracy example-2 (Tie first Artificial variable removed)
  12. Unrestricted variable example
  13. Multiple optimal solution example
  14. Infeasible solution example
  15. Unbounded solution example
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. Graphical method
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2. Algorithm
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1. Structure of Linear programming problem





General structure of Linear programming problem

(Maximize or Minimize) `Z_x=c_1x_1+c_2x_2+c_3x_3+...+c_nx_n` (objective function)
Subject to the constraints
`a_11x_1+a_12x_2+a_13x_3+...+a_(1n)x_n(<=,>=,=)b_1`
`a_21x_1+a_22x_2+a_23x_3+...+a_(2n)x_n(<=,>=,=)b_2`
:
:
`a_(m1)x_1+a_(m2)x_2+a_(m3)x_3+...+a_(mn)x_n(<=,>=,=)b_m` (constraints)
and `x_1,x_2,...,x_n>=0` (non-negative condition)


Here `x_1,x_2,...,x_n` are decision variables

Each constraint may be either
1. `<=` (less than or equal to)
2. `>=` (greater than or equal to)
3. `=` (equal to)

`x_1,x_2,...,x_n>=0` means each `x_i` must be non-negative



A linear problem is said to be in standard form if
  1. It is maximization problem
  2. There are only equalities (=) (by adding slack, surplus or artificial variables)
  3. All variables are restricted to be non-negativity



Any linear program can in fact be transformed into an equivalent linear program in standard form
  1. If the objective function is minimize `Z=2x_1-3x_2` then we can simply maximize it by multiplying -1,
    maximize `Z'=` minimize `-Z=-2x_1+3x_2`
  2. If constraint is `<=` type then we have to add slack variable
    eg. `3x_1+2x_2<=4` then `3x_1+2x_2+S_1=4` (here `S_1` is slack variable)
  3. If constraint is `>=` type then we have to subtract surplus variable and add aritificial variable
    eg. `5x_1+3x_2>=6` then `5x_1+3x_2-S_2+A_1=4` (here `S_2` is surplus variable and `A_1` is aritificial variable)
  4. If constraint is `=` type then we have to add aritificial variable
    eg. `x_1-x_2=1` then `x_1-x_2+A_2=1` (here `A_2` is aritificial variable)
  5. If `x_1` is unrestricted in sign then we can introduce two new decision variables `x_1'` and `x_1''`
    and now `x_1=x_1'-x_1''` where `x_1,x_1',x_1'' >= 0`


For eg.
Min `Z=2x_1-3x_2`
subject to the constraint
`3x_1+2x_2<=4`
`5x_1+3x_2>=6`
`x_1-x_2=1`
and `x_1,x_2>=0`

Then it is converted to
Max `Z'=-2x_1+3x_2`
subject to the constraint
`3x_1+2x_2+S_1=4`
`5x_1+3x_2-S_2+A_1=4`
`x_1-x_2+A_2=1`
and `x_1,x_2,S_1,S_2,A_1,A_2>=0`




This material is intended as a summary. Use your textbook for detail explanation.
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