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MB106
QUANTITATIVE TECHNIQUES
MODULE I
LECTURE 4
Linear Programming: Graphical solution continued
PROF. KRISHNA ROY
graphical solution of two-variable l.p. problem - An indefinite number of optimal solutions
Example: A firm uses lathes, milling machines and grinding machines to
produce two machine parts. Given below are the machine times
required for each part, the machining times available on different
machines and the profit on each machine part
Find the number of parts I and II to be manufactured per week to
maximize the profit.
09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 2
Type of machine Machining time required
I II
Maximum time
available per week in
mins
Lathes 12 6 3000
Milling machines 4 10 2000
Grinding machines 2 3 900
Profit/unit Rs. 40 Rs. 100
graphical solution of two-variable l.p. problem - An indefinite number of optimal
solutions
Decision Variables: Let x1 be the number of parts I manufactured
Let x2 be the number of parts II manufactured
Objective function: Maximize Z=40 x1 +100 x2
Slope Calculation 40 x1 +100 x2 =0 or x1 = -2 x2 /5 or x1 / x2 = -2/5
Constraints: 12 x1 +6 x2 ≤ 3000
4 x1 +10 x2 ≤ 2000
2 x1 +3 x2 ≤ 900
x1 ≥0, x2 ≥0
09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 3
graphical solution of two-variable l.p. problem - An indefinite number of optimal
solutions
For plotting the graph, identifying two points on each line :-
12 x1 +6 x2 = 3000
Putting x1 =0 we get x2 =3000/6=500 and putting x2 =0 we get x1
=3000/12=250
Therefore the two points on the first line are (0,500) and (250,0)
4 x1 +10 x2 = 2000
Putting x1 =0 we get x2 =2000/10=200 and putting x2 =0 we get x1
=2000/4=500
Therefore the two points on the first line are (0,200) and (500,0)
2 x1 +3 x2 = 900
Putting x1 =0 we get x2 =900/3=300 and putting x2 =0 we get x1
=900/2=450
Therefore the two points on the first line are (0,300) and (450,0)
09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 4
graphical solution of two-variable l.p. problem - An indefinite number of optimal
solutions
The constraints 4 x1 +10 x2 = 2000 1
and 12 x1 +6 x2 = 3000  2
intersect at a point.
Multiplying equation 1 by 3 and subtracting from equation 2 we get
12 x1 +6 x2 = 30002
-12 x1 -30 x2 =-60001X3
-24 x2 =-3000 or 24 x2 =3000 or x2 =3000/24=125
Putting x2=125 in equation 1 we get 4 x1 +10 X 125 = 2000
or x1 = (2000-1250)/4=750/4=187.5
Therefore the point of intersection is (187.5,125)
09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 5
graphical solution of two-variable l.p. problem - An indefinite number of optimal
solutions
The vertices of the feasible solution zone
are(0,0),(250,0),(187.5,125) and (0,200)
At (0,0) Z=40X0+100X0=0
At (250,0) Z=40X250+100X0=10000
At (187.5,125) Z=40X187.5+100X125=20000
At (0,200) Z=40X0+100X200=20000
Since the optimal solution 20000 lies at 2 vertices, the
same value lies at all points along the edge joining
these points. Thus there are multiple optimal solutions
for this problem
09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 6
graphical solution of two-variable l.p. problem – no solution
Example: Maximize Z=3x+2y
Subject to -2x+3y≤9
3x-2y ≤-20
Slope calculation 3x+2y=0 or 3x=-2y or x/y=-2/3
Putting x=0 in constraint 1 and converting to equality we get 3y=9 or y=3
Putting y=0 we get -2x=9 or x=-4.5
Therefore points (0,3) and (-4.5,0) lie on this line
Putting x=0 in constraint 2 and converting to an equality we get -2y=-20 or y=10
Putting y=0 we get 3x=-20 or x=-20/3=-6.67
Therefore points (0,10) and (-6.67,0) lie on this line
09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 7
09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 8
The feasible zone of
the two constraints
do not overlap.
