Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

309 Term Final > 309 Cards 1-9 > Flashcards

309 Cards 1-9 Flashcards

1
Q

A situation in which more than one optimal solution is possible. It arises when the angle or slope of the objective is the same as the slope of the constraint.

A

Alternative Optimal Solution

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Cells that represent the decision variables in Solver.

A

Changing Cell

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

A restriction (stated in the form of an inequality of an equation) that inhibits (or binds) the value that can be achieved by the objective function.

A

Constraint

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

A point that lies on one of the corner of the feasible region. This means that it falls at the intersection of two constraint lines.

A

Corner (or Extreme) Point

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

The method of finding the optimal solution to an LP problem that involves testing the profit or cost level at each corner point of the feasible region. The theory of LP states that the optimal solution must lie at one of the corner points.

A

Corner Point Method

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

The unknown quantities in a problem for which optimal solution values are to be found.

A

Decision Variable

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

The area that satisfies all of a problem’s resource restrictions—that is, the region where all constraint overlap. All possible solutions to the problem lie in the feasible region.

A

Feasible Region

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

Any point that lies outside the feasible region. It violates one or more of the stated constraints.

A

Infeasible Solution

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

A straight line that represents all nonnegative combinations of the decision variable for a particular profit (or cost) level.

A

Level (Iso) Line

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

The general category of mathematical modeling and solution techniques used to allocate resources while optimizing a measurable goal; LP is one type of programming model.

A

Mathematical Programming

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

A mathematical statement of the goal of an organization, stated as an intent to maximize or minimize some important quantity, such as profit or cost.

A

Objective Function

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

A common LP problem that involves a decision as to which products a firm should produce given that it faces limited resources.

A

Product Mix Problem

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

A constraint that does not affect the feasible solution region.

A

Redundant Constraint

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

An iteractive procedure for solving LP problems.

A

Simplex Method

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

The difference between the right-hand-side and left-hand-side of a ≤ constraint. Slack typically represents the unused resource.

A

Slack

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

An Excel add-in that allows LP problems to be set up and solved in Excel.

A

Solver

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

The difference between the left-hand-side and right-hand-side of a ≥ constraint. Surplus typically represents the level of oversatisfaction of a requirement.

A

Surplus

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

The cell that contains the formula for the objective function in Solver

A

Target Cell

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

A condition that exists when the objective value can be made infinitely large (in a maximization problem) or small (in a minimization problem) without violating any of the problem’s constraints.

A

Unbounded Solution

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

The coefficient for a decision variable in the objective function. Typically, this refers to unit profit or unit cost.

A

Objective Function Coefficient

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

The difference between the marginal contribution to the objective function value from the inclusion of a decision variable and the marginal worth of the resources it consumes. IN the case of a decison variable that has an optimal value of zero, it is also the minimum amount by which the OFC of that variable should change before it would have a nonzero optimal value.

A

Reduced Cost

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

The study of how sensitive an optimal solution is to model assumptions and to data changes. Also referred to as postoptimality analysis.

A

Sensitivity Analysis

23
Q

The magnitude of the change in the objective function value for a unit increase in the RHS of a constraint.

A

Shadow Price

24
Q

The difference between the RHS and LHS of a ≤ constraint. Typically represents the unused resource.

A

Slack

25
Q

The difference between the LHS and RHS of a ≥ constraint. Typically represents the level of oversatisfication of a requirement.

A

Surplus

26
Q

Decision variables that are required to have integer values of either 0 or 1. Also called 0-1 variables.

A

Binary Variables

27
Q

An algorithm used by Solver and other software to solve IP problems. It divides the set of feasible solutions into subregions that are examined systematically.

A

Branch-and-Bound Method

28
Q

Decision variables that are required to be integer valued. Actual values of these variables are restricted only by the constraints in the problem.

A

General Integer Variables

29
Q

A mathematical programming technique that produces integer solutions to LP problems.

A

Integer Programming

30
Q

A category of problems in which some decision variables must have integer values (either general integer or binary) and other decision variables can have fractional values.

A

Mixed Integer Programming

31
Q

The minimum guaranteed amount one is willing to accept to avoid the risk associated with a gamble.

A

Certainty Equivalent

32
Q

A number from 0 to 1 such that when α is close to 1, the decision criterion is optimistic, and when α is close to zero, the decision criterion is pessimistic.

A

Coefficient of Realism

33
Q

A course of action or a strategy that can be chosen by a decision maker.

A

Decision Alternative

34
Q

A decision-making environment in which several outcomes can occur as a result of a decision or alternative. Probabilities of the outcomes are known.

A

Decision Making Under Risk

35
Q

A decision-making environment in which several outcomes can occur. Probabilities of these outcomes, however, are not known.

A

Decision Making under Uncertainty

36
Q

A table in which decision alternatives are listed down the rows and outcomes are listed across the columns. The body of the table contain the payoff.

A

Decision Table

37
Q

A ratio of the expected value of sample information and the expected value of perfect information.

A

Efficiency of Sample Information

38
Q

The average or expected monetary outcome of a decision if it can be repeated many times. This is determined by multiplying the monetary outcomes by their respective probabilities. The results are then added to arrive at the EMV.

A

Expected Monetary Value (EMV):

39
Q

The average or expected regret of a decision.

A

Expected Opportunity Loss (EOL):

40
Q

The average or expected value of information if it is completely accurate.

A

Expected Value of Perfect Information (EVPI):

41
Q

The average or expected value of the decision if the decision maker knew what would happen ahead of time.

A

Expected Value with Perfect Information (EVwPI):

42
Q

The average or expected value of imperfect or survey information.

A

Expected Value of Sample Information (EVSI):

43
Q

An optimistic decision-making criterion. This is the alternative with the highest possible return.

A

Maximax

44
Q

A pessimistic decision-making criterion that maximizes the minimum outcome. It is the best of the worst possible outcomes.

A

Maximin

45
Q

A decision criterion that minimizes the maximum opportunity loss.

A

Minimax Regret

46
Q

The amount you would lose by not picking the best alternative. For any outcome, this is the difference between the consequences of any alternative and the best possible alternative. Also called regret.

A

Opportunity Loss

47
Q

A person who avoids risk. As the monetary value increases on the utility curve, the utility increases at a decreasing rate. This decision maker gets less utility for a greater risk and higher potential returns.

A

Risk Avoider

48
Q

A person who is indifferent toward risk. The utility curve for a risk-neutral person is a straight line.

A

Risk Neutral

49
Q

The monetary amount that a person is willing to give up in order to avoid the risk associated with a gamble.

A

Risk Premium

50
Q

A person who seeks risk. As the monetary values increases on the utility curve, the utility increases at an increasing rate. This decision maker gets more pleasure for a greater risk and higher potential returns.

A

Risk Seeker

51
Q

Decisions in which the outcome of one decision influences other decisions.

A

Sequential Decisions

52
Q

A graph or curve that illustrates the relationship between utility and monetary values. When this curve has been constructed, utility values from the curve can be used in the decision-making process.

A

Utility Curve

53
Q

A theory that allows decision makers to incorporate their risk preference and other factors into the decision-making process.

A

Utility Theory

309 Term Final (5 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions