Information Management Systems Test 3
In adjusted exponential smoothing, the closer beta is to ________, the stronger a trend is reflected.
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Trends
A gradual, long-term, up-or-down movement of demand
Linear Trend
A linear regression model that relates demand to time
Correlation
A measure of the strength of the relationship between independent and dependent variables
Constant Service Times
A special case of the single-server model with undefined service times. There is no variability in service times; beta = 0. Typically used with machinery and automated equipment
Monte Carlo
A technique for selecting numbers randomly from a probability distribution. It is analogous to gambling devices.
Queuing System Operating Statistics
Are steady state, or constant, over time
L
Average number of customers in the queuing system (customers being serviced and in the waiting line)
Lq
Average number of customers in the waiting line
W
Average time a customer spends in the total queuing system (waiting and being served)
Wq
Average time a customer spends waiting in the queue to be served
Single-Server Model
Characterized by an infinite calling population, a first-come, first-served queue discipline, Poisson arrival rate and exponential service times.
________ is the percentage of the variation in the dependent variable that results from the independent variable.
Coefficient of determination
________ is a measure of the strength of the relationship between independent variable(s) and a dependent variable.
Correlation
The ________ is a procedure for developing a consensus forecast about what will occur in the future.
Delphi method
Linear Regression Methods
Develops a mathematical relationship between the forecasted item and factors that cause it to behave the way it does
________ is not part of a Monte Carlo simulation.
Finding an optimal solution
Times Series Methods
Forecasts are statistical techniques that use historical data
Delphi Method
Forecasts future using informed judgement and opinions from knowledgeable individuals
Characteristics of Moving Averages
Is good for stable demand with no pronounced behavioral patterns
________ indicates a forecast is biased high.
Large "-" Ebar
________ moving averages react more slowly to recent demand changes than do ________ moving averages.
Longer-period, shorter-period
________ is absolute error as a percentage of demand.
MAPD
MAD
Mean Absolute Deviation (MAD) is the average, absolute difference between the forecast and the demand. The lower the MAD, the more accurate.
MAPD
Mean absolute percent deviation (MAPD) is absolute error as a percentage of demand
Lambda
Mean arrival rate
Mu
Mean service rate
The ________ process is analogous to gambling devices.
Monte Carlo
________ is a technique for selecting numbers randomly from a probability distribution.
Monte Carlo
Random Numbers
Numbers equally likely to be chosen from a large population of numbers
U
Probability that the server is busy (probability that a customer has to wait) known as the utilization factor (U=lambda/mu)
I
Probability that the server is idle (probability that a customer can be served) I = 1-U or 1-(lambda/mu)
________ use management judgment, expertise, and opinion to make forecasts.
Qualitative methods
An important factor to consider in analyzing a queuing system is the:
Queue Discipline
Pseudorandom Numbers
Random numbers generated by a mathematical process instead of a physical process
Analogue Simulation
Replaces a physical system with an analogous physical system that is easier to manipulate
Manual Simulation
Simulation conducted by hand
Finite Queue
The length of the queue is limited
Infinite Queue
The length of the queue is unlimited
Queuing Discipline
The order in which waiting customers are served
Average Error
The per-period average of cumulative error
Coefficient of Determination
The percentage of the variation in the dependent variable that results from the independent variable
Characteristics of a Table of Random Numbers
The random numbers are derived from some artificial process, like a computer program. They are equally likely to occur.
Cumulative Error
The sum of the forecast errors
________ methods assume that what has occurred in the past will continue to occur in the future.
Time series
Why do we do simulations?
To help solve some real world problems. It provides a laboratory experimentation on a real system.
Multiple-Server Model
Two or more independent servers in parallel serve a single waiting line
Cyclical Patterns
Up-and-down repetitive movement in demand
Seasonal Patterns
Up-and-down, repetitive movement within a trend occurring periodically
Qualitative Methods
Use judgement, expertise, and opinion to make forecasts
Characteristics of Weighted Moving Averages
Weights are assigned to the most recent data
In a single-server queuing model, L represents the:
average number of customers waiting and being served.
A ________ is an up-and-down repetitive movement that repeats itself over a time span of more than 1 year.
cyclical pattern
Coefficient of determination is the percentage of the variation in the ________ variable that results from the ________ variable.
dependent, independent
A table of random numbers must be:
efficiently generated.
Which of the following will not decrease system utilization?
increase in arrival rate
In a finite queue, the length of the queue is:
limited.
Random numbers generated by a ________ process instead of a ________ process are pseudorandom numbers.
mathematical, physical
A limitation of simulation is that:
model building is costly and time-consuming.
A seed value is a(n):
number used to start a stream of random numbers.
Simulations are normally done:
on the computer.
Simulation does not usually provide recommended decisions. Instead it provides:
operating characteristics.
Developing the cumulative probability distribution helps to determine:
random number ranges.
The arrival rate is the:
rate of arrivals to the service facility.
In the Monte Carlo process, values for a random variable are generated by ________ a probability distribution.
sampling from
A ________ is a gradual, long-term, up-or-down movement of demand.
trend
Confidence Intervals
upper confidence: Xbar + (1.96)(s/sqrt[n]) lower confidence: Xbar - (1.96)(s/sqrt[n]) xbar is the mean and s is the sample SD from sample size n