1(a)
Define the following with reference to linear programming model.

i) Unbounded solution

ii) Feasible solution

iii) Slack variable

iv) Surplus variable

v) Optimal Solution.

i) Unbounded solution

ii) Feasible solution

iii) Slack variable

iv) Surplus variable

v) Optimal Solution.

10 M

1(b)
The whit window company is a company with only 3 employees which makes two different
kinds of handcrafted windows a wood framed and an aluminum framed window. They earn
$60 profit for each wood framed window and $30 profit for each aluminum framed window. Doug makes the wood frames and can make 6 per day. Linda makes the aluminium frames and can make 4 per day. Bob forms and cutsthe glass and can make 48 square feet of glass per day. Each wood framed window uses 6 square foot of glass and each aluminum framed windows used 8 square feet of glass. The company wishes to determine how many windows of each type to produce per day to it maximize total profit. Formulate it as LPP and solve
Graphically.

10 M

2(a)
Find all the basic solutions to the following systems of equations indentifying in each case the basic and non basic variable and finally the optimal solution.

Maximize Z = 5x

Maximize Z = 5x

_{1 + 3x2 + 4x3 Subject to 2x1 + x2 + x3 ≤ 20 3x1 + x2 + 2x3 ≤ 30 x1, x2, x3 ≥ 0.}
10 M

2(b)
Use the simplex method to solve the following problem.

Maximize Z = x

Subject to

3x

x

Maximize Z = x

_{1}+ 2x_{2}+ 4x_{3}Subject to

3x

_{1}+ x_{2}+ 5x_{3}≤ 10x

_{1 + 4x2 + x3 ≤ 8 2x1 + 2x3 ≤ 7 x1, x2, x3 ≥ 0.}
10 M

3(a)
Solve the following LPP using two phase method

Minimize Z = 2x

x

3x

x

Minimize Z = 2x

_{1}+ 3x_{2}+ x_{3}x

_{1}+ 4x_{2}+ 2x_{3}≥ 83x

_{1}+ 2x_{2}≥ 6x

_{1}, x_{2}, x_{3}≥ 0.
10 M

3(b)
Use Big M method to solve the problem

Minimize Z = 3x

Subject to

2x

3x

x

Minimize Z = 3x

_{1}+ 2x_{2}+ 4x_{3}Subject to

2x

_{1}+ x_{2}+ 3x_{3}= 603x

_{1}+ 3x_{2}+ 5x_{3}≥ 120x

_{1}, x_{2}, x_{3}≥ 0.
10 M

4(a)
Solve by revised simplex method

Maximize Z = 6x

Subject to

2x

x

Maximize Z = 6x

_{1}- 2x_{2}+ 3x_{3}Subject to

2x

_{1}- x_{2}+ 2x_{3}≤ 2x

_{1}+ 4x_{3}≤ 4 and x_{1}, x_{2}, x_{3}≥ 0.
10 M

4(b)
Use duality to solve ;

Minimize Z

Subject to

x

2x

Minimize Z

_{x}=3x_{1}+ x_{2}Subject to

x

_{1}+ x_{2}≥ 12x

_{1}+ 3x_{2}≥ 2, x_{1}, x_{2}, x_{3}≥ 0.
10 M

5(a)
Solve the following problem by dual simplex method.

Minimize Z = 2x

Subject to

3x

4x

x

x

Minimize Z = 2x

_{1}+ x_{2}Subject to

3x

_{1}+ x_{2}≥ 34x

_{1}+ 3x_{2}≥ 6x

_{1}+ 2x_{2}≥ 3x

_{1}, x_{2}≥ 0.
10 M

5(b)
Solve the following problem by using lower bound technique.

Maximize Z = 10x

Subject to

x

2x

3x

x

Maximize Z = 10x

_{1}+ 15x_{2}+ 8x_{3}Subject to

x

_{1}+ 2x_{2}+ 2x_{3}≤ 2002x

_{1}+ x_{2}+ x_{3}≤ 2203x

_{1}+ x_{2}+ 2x_{3}≤ 180x

_{1}≥ 10, x_{2}≥ 20, x_{3}≥ 30.
10 M

6(a)
Hindustan construction company needs 3, 3, 4 and 5 million cubic feet of fill at four earthern dams-sites in Punjab. It can transfer the fill from three mounds A, B and C where 2, 6 and 7 million cubic feet of fill is available, cost of transporting one million cubic feet of fill from mounds to the four sites in lakhs are given in the table.

Find IBFs by using any method and check for optimality.

Find IBFs by using any method and check for optimality.

10 M

6(b)
Five men are available to do five different jobs. From past records the time (in hrs) that each man takes to do each job is known and given in the following table:

10 M

7(a)
Define the following with reference to game theory with an example :

i) Pure strategy

ii) Mixed strategy

iii) Saddle point

iv) Pay off matrix

v) 2 person zero sum games.

i) Pure strategy

ii) Mixed strategy

iii) Saddle point

iv) Pay off matrix

v) 2 person zero sum games.

10 M

7(b)
In a game of matching coins with two players, suppose one player wins Rs 2 when there are two heads and wins nothing when there are two tails and loses Rs 1 when there are one head and one teil. Determine the payoff matrix, the best strategies for each player and the value of the game.

10 M

Explain briefly the following:

8(a)(i)
Tabu search

5 M

8(a)(ii)
Genetic Algorithm

5 M

8(a)(iii)
Simulated annealing technique

5 M

8(a)(iii)
Meta heuristics.

5 M

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