Operation Research Quiz

What is the objective of linear programming?

• To maximize or minimize a linear function

• To calculate the area of a triangle

• To solve quadratic equations

• To find the intersection of two lines

What are the components of a linear programming problem?

• objective function, decision variables, constraints, and non-negativity constraints

• objective function, decision variables, constraints, and upper bound constraints

• objective function, decision variables, constraints, and equality constraints

• objective function, decision variables, constraints, and non-negativity constraints

What is the difference between feasible and infeasible solutions in linear programming?

• Feasible solutions satisfy all constraints, while infeasible solutions violate one or more constraints.

• Feasible solutions and infeasible solutions both satisfy all constraints.

• Feasible solutions violate one or more constraints, while infeasible solutions satisfy all constraints.

• Feasible solutions violate all constraints, while infeasible solutions satisfy one or more constraints.

What is the graphical method used for in linear programming?

• To visually represent the feasible region and find the optimal solution.

• To identify the decision variables.

• To calculate the objective function.

• To determine the constraints of the problem.

What is the difference between an optimal solution and a feasible solution in linear programming?

• An optimal solution is the worst possible solution, while a feasible solution is the best possible solution.

• An optimal solution is the best possible solution that maximizes or minimizes the objective function, while a feasible solution is any solution that satisfies all the constraints of the linear programming problem.

• An optimal solution is any solution that satisfies all the constraints, while a feasible solution maximizes or minimizes the objective function.

• An optimal solution is a solution that violates some of the constraints, while a feasible solution is the best possible solution.

What is the difference between a constraint and an objective function in linear programming?

• Constraints are used to maximize the objective function.

• Constraints define limitations or restrictions, while the objective function defines the goal or objective to be optimized.

• Objective functions are used to set limitations or restrictions.

• Constraints and objective functions are the same thing.

What is the difference between a maximization problem and a minimization problem in linear programming?

• The difference is the goal of finding the average value of the objective function.

• The difference is the goal of finding the middle value of the objective function.

• The difference is the goal of finding the sum of all values of the objective function.

• The difference is the goal of finding the highest or lowest value of the objective function.

What is the difference between an integer programming problem and a linear programming problem?

• The main difference is that in an integer programming problem, the constraints are always linear equations, while in a linear programming problem, the constraints can be linear or nonlinear equations.

• The main difference is that in an integer programming problem, the decision variables are restricted to be integers, while in a linear programming problem, the decision variables can be any real number.

• The main difference is that in an integer programming problem, the decision variables are restricted to be positive integers, while in a linear programming problem, the decision variables can be any real number.

• The main difference is that in an integer programming problem, the objective function is always maximized, while in a linear programming problem, the objective function can be maximized or minimized.

What is the purpose of sensitivity analysis in linear programming?

• To determine the impact of changes in input parameters on the optimal solution.

• To calculate the objective function value.

• To determine the feasibility of the solution.

• To identify the constraints in the linear programming model.

What are the limitations of linear programming?

• The limitations of linear programming include linearity assumption, certainty assumption, proportionality assumption, limited number of variables and constraints, and potential for local optimal solutions.

• Linear programming can handle any type of problem, regardless of its complexity.

• Linear programming always guarantees the optimal solution.

• Linear programming can handle an unlimited number of variables and constraints.