13 terms

An iso-profit line represents all combinations of the products which would yield the same value of profits.

t

The feasible solution space (or area) only contains points that satisfy all constraints.

t

A simplex method is possible to solve L.P. problems with more than three (n>3) decision variables.

t

Changing one or more of the objective coefficients will change the feasible solution area(or space) in the L.P.

f

Iso-profit line represents all possible combinations of:

a. the decision variables which will produce a given profit. c. the constraints which will produce a given profit.

b. the objective function which will produce a given profit. d. the solution points in the feasible solution area.

a. the decision variables which will produce a given profit. c. the constraints which will produce a given profit.

b. the objective function which will produce a given profit. d. the solution points in the feasible solution area.

a

Some assumptions of the general L.P. model are:

a. linearity, divisibility, certainty, nonnegativity. b. linearity, stockasticity, additivity, divisibility.

c. certainty, linearity, feasibility, nonnegativity. d. constraints, objective function, decision variable.

a. linearity, divisibility, certainty, nonnegativity. b. linearity, stockasticity, additivity, divisibility.

c. certainty, linearity, feasibility, nonnegativity. d. constraints, objective function, decision variable.

a

A feasible solution area (or space) in a L.P. problem represents:

a. area which satisfies all of the inequality constraints. b. area which satisfies all of the constraints.

c. area which satisfies all of the objective function. d. are which satisfies all of the decision variables.

a. area which satisfies all of the inequality constraints. b. area which satisfies all of the constraints.

c. area which satisfies all of the objective function. d. are which satisfies all of the decision variables.

b

Which of the following is not a major components required to structuring a linear programming ?

a. constraints b. decision variables c. objective function d. feasible solutions

a. constraints b. decision variables c. objective function d. feasible solutions

d

The theoretical limit on the number of decision variables that can be handled by the simplex method is:

a. 1 b. 2 c. 3 d. unlimited

a. 1 b. 2 c. 3 d. unlimited

d

For the constraints given below, which pairs of points are (within) the feasible solution space of this maximization problem?

(1) X1 + X2 ≤ 5, (2) 2X1 + 2X2 ≥ 6, (3) 3X1 + 5X2 ≤ 15

a. X1=3.0, X2=3.0 b. X1=3.0, X2=1.0 c. X1=1.0, X2=1.0 d. X1=2.0, X2=4.0 e. None

(1) X1 + X2 ≤ 5, (2) 2X1 + 2X2 ≥ 6, (3) 3X1 + 5X2 ≤ 15

a. X1=3.0, X2=3.0 b. X1=3.0, X2=1.0 c. X1=1.0, X2=1.0 d. X1=2.0, X2=4.0 e. None

b

Which of the choices below constitutes a simultaneous solution to these equations:

(1) 3X + 4Y = 10 and (2) 5X + 4Y = 14 ?

a. X = 2, Y = 0.5 b. X = 4, Y = -0.5 c. X = 2, Y = 1 d. X = 1, Y = 2 e. None

(1) 3X + 4Y = 10 and (2) 5X + 4Y = 14 ?

a. X = 2, Y = 0.5 b. X = 4, Y = -0.5 c. X = 2, Y = 1 d. X = 1, Y = 2 e. None

c

What combination of X and Y will yield the optimum for this problem ?

Maximize Z = 10X + 30Y subject to: (1) 4X + 6Y ≤ 12 and (2) 8X + 4Y ≤ 16.

a. X = 2.0, Y = 0.0 b. X = 1.5, Y = 1.0 c. X = 0.0, Y = 2.0 d. X = 3.0, Y = 2.0 e. None

Maximize Z = 10X + 30Y subject to: (1) 4X + 6Y ≤ 12 and (2) 8X + 4Y ≤ 16.

a. X = 2.0, Y = 0.0 b. X = 1.5, Y = 1.0 c. X = 0.0, Y = 2.0 d. X = 3.0, Y = 2.0 e. None

c

What combination of X and Y will provide a minimum for this problem?

Minimize Z = X + 3Y subject to: (1) 2X + 4Y ≥ 12 and (2) 5X + 2Y ≥10

a. X = 6.0, Y = 0.0 b. X = 5.0, Y = 0.0 c. X = 1.0, Y = 2.5 d. X = 2.5, Y = 1.0 e. None

Minimize Z = X + 3Y subject to: (1) 2X + 4Y ≥ 12 and (2) 5X + 2Y ≥10

a. X = 6.0, Y = 0.0 b. X = 5.0, Y = 0.0 c. X = 1.0, Y = 2.5 d. X = 2.5, Y = 1.0 e. None

a