Hence there exists
no solution to this
problem.
graphical solution of two-variable l.p. problem – no solution
graphical solution of two-variable l.p. problem – unboundedsolution
Example : Maximize Z=5 x1 +4 x2
Subject to x1-2 x2 ≤ 1
x1 +2 x2 ≥ 3
x1 ≥0, x2 ≥0
Slope Calculation 5 x1 +4 x2 =0 or x1 = -4 x2 /5 or x1 / x2 = -4/5
Putting x1 =0 in constraint 1 and equating to 0, we get -2 x2=1 or x2 = -1/2
Putting x2 =0 in constraint 1 and equating to 0, we get x1 =1
Two points on the line are (0,-1/2) and (1,0)
Putting x1 =0 in constraint 2 and equating to 0, we get 2 x 2=3 or x2 = 3/2=1.5
Putting x2 =0 in constraint 2 and equating to 0, we get x1 =3
Two points on the line are (0,1.5) and (3,0)
09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 9
09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 10
The solution is
unbounded because
Z increases towards
the right and the
feasible zone moves
to infinity towards
the right.
graphical solution of two-variable l.p. problem – unbounded solution
graphical solution of two-variable l.p. problem – A single solution
Example : Maximize Z=5 x1 +8x2
Subject to: 3 x1+5 x2= 18
5 x1 +3 x2=14
x1 ≥0, x2 ≥0
Putting x1 =0 in constraint 1 , we get 5 x2=18 or x2 = 18/5=3.6
Putting x2 =0 in constraint 1, we get 3x1 =18 or x1 =18/3=6
Two points on the line are (0,3.6) and (6,0)
Putting x1 =0 in constraint 2, we get 3 x 2=14 or x2 = 14/3=4.67
Putting x2 =0 in constraint 2 , we get 5 x1 =14 or x1 =14/5=2.8
Two points on the line are (0,4.67) and (2.8,0)
09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 11
09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 12
The two equality
constraints intersect
at (1,3). Hence a
single solution exists
and no optimization
is possible. The
solution is
5X1+8X3=29
graphical solution of two-variable l.p. problem – single solution
• Till we meet again in the next class……….
PROF. KRISHNA ROY, FMS, BCREC 13
09-11-2021

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Mb 106 quantitative techniques 4

  • 1. MB106 QUANTITATIVE TECHNIQUES MODULE I LECTURE 4 Linear Programming: Graphical solution continued PROF. KRISHNA ROY
  • 2. graphical solution of two-variable l.p. problem - An indefinite number of optimal solutions Example: A firm uses lathes, milling machines and grinding machines to produce two machine parts. Given below are the machine times required for each part, the machining times available on different machines and the profit on each machine part Find the number of parts I and II to be manufactured per week to maximize the profit. 09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 2 Type of machine Machining time required I II Maximum time available per week in mins Lathes 12 6 3000 Milling machines 4 10 2000 Grinding machines 2 3 900 Profit/unit Rs. 40 Rs. 100
  • 3. graphical solution of two-variable l.p. problem - An indefinite number of optimal solutions Decision Variables: Let x1 be the number of parts I manufactured Let x2 be the number of parts II manufactured Objective function: Maximize Z=40 x1 +100 x2 Slope Calculation 40 x1 +100 x2 =0 or x1 = -2 x2 /5 or x1 / x2 = -2/5 Constraints: 12 x1 +6 x2 ≤ 3000 4 x1 +10 x2 ≤ 2000 2 x1 +3 x2 ≤ 900 x1 ≥0, x2 ≥0 09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 3
  • 4. graphical solution of two-variable l.p. problem - An indefinite number of optimal solutions For plotting the graph, identifying two points on each line :- 12 x1 +6 x2 = 3000 Putting x1 =0 we get x2 =3000/6=500 and putting x2 =0 we get x1 =3000/12=250 Therefore the two points on the first line are (0,500) and (250,0) 4 x1 +10 x2 = 2000 Putting x1 =0 we get x2 =2000/10=200 and putting x2 =0 we get x1 =2000/4=500 Therefore the two points on the first line are (0,200) and (500,0) 2 x1 +3 x2 = 900 Putting x1 =0 we get x2 =900/3=300 and putting x2 =0 we get x1 =900/2=450 Therefore the two points on the first line are (0,300) and (450,0) 09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 4
