Quantitative Methods Test 1

Front 1

The process of identifying a difference between the actual and the desired state of affairs and then taking action to resolve the difference.

Back 1

Problem solving

Front 2

The process of defining the problem, identifying the alternatives, determining the criteria, evaluating the alternatives, and choosing the alternative.

Back 2

Decision making

Front 3

A problem in which the objective is to find the "best" solution with respect to just one criterion.

Back 3

Single-criterion decision problem

Front 4

A problem that involves more than one criterion; the objective is to find the "best" solution, taking into account all the criteria.

Back 4

Multicriteria decision making

Front 5

The alternative selected.

Back 5

Decision

Front 6

A representation of a real object or situation.

Back 6

Model

Front 7

A physical replica, or representation, of a real object.

Back 7

Iconic model

Front 8

Although physical in form, an analog model does not have a physical appearance similar to the real object or situation it represents.

Back 8

Analog model

Front 9

Mathematical symbols and expressions used to represent a real situation.

Back 9

Mathematical model

Front 10

Restrictions or limitations imposed on a problem.

Back 10

Constraints

Front 11

A mathematical expression that describes the problem's objectives.

Back 11

Objective function

Front 12

The environmental factors or inputs that cannot be controlled by the decision maker.

Back 12

Uncontrollable inputs

Front 13

The inputs that are controlled or determined by the decision maker.

Back 13

Controllable inputs

Front 14

Another term for controllable input.

Back 14

Decision variable

Front 15

A model in which all uncontrollable inputs are known and cannot vary.

Back 15

Deterministic model

Front 16

A model in which at least one uncontrollable input is uncertain and subject to variation; stochastic models are also referred to as probabilistic models.

Back 16

Stochastic (probabilistic) model

Front 17

The specific decision-variable value or values that provided the "best" output for the mode.

Back 17

Optimal solution

Front 18

A decision alternative or solution that does not satisfy one or more constraints.

Back 18

Infeasable solution

Front 19

A decision alternative or solution that satisfies all constraints.

Back 19

Feasible solution

Front 20

The portion of the total cost that does not depend on the volume; this cost remains the same no matter how much is produced.

Back 20

Fixed cost

Front 21

The portion of the total cost that is dependent on and varies with the volume.

Back 21

Variable cost

Front 22

The rate of change of the total cost with respect to volume.

Back 22

Marginal cost

Front 23

The rate of change of total revenue with respect to volume.

Back 23

Marginal revenue

Front 24

The volume at which total revenue equals total cost.

Back 24

Breakeven point

Front 25

An equation or inequality that rules out certain combinations of decision variables as feasible solutions.

Back 25

Constraint

Front 26

The process of translating the verbal statement of a problem into a mathematical statement called the mathematical model.

Back 26

Problem formulation

Front 27

A controllable input for a linear programming model.

Back 27

Decision variable

Front 28

A set of constraints that requires all variables to be non-negative.

Back 28

Nonnegativity constraints

Front 29

A representation of a problem where the objective and all constraint conditions are described by mathematical expressions.

Back 29

Mathematical model

Front 30

A mathematical model with a linear objective function, a set of linear constraints, and nonnegative variables.

Back 30

Linear programming model

Front 31

Another term for linear programming model.

Back 31

Linear program

Front 32

Mathematical expressions in which the variables appear in separate terms and are raised to the first power.

Back 32

Linear functions

Front 33

A solution that satisfies all the constraints.

Back 33

Feasible solution

Front 34

The set of all feasible solutions.

Back 34

Feasible region

Front 35

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.

Back 35

Slack variable

Front 36

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.

Back 36

Standard form

Front 37

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.

Back 37

Redundant constraint

Front 38

Graphically speaking, extreme points are the feasible solution points occurring at the vertices or "corners" of the feasible region. With two-variable problems, extreme points are determined by the intersection of the constraint lines.

Back 38

Extreme point

Front 39

A variable subtracted from the left-hand side of a greater-tha-or-equal-to constraint to convert the constraint into an equality. The value of this variable can usually be interpreted as the amount over and above some required minimum level.

Back 39

Surplus variable

Front 40

The case in which more than one solution provides the optimal value for the objective function.

Back 40

Alternative optimal solutions

Front 41

The situation in which no solution to the linear programming problem satisfies all the constraints.

Back 41

Infeasibility

Front 42

If the value of the solution may be made infinitely large in a maximization linear programming problem or infinitely small in a minimization problem without violating any of the constraint, the problem is said to be unbounded.

Back 42

Unbounded

Front 43

The study of how changes in the coefficients of a linear programming problem affect the optimal solution.

Back 43

Sensitivity analysis

Front 44

The range of values over which an objective function coefficient may vary without causing any change in the values of the decision variables in the optimal solution.

Back 44

Range of optimality

Front 45

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

Back 45

Dual price

Front 46

The amount by which an objective function coefficient would have to improve (increase for a maximization problem, decrease for a minimization problem) before it would be possible for the corresponding variable to assume a positive value in the optimal solution.

Back 46

Reduced cost

Front 47

The range of values over which the dual price is applicable.

Back 47

Range of feasibility

Front 48

A rule indicating when simultaneous changes in two or more objective function coefficients will not cause a change in the optimal solution It can also be applied to indicate when two or more right-hand-side changes will not cause a change in any of the dual prices.

Back 48

100 percent rule

Front 49

A cost that is not affected by the decision made. It will be incurred no matter what values the decision variables assume.

Back 49

Sunk cost

Front 50

A cost that depends upon the decision made. The amount of a relevant cost will vary depending on the values of the decision variables.

Back 50

Relevant cost

Front 51

An algebraic procedure for solving linear programming problems.

Back 51

Simplex method

Front 52

Given a linear program in standard form, with n variables and m constants, is obtained by setting n-m of the variables equal to zero and solving the constraint equations for the values of the other m variables

Back 52

Basic solution

Front 53

One of the n-m variables set equal to zero in a basic solution.

Back 53

Nonbasic variable

Front 54

One of the m variables not required to equal zero in a basic solution.

Back 54

Basic variable

Front 55

A basic solution that is also feasible; that is, it satisfies the nonnegativity constraints.

Back 55

Basic feasible solution

Front 56

The form in which a linear program must be written before setting up the initial simplex tableau.

Back 56

Tableau form

Front 57

A table used to keep track of the calculations required by the simplex method.

Back 57

Simplex tableau

Front 58

A vector of column of a matrix that has a zero in every position except one. In the nonzero position there is a 1. There is a unit column in the simplex tableau for each basic variable.

Back 58

Unit column or unit vector

Front 59

The set of variables that are not restricted to equal zero in the current basic solution. The variables that make up the basis are termed basic variables, and the remaining variables are called nonbasic variables.

Back 59

Basis

Front 60

The row in the simplex tableau that contains the value of cj-zj for every variable (column).

Back 60

Net evaluation row

Front 61

The process of moving from one basic feasible solution to another.

Back 61

Iteration

Front 62

The element of the simplex tableau that is in both the pivot row and the pivot column.

Back 62

Pivot element

Front 63

The column in the simplex tableau corresponding to the nonbasic variable that is about to be introduced into solution.

Back 63

Pivot column

Front 64

The row in the simplex tableau corresponding to the basic variable that will leave the solution

Back 64

Pivot row

Front 65

When one or more of the basic variables has a value of zero.

Back 65

Degeneracy

Front 66

An associated linear programming problem to a linear programming problem.

Back 66

Deal problem