Discrete/Stochastic Simulation. Using PROMODEL. Usages. Business Process Re-engineering Manufacturing Process Design Service Process Design Operations Supply chains As a planning tool As an innovation and improvement tool. Applications of Discrete Stochastic Simulation.
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function random(float u, int I)
I = I * 1220703125;
if I<0 then
I = I + 2147483647 + 1;
U = I * 0.4656613E-9;
return “and” end;
The last multiplication is like dividing the number by the largest integer possible
1/2147483647 = .4656613x10 to the minus 9
Every non-uniform random variate has an associated cumulative distribution function F(x) whose values are contained within the interval 0 to 1 and whose values are uniformly distributed over this interval
If x is a non uniform random variate, y = F(x) is uniformly distributed over the interval 0 to 1.
If the inverse of the cumulative distribution function F(x) exists so that x = F-1(y) can be determined, then
1) simply generate a random number uniformly distributed on the interval 0 to 1
2) call this number y and apply the inverse transformation F-1(y) to obtain a random number x with the appropriate distribution.
1) generate a random number U using the program given above
2)then apply EXPRND = -XMEAN * ALOG(U)
(This is simply using the inverse distribution method)
You can use a table function
You can use specialized algorithms that have been developed by academics over thirty-five years of cumulative research
What do we need to know??