  • 5. graphical solution of two-variable l.p. problem - An indefinite number of optimal solutions The constraints 4 x1 +10 x2 = 2000 1 and 12 x1 +6 x2 = 3000  2 intersect at a point. Multiplying equation 1 by 3 and subtracting from equation 2 we get 12 x1 +6 x2 = 30002 -12 x1 -30 x2 =-60001X3 -24 x2 =-3000 or 24 x2 =3000 or x2 =3000/24=125 Putting x2=125 in equation 1 we get 4 x1 +10 X 125 = 2000 or x1 = (2000-1250)/4=750/4=187.5 Therefore the point of intersection is (187.5,125) 09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 5
  • 6. graphical solution of two-variable l.p. problem - An indefinite number of optimal solutions The vertices of the feasible solution zone are(0,0),(250,0),(187.5,125) and (0,200) At (0,0) Z=40X0+100X0=0 At (250,0) Z=40X250+100X0=10000 At (187.5,125) Z=40X187.5+100X125=20000 At (0,200) Z=40X0+100X200=20000 Since the optimal solution 20000 lies at 2 vertices, the same value lies at all points along the edge joining these points. Thus there are multiple optimal solutions for this problem 09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 6
  • 7. graphical solution of two-variable l.p. problem – no solution Example: Maximize Z=3x+2y Subject to -2x+3y≤9 3x-2y ≤-20 Slope calculation 3x+2y=0 or 3x=-2y or x/y=-2/3 Putting x=0 in constraint 1 and converting to equality we get 3y=9 or y=3 Putting y=0 we get -2x=9 or x=-4.5 Therefore points (0,3) and (-4.5,0) lie on this line Putting x=0 in constraint 2 and converting to an equality we get -2y=-20 or y=10 Putting y=0 we get 3x=-20 or x=-20/3=-6.67 Therefore points (0,10) and (-6.67,0) lie on this line 09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 7
  • 8. 09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 8 The feasible zone of the two constraints do not overlap. Hence there exists no solution to this problem. graphical solution of two-variable l.p. problem – no solution
  • 9. graphical solution of two-variable l.p. problem – unboundedsolution Example : Maximize Z=5 x1 +4 x2 Subject to x1-2 x2 ≤ 1 x1 +2 x2 ≥ 3 x1 ≥0, x2 ≥0 Slope Calculation 5 x1 +4 x2 =0 or x1 = -4 x2 /5 or x1 / x2 = -4/5 Putting x1 =0 in constraint 1 and equating to 0, we get -2 x2=1 or x2 = -1/2 Putting x2 =0 in constraint 1 and equating to 0, we get x1 =1 Two points on the line are (0,-1/2) and (1,0) Putting x1 =0 in constraint 2 and equating to 0, we get 2 x 2=3 or x2 = 3/2=1.5 Putting x2 =0 in constraint 2 and equating to 0, we get x1 =3 Two points on the line are (0,1.5) and (3,0) 09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 9
  • 10. 09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 10 The solution is unbounded because Z increases towards the right and the feasible zone moves to infinity towards the right. graphical solution of two-variable l.p. problem – unbounded solution
  • 11. graphical solution of two-variable l.p. problem – A single solution Example : Maximize Z=5 x1 +8x2 Subject to: 3 x1+5 x2= 18 5 x1 +3 x2=14 x1 ≥0, x2 ≥0 Putting x1 =0 in constraint 1 , we get 5 x2=18 or x2 = 18/5=3.6 Putting x2 =0 in constraint 1, we get 3x1 =18 or x1 =18/3=6 Two points on the line are (0,3.6) and (6,0) Putting x1 =0 in constraint 2, we get 3 x 2=14 or x2 = 14/3=4.67 Putting x2 =0 in constraint 2 , we get 5 x1 =14 or x1 =14/5=2.8 Two points on the line are (0,4.67) and (2.8,0) 09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 11
  • 12. 09-11-2021 Prof. Krishna Roy, Dr. B. C. Roy Engineering College 12 The two equality constraints intersect at (1,3). Hence a single solution exists and no optimization is possible. The solution is 5X1+8X3=29 graphical solution of two-variable l.p. problem – single solution
  • 13. • Till we meet again in the next class………. PROF. KRISHNA ROY, FMS, BCREC 13 09-11-2021