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Analytics > Final > Flashcards

Final Flashcards

1
Q

Purpose of Simulation

A

To analyze large and complex real-world situations

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2
Q

Simulations:

A

Duplicate features, appearance, and
characteristics of a real system

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3
Q

Analogue Simulation

A

(Performance)

-Product design and testing
-Space walks
-“games”

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4
Q

Monte Carlo Simulation

A

(Risk)

-Models the uncertainty or randomness of a system by “replicating” it many times with different values for random inputs

-Provides knowledge of the underlying distribution of the uncertain events and a better understanding of the distribution of possible outcomes and the risk involved

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5
Q

2-Step Process of Monte Carlo Simulation

A
  1. Formulate (build your model)
  2. Input the model into your software (i.e. EXCEL)
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6
Q

System Simulation

A

(Performance)

-Typically used to analyze “system performance” and the effects of
changes on “system performance”

-Continuous systems (weather, wind flow)

-Discrete systems (production, logistics, transportation, service)

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7
Q

Simulation advantages

A

-Straight forward and flexible
-Can analyze large and complex real-world situations
-Allows the user to ask “What-if?”
-Simulations do not interfere with real-world system
-Identifies important component thru simulation interactions
-“Time compression” is possible
-Allows the inclusion of real world complications

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8
Q

Simulation disadvantages

A

-Good simulations can be expensive and time consuming
-Does not generate optimal solutions, but runs trial and error
approach yielding different results with each run
-Requires the generation of all conditions and constraints
-Each model is unique; not transferable to other problems

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9
Q

Probability Distributions

A
  1. Normal vs. Uniform
  2. Discrete vs. Continuous
  3. Symmetric vs. Skewed
  4. Bounded vs. Unbounded
  5. Positive vs. not necessarily positive
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10
Q

Discrete vs. Continuous Random Variables

A

Discrete random variables – may assume one of a fixed set of values
* Ex: Integers

  • Continuous random variables – may assume one of an infinite number of values in a specified range
  • Ex: the amount of gasoline in a gas tank (gallons, centimeters)
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11
Q

Profit Equation

A

𝜋 = 𝑠𝑥 − 𝑣𝑥 − 𝑓

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12
Q

Break-even point is when

A

Total Costs = Revenue

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13
Q

How to find indifference points

A

Set the two profit equations equal

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14
Q

Maximax

A

(Optimistic)

Max of Max’s

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15
Q

Maximin

A

(Pessimistic)

Max of Min’s

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16
Q

Minimax Regret

A

Minimum opportunity loss

Regret of outcome = (Best payoff per outcome) - (Actual payoff per outcome)

17
Q

Criterion of Realism (Hurwicz)

A

Use coefficient of Realism

Realism Payoff = (alpha x Max.) + ((1-alpga) x Min)

18
Q

Equally Likely (LaPlace) Criterion

A

Choose highest alternative average payoff

Ex. 0.5(100000)+0.5(200000)

19
Q

Expected Monetary Value

A

Given p=0.9

Ex. 0.9(100000) + 0.1(200000)

20
Q

Expected Opportunity Loss

A

EMV but for opportunity loss

(Choose lowest)

21
Q

EVwPI =

A

Expected Value with perfect information

Sum of (p of outcome) x (best value of outcome)

22
Q

EVPI =

A

The amount by which perfect information would increase our expected payoff

EVwPI - EMV

23
Q

Decision Trees

A

Right to Left

Values on the right, than probabilities, find EMV, Calculate at circle, Decision at squares.

24
Q

Unbounded Solutions always result from

A

A modeling error

25
Q

Any change to the RHS of a non-redundant constraint will

A

Change the feasible region

26
Q

Shadow Price

A

Indicated the amount by which the objective function value (solution) changes given a unit increase in the RHS value of the constraint

27
Q

How to find indifference point for allowable increase/decrease

A

Set profit of normal equation equal to profit at adjacent point

28
Q

Shadow Price

A

Change in Objective Function Value if we increase one constraint by 1 unit.

29
Q

How to find shadow price

A

-Add 1 unit to 1 constraint
-Solve for new corner point
-Calculate new objective function value
-Compare to original Objective function value
-New OFV - Old OFV

30
Q

Allowable increase/decrease only apply to

A

Objective function coefficient

Never Final value

31
Q

What happens if LHS=RHS

A

Binding constraint

Shadow price doesn’t equal 0

32
Q

What happens if LHS doesn’t equal RHS

A

Shadow Price equals 0

33
Q

Nodes

A

(Circles) A specific point or location in a network

34
Q

Arcs

A

Lines that connect nodes

35
Q

Origins

A

A location that creates goods (factory)

36
Q

Destinations

A

A location that consumes goods (stores)

37
Q

Transshipment

A

A location through which goods pass on their way to or from other locations (warehouse)

38
Q

Node Flow Balance Constraint

A

(Total Flow into Node) - (Total Flow Out of Node) = Net Flow

Analytics (1 decks)

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