ops - lecture 7

monte carlo is good when:

-complex problems -no other appropriate tech. -uncertainty and probability distribution -stochasticity

analogue simulation

-original physical system is replaced by an analogous physical system that is EASIER TO MANIPULATE -ex. spaceflight conditions

**artificially created random numbers must have the following characteristics**

-random numbers are uniformly distributed -numerical technique for generating the numbers must be efficient -no pattern

T/F: it is difficult to validate results of simulation that replicate reality

TRUE

probabilistic models

a large proportion of the applications of simulations are used for this

simulation results will not ..

equal analytical results unless trials have been conducted to reach steady state

what if analysis

form of model experimentation using a computer to ascertain the results of making changes in model

in the monte carlo process, the values for a random variable are..

generated by sampling from a probability distribution

simulated time

long period of real time represents by a short period

in simulation modeling, random numbers are generated by,,

mathematical process instead of a physical

numbers generated using a numerical technique are..

not true random numbers but pseudorandom numbers

true random numbers can be produced by..

only a physical process like spinning a roulette wheel

pseudorandom numbers

random numbers generated by a math process instead of a physical process

monte carlo technique is not a type of ...

simulation model but a math process used within a simulation

monte carlo

system used as a technique for selecting numbers RANDOMLY from a probability distribution for use in a trial run of a simulation

computer mathematical simulation

systems are replicated with math models on computer -used for analyzing complex systems

the more periods for which simulation occurs...

the more accurate the result

purpose of the monte carlo process is to generate..

the random variable from randomly selecting from the probability distribution

when analytical analysis is not possible...

there is no analytical standard of comparison so validation is difficult

as simulation models get more complex..

they become impossible to perform manually