Every time I run a simulation with the same parameters in the Run window I get exactly the same results. The results are different if a different number of replications is set each time the run is started
These are my settings in the run window:
enter image description here
I have a lot of Process blocks. Most of them have a normal distribution in duration. Why are the results not different?
If it helps in any way, her is a photo of the constructed model:
enter image description here
Arena uses the same random nr stream for your run unless you tell it to use another - so your first rep will look the same every time. The answer depends on the distributions you sample from. If you change the logic and sample at other times, the answer will change. Each following rep will have a unique answer based on the random nr streams you are sampling from. It makes it easier/possible to find errors if you can execute exactly the same run.
Arena provides (by default) ten different "streams" of (pseudo) random numbers. If you don't ask the system to use a particular stream, it will use stream 10. For example NORM(10,2) will use stream 10 to calculate a random normally distributed number (mean 10, standard deviation 2); NORM(10,2,4) will use stream 4 to calculate a similarly distributed number.
By default, the 10 random number generators for the streams are initialised at the beginning of each run to 14561, 25971, 31131, 22553, 12121, 32323, 19991, 18765, 14327, and 32535 (from Arena help). At the end of one replication, the generators will not be re-initialised, so they will start the next replication with a new value.
You can control the random number generator initialisation with the SEEDS element.
As #Marlize says, this helps to ensure that you can reproduce a simulation result if you need to.
Related
I am currently completing some verification checks on an Anylogic DES simulation model, and I have two source blocks with identical hourly arrival rate schedules, broken down into 24 x 1h blocks.
The issue I am encountering is significant differences in the number of agents generated by one block compared with another. I understand that the arrival rate is based on the poisson distribution, so there is some level of randomness in the instants of agent generation, but I would expect that the overall number generated by these two blocks should be similar, if not identical. For example, in one operating scenario one block is generating 78 agents, whilst the other is only generating 67 over the 24h period. This seems to be a common issue across all operating scenarios.
Is there a potential explanation regarding idiosyncrasies within Anylogic that might explain this?
Any pointers would be welcomed.
I think it occurs because it follows a poisson distribution. To solve this, you could use the interarrival time function of the source block. In that case you would have the same number of arrivals for different source blocks. However, I'm not sure whether this fits a schedule. If not, you could use the getHourOfDay() function together with a parameter representing the interarrival time. You then have to write the code below for every hour of the day:
if(getHourOfDay()==14) parameter =5;
using sources with poisson distributions will definitely not produce same results... That's the magic of stocastic models.
An alternative to solve this problem is the following:
sources will generate using the inject function
use dynamic events that will be in charge to do source.inject();
let's imagine you have R trains coming per day, and this is a fixed value you want to use, you can then distribute the trains accross the day by doing this:
for(int i=0;i<R;i++){
create_DynamicEvent1(uniform(0,1),DAY); //for source1
create_DynamicEvent2(uniform(0,1),DAY); //for source2
}
This doesn't follow a poisson distribution, but generates a predefined number of arrivals of trains throughout the day, and you can use another distribution of your choice if the uniform is not good enough for you.
run this for every day
I would like to predict the switching behavior of time-dependent signals. Currently the signal has 3 states (1, 2, 3), but it could be that this will change in the future. For the moment, however, it is absolutely okay to assume three states.
I can make the following assumptions about these states (see picture):
the signals repeat periodically, possibly with variations concerning the time of day.
the duration of state 2 is always constant and relatively short for all signals.
the duration of states 1 and 3 are also constant, but vary for the different signals.
the switching sequence is always the same: 1 --> 2 --> 3 --> 2 --> 1 --> [...]
there is a constant but unknown time reference between the different signals.
There is no constant time reference between my observations for the different signals. They are simply measured one after the other, but always at different times.
I am able to rebuild my model periodically after i obtained more samples.
I have the following problems:
I can only observe one signal at a time.
