M3L4.ppt

D Nagesh Kumar, IISc Optimization Methods: M3L4
1
Linear Programming
Simplex method - II

2
Objectives
Objectives
 To discuss the Big-M method
 Discussion on different types of LPP
solutions in the context of Simplex method
 Discussion on maximization verses
minimization problems

3
Big-M Method
Simplex method for LP problem with ‘greater-than-
equal-to’ ( ) and ‘equality’ (=) constraints
needs a modified approach. This is known as
Big-M method.
 The LPP is transformed to its standard form by
incorporating a large coefficient M


4
Transformation of LPP for Big-M
method
1. One ‘artificial variable’ is added to each of the ‘greater-than-
equal-to’ (≥) and equality (=) constraints to ensure an initial
basic feasible solution.
2. Artificial variables are ‘penalized’ in the objective function by
introducing a large negative (positive) coefficient for
maximization (minimization) problem.
3. Cost coefficients, which are supposed to be placed in the Z-
row in the initial simplex tableau, are transformed by ‘pivotal
operation’ considering the column of artificial variable as
‘pivotal column’ and the row of the artificial variable as ‘pivotal
row’.
4. If there are more than one artificial variables, step 3 is
repeated for all the artificial variables one by one.

5
Example
Consider the following problem
1 2
1 2
2
1 2
1 2
Maximize 3 5
subject to 2
6
3 2 18
, 0
Z x x
x x
x
x x
x x
 
 

 


6
Example
 After incorporating the artificial variables
where x3 is surplus variable, x4 is slack variable and a1
and a2 are the artificial variables
1 2 1 2
1 2 3 1
2 4
1 2 2
1 2
Maximize 3 5
subject to 2
6
3 2 18
, 0
Z x x Ma Ma
x x x a
x x
x x a
x x
   
   
 
  


7
Transformation of cost coefficients
Considering the objective function and the first constraint
 
 
1 2 1 2 1
1 2 3 1 2
3 5 0
2
Z x x Ma Ma E
x x x a E
    
   
Pivotal Column
Pivotal Row
By the pivotal operation 2
1 E
M
E 
 the cost coefficients are modified as
    M
Ma
a
Mx
x
M
x
M
Z 2
0
5
3 2
1
3
2
1 









8
Transformation of cost coefficients
Considering the modified objective function and the third
constraint
Pivotal Column
Pivotal Row
By the pivotal operation the cost coefficients are modified as
     
 
1 2 3 1 2 3
1 2 2 4
3 5 0 2
3 2 18
Z M x M x Mx a Ma M E
x x a E
        
  
4
3 E
M
E 

    M
a
a
Mx
x
M
x
M
Z 20
0
0
3
5
4
3 2
1
3
2
1 









9
Construction of Simplex
Tableau
Corresponding simplex tableau
Pivotal row, pivotal column and pivotal elements are shown as earlier

10
Simplex Tableau…contd.
Successive simplex tableaus are as follows:

11

12
Optimality has reached. Optimal solution is Z = 36 with x1 = 2 and x2 = 6

13
Simplex method: ‘Unbounded’,
‘Multiple’ and ‘Infeasible’ solutions
Unbounded solution
 If at any iteration no departing variable can be found
corresponding to entering variable, the value of the
objective function can be increased indefinitely, i.e.,
the solution is unbounded.

14
Multiple (infinite) solutions
 If in the final tableau, one of the non-basic variables
has a coefficient 0 in the Z-row, it indicates that an
alternative solution exists.
 This non-basic variable can be incorporated in the
basis to obtain another optimal solution.
 Once two such optimal solutions are obtained,
infinite number of optimal solutions can be obtained
by taking a weighted sum of the two optimal
solutions.

15
Simplex method: Example of
Multiple (indefinite) solutions
Consider the following problem
1 2
1 2
2
1 2
1 2
Maximize 3 2
subject to 2
6
3 2 18
, 0
Z x x
x x
x
x x
x x
 
 

 

 Only modification, compared to earlier problem, is that the
coefficient of x2 is changed from 5 to 2 in the objective function.
 Thus the slope of the objective function and that of third
constraint are now same, which leads to multiple solutions

16
Following similar procedure as described earlier, final simplex
tableau for the problem is as follows:

17
As there is no negative coefficient in the Z-row optimal solution
is reached.
Optimal solution is Z = 18 with x1 = 6 and x2 = 0
However, the coefficient of non-basic variable x2 is zero in the Z-row
Another solution is possible by incorporating x2 in the basis
Based on the , x4 will be the exiting variable
rs
r
c
b

18
So, another optimal solution is Z = 18 with x1 = 2 and x2 = 6
If one more similar step is performed, previous simplex tableau will be obtained back

19
Thus, two sets of solutions are: and
Other optimal solutions will be obtained as
where
For example, let b = 0.4, corresponding solution is
Note that values of the objective function are not
changed for different sets of solution; for all the
cases Z = 18.
6
0
 
 
 
 
 
2
6
 
 
 
 
 
 
6 2
1
0 6
b b
   
   
 
   
   
   
 
0,1
b 
3.6
3.6
 
 
 
 
 

20
Infeasible solution
 If in the final tableau, at least one of the artificial
variables still exists in the basis, the solution is
indefinite.

21
Minimization versus
maximization problems
 Simplex method is described based on the
standard form of LP problems, i.e., objective
function is of maximization type
 However, if the objective function is of
minimization type, simplex method may still
be applied with a small modification

22
Minimization versus
The required modification can be done in either of
following two ways.
1. The objective function is multiplied by -1 so as to keep the
problem identical and ‘minimization’ problem becomes
‘maximization’. This is because minimizing a function is
equivalent to the maximization of its negative
2. While selecting the entering nonbasic variable, the variable
having the maximum coefficient among all the cost
coefficients is to be entered. In such cases, optimal
solution would be determined from the tableau having all
the cost coefficients as non-positive ( )
0


23
Minimization versus
 One difficulty, that remains in the minimization
problem, is that it consists of the constraints with
‘greater-than-equal-to’ ( ) sign. For example,
minimize the price (to compete in the market),
however, the profit should cross a minimum
threshold. Whenever the goal is to minimize some
objective, lower bounded requirements play the
leading role. Constraints with ‘greater-than-equal-to’
( ) sign are obvious in practical situations.
 To deal with the constraints with ‘greater-than-
equal-to’ ( ) and equality sign, Big-M method is to
be followed as explained earlier.




24
Thank You

M3L4.ppt

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M3L4.ppt