Integer Programming (Chapter 5)

A set of vocabulary flashcards designed to help reinforce key concepts and terminology related to integer programming based on today’s lecture notes.

Integer Programming

A mathematical optimization technique where some or all of the unknowns are required to be integers.

Total Integer Model

A type of integer programming model where all decision variables must have integer values.

Mixed Integer Model

A type of integer programming model where only some decision variables are constrained to integer values.

0-1 Integer Model

A model where decision variables can only take on values of 0 or 1.

Decision Variables

The variables that decision makers will decide the values of in order to achieve the best outcome.

Constraints

Conditions that must be satisfied in a mathematical model, often expressed as inequalities.

Objective Function

A formula that the decision-maker seeks to maximize or minimize.

Feasible Solution

A solution that satisfies all of the problem's constraints.

Optimal Solution

The best feasible solution, which results in the maximum or minimum value of the objective function.

Sensitivity Analysis

The study of how changes in the inputs of a mathematical model affect the optimal solution.

Resource Constraints

Limits on resources available for a mathematical problem, such as budget or space.

Profit Maximization

The process of increasing a business's profit through various means, including decisions made in integer programming.

Solver Parameters

Settings in mathematical programming software that define how the optimization process is executed.

Investment Alternatives

Different options available for investment decisions, which can be analyzed using integer programming.

Set Covering Problem

A type of optimization problem where the goal is to cover a set of requirements with a minimum number of resources.

Binary Variable

A variable that can take on only two values: 0 or 1, often used in 0-1 integer programming models.