Quantitative Management Chapter 2

An equation or inequality that rules out certain combinations of decision variables as feasible solutions.
Problem Formulation
The process of translating the verbal statement of a problem into a mathematical statement called the mathematical model.
Decision Variable
A controllable input for a linear programming model.
Nonnegativity Constraints
A set of constraints that requires all variables to be nonnegative
Mathematical Model
A representation of a problem where the objective and all constraint conditions are described by mathematical expressions.
Linear Programming Model
A mathematical model with a linear objective function, a set of linear constraints, and nonnegative variables.
Linear Program
Another term for linear programming model
Linear function
Mathematical expressions in which the variables appear in separate terms and are raised to the first power.
Feasible Solution
A solution that satisfies all the constraints.
Feasible Region
The set of all feasible solutions.
Slack Variable
A variable added to the left hand side of a less-than-or-equal to constraint to convert the constraint into an equality. The value of this variable can usually be interpreted as the amount of unused resource.
Standard Form
A linear program in which all the constraints are written as equalities. The optimal solution of the standard form of a linear program is the same as the optimal solution of the original formulation of the linear program.
Redundant Constraint
A constraint that does not affect the feasible region. If a constraint is redundant, it can be removed from the problem without affecting the feasible region.
Extreme point
Graphically speaking, extreme points are the feasible solution points occurring at the concerns of the feasible region
Surplus Variable
A variable subtracted from the left-hand side of a greater-than-or-equal-to constraint to convert the constraint into an equality.
Alternative Optimal Solution
The case in which more than one solution provides the optimal value for the objective function.
Does not satisfies all the constraints.
If the value of the solution may be made infinitely large in maximization linear programming problem or infinitely small in a minimization problem without violating any of the constraints.