I can only observe the signals at different times.
I cannot trigger my measurement with the state transition. That means, when I measure, I am always "in the middle" of a state. Therefore I don't know when this state has started and also not exactly when this state will end.
I cannot observe a certain signal for a long duration. So, i am not able to observe a complete period.
My samples (observations) are widespread in time.
I would like to get a prediction either for the state change or the current state for the current time. It is likely to happen that i will never have measured my signals for that requested time.
So far I have tested the TimeSeriesPredictor from the ML.NET Toolbox, as it seemed suitable to me. However, in my opinion, this algorithm requires that you always pass only the data of one signal. This means that assumption 5 is not included in the prediction, which is probably suboptimal. Also, in this case I had problems with the prediction not changing, which should actually happen time-dependently when I query multiple predictions. This behavior led me to believe that only the order of the values entered the model, but not the associated timestamp. If I have understood everything correctly, then exactly this timestamp is my most important "feature"...
So far, i did not do any tests on Regression-based approaches, e.g. FastTree, since my data is not linear, but keeps changing states. Maybe this assumption is not valid and regression-based methods could also be suitable?
I also don't know if a multiclassifier is required, because I had understood that the TimeSeriesPredictor would also be suitable for this, since it works with the single data type. Whether the prediction is 1.3 or exactly 1.0 would be fine for me.
To sum it up:
I am looking for a algorithm which is able to recognize the switching patterns based on lose and widespread samples. It would be okay to define boundaries, e.g. state duration 3 of signal 1 will never last longer than 30s or state duration 1 of signal 3 will never last longer 60s.
Then, after the algorithm has obtained an approximate model of the switching behaviour, i would like to request a prediction of a certain signal state for a certain time.
Which methods can I use to get the best prediction, preferably using the ML.NET toolbox or based on matlab?
Not sure if this is quite what you're looking for, but if detecting spikes and changes using signals is what you're looking for, check out the anomaly detection algorithms in ML.NET. Here are two tutorials that show how to use them.
Detect anomalies in product sales
Spike detection
Change point detection
Detect anomalies in time series
Detect anomaly period
Detect anomaly
One way to approach this would be to first determine the periodicity of each of the signals independently. This could be done by looking at the frequency distribution of time differences between measurements of state 2 only and separately for each signal.
This will give a multinomial distribution. The shortest time difference will be the duration of the switching event (after discarding time differences less than the max duration of state 2). The second shortest peak will be the duration between the end of one switching event and the start of the next.
When you have the 3 calculations of periodicity you can simply calculate the difference between each of them. Given you have the timestamps of the measurements of state 2 for each signal you should be able to calculate the time of switching for all other signals.
Currently, I have two delay blocks above each other, where one agent goes through one of the blocks and the other through the other one.
But when I want exponentially distributed values with a mean of 120 seconds, They both need to have the same value always. So they are done at the same time.
You simply need to have the two delay blocks use their own (but with the same seed) random object.
Start off by creating two identical random objects
And have each of the delay blocks use of them of them
Then the numbers they will sample will be the same for every sampling iteration.
See this post for a similar problem
Why do two flowcharts set up exactly the same end with different results every time the simulation is run even when I use a fixed seed?
Not questioning your probably bad design by not using resources, These are the steps to follow to ensure things happen at the exact same time:
1.Create a variable called seed of type long
2.create a cyclic event that runs every 1 minute and has the following code:
seed=(new Random()).nextLong();
3.In both blocks you will use the following code to calculate the exponential distribution:
exponential(120,0,new Random(seed))
I am currently working on a simple simulation that consists of 4 manufacturing workstations with different processing times and I would like to measure the WIP inside the system. The model is PennyFab2 in case anybody knows it.
So far, I have measured throughput and cycle time and I am calculating WIP using Little's law, however the results don't match he expectations. The cycle time is measured by using the time measure start and time measure end agents and the throughput by simply counting