Hello,




More of my philosophy about my PERT++ and about my JNI Wrapper for Delphi and FreePascal and about the new Java SE Development Kit 19.0.1 and more of my thoughts..

I am a white arab, and i think i am smart since i have also
invented many scalable algorithms and algorithms..

I have just downloaded and installed the new Java SE Development Kit 19.0.1

Here it is:

https://www.oracle.com/java/technologies/javase/jdk19-archive-downloads.html


And i have just tested my open source JNI Wrapper for Delphi and FreePascal with the Java SE Development Kit 19.0.1, and it i working perfectly, so you can download my JNI Wrapper for Delphi and FreePascal from my website here:

https://sites.google.com/site/scalable68/jni-wrapper-for-delphi-and-freepascal


And I have also tested my other open source software project called PERT++ with the new Java SE Development Kit 19.0.1, and it is working perfectly, so you can download my PERT++ from my website here:

https://sites.google.com/site/scalable68/pert-an-enhanced-edition-of-the-program-or-project-evaluation-and-review-technique-that-includes-statistical-pert-in-delphi-and-freepascal

And I have in my PERT++ provided you with two ways of how to estimate the critical path, first, by the way of CPM(Critical Path Method) that shows all the arcs of the estimate of the critical path, and the second way is by the way of the central limit
theorem by using the inverse normal distribution function, and you have to provide my software project that is called PERT++ with three types of estimates that are the following:

Optimistic time - generally the shortest time in which the activity
can be completed. It is common practice to specify optimistic times
to be three standard deviations from the mean so that there is
approximately a 1% chance that the activity will be completed within
the optimistic time.

Most likely time - the completion time having the highest
probability. Note that this time is different from the expected time.

Pessimistic time - the longest time that an activity might require. Three standard deviations from the mean is commonly used for the pessimistic time.

The central limit theorem states that the sampling distribution of the mean of any independent, random variable will be normal or nearly normal, if the sample size is large enough.

How large is "large enough"?

In practice, some statisticians say that a sample size of 30 is large enough when the population distribution is roughly bell-shaped. Others recommend a sample size of at least 40. But if the original population is distinctly not normal (e.g., is badly
skewed, has multiple peaks, and/or has outliers), researchers like the sample size to be even larger. So i invite you to read my following thoughts about my software
project that is called PERT++, and notice that the PERT networks are referred to by some researchers as "probabilistic activity networks" (PAN) because the duration of some or all of the arcs are independent random variables with known probability
distribution functions, and have finite ranges. So PERT uses the central limit theorem (CLT) to find the expected project duration.

So I have provided you in my PERT++ with the following functions:


function NormalDistA (const Mean, StdDev, AVal, BVal: Extended): Single;

function NormalDistP (const Mean, StdDev, AVal: Extended): Single;

function InvNormalDist(const Mean, StdDev, PVal: Extended; const Less: Boolean): Extended;

For NormalDistA() or NormalDistP(), you pass the best estimate of completion time to Mean, and you pass the critical path standard deviation to StdDev, and you will get the probability of the value Aval or the probability between the values of Aval and
Bval.

For InvNormalDist(), you pass the best estimate of completion time to Mean, and you pass the critical path standard deviation to StdDev, and you will get the length of the critical path of the probability PVal, and when Less is TRUE, you will obtain a
cumulative distribution.

So as you are noticing from my above thoughts that since PERT networks are referred to by some researchers as "probabilistic activity networks" (PAN) because the duration of some or all of the arcs are independent random variables with known probability
distribution functions, and have finite ranges. So PERT uses the central limit theorem (CLT) to find the expected project duration. So then you have to use my above functions
that are Normal distribution and inverse normal distribution functions, please look at my demo inside my zip file to understand better how i am doing it:

You can download and read about my PERT++ from my website here:

https://sites.google.com/site/scalable68/pert-an-enhanced-edition-of-the-program-or-project-evaluation-and-review-technique-that-includes-statistical-pert-in-delphi-and-freepascal



Thank you,
Amine Moulay Ramdane.

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