Question 1

Only binding constraints form the shape (boundaries) of the feasible region.

Accepted Answer

A) True 
 B)False

Question 2

The improvement in the value of the objective function per unit increase in a right-hand side is the

Accepted Answer

A) sensitivity value. 
B) dual price. 
C) constraint coefficient. 
D) slack value. 
A) sensitivity value. 
B) dual price. 
C) constraint coefficient. 
D) slack value.

Question 3

Decision variables limit the degree to which the objective in a linear programming problem is satisfied.

Accepted Answer

A) True 
 B)False

Question 4

A range of optimality is applicable only if the other coefficient remains at its original value.

Accepted Answer

A) True 
 B)False

Question 5

Which of the following statements is NOT true?

Accepted Answer

A) A feasible solution satisfies all constraints. 
B) An optimal solution satisfies all constraints. 
C) An infeasible solution violates all constraints. 
D) A feasible solution point does not have to lie on the boundary of the feasible region. 
A) A feasible solution satisfies all constraints. 
B) An optimal solution satisfies all constraints. 
C) An infeasible solution violates all constraints. 
D) A feasible solution point does not have to lie on the boundary of the feasible region.

Question 6

Because surplus variables represent the amount by which the solution exceeds a minimum target, they are given positive coefficients in the objective function.

Accepted Answer

A) True 
 B)False

Question 7

The standard form of a linear programming problem will have the same solution as the original problem.

Accepted Answer

A) True 
 B)False

Question 8

In a linear programming problem, the objective function and the constraints must be linear functions of the decision variables.

Accepted Answer

A) True 
 B)False

Only binding constraints form the shape (boundaries) of the feasible region.

The improvement in the value of the objective function per unit increase in a right-hand side is the

Decision variables limit the degree to which the objective in a linear programming problem is satisfied.

A range of optimality is applicable only if the other coefficient remains at its original value.

Which of the following statements is NOT true?

Because surplus variables represent the amount by which the solution exceeds a minimum target, they are given positive coefficients in the objective function.

The standard form of a linear programming problem will have the same solution as the original problem.

In a linear programming problem, the objective function and the constraints must be linear functions of the decision variables.

Introduction

LP Sensitivity Analysis and Interpretation of Solution

Linear Programming Applications

Advanced Linear Programming Applications

Distribution and Network Problems

Integer Linear Programming

Nonlinear Optimization Models

Project Scheduling: Pertcpm

Inventory Models

Waiting Line Models

Simulation

Decision Analysis

Multicriteria Decisions

Forecasting

Markov Processes

LP: Simplex Method

Simplex-Based Sensitivity Analysis and Duality

Solution Procedures for Transportation and Assignment Problems

Minimal Spanning Tree

Dynamic Programming

Filters

Exam 2: Introduction to Linear Programming

Only binding constraints form the shape (boundaries) of the feasible region.

The improvement in the value of the objective function per unit increase in a right-hand side is the

Decision variables limit the degree to which the objective in a linear programming problem is satisfied.

A range of optimality is applicable only if the other coefficient remains at its original value.

Which of the following statements is NOT true?

Because surplus variables represent the amount by which the solution exceeds a minimum target, they are given positive coefficients in the objective function.

The standard form of a linear programming problem will have the same solution as the original problem.

In a linear programming problem, the objective function and the constraints must be linear functions of the decision variables.

Introduction

LP Sensitivity Analysis and Interpretation of Solution

Linear Programming Applications

Advanced Linear Programming Applications

Distribution and Network Problems

Integer Linear Programming

Nonlinear Optimization Models

Project Scheduling: Pertcpm

Inventory Models

Waiting Line Models

Simulation

Decision Analysis

Multicriteria Decisions

Forecasting

Markov Processes

LP: Simplex Method

Simplex-Based Sensitivity Analysis and Duality

Solution Procedures for Transportation and Assignment Problems

Minimal Spanning Tree

Dynamic Programming

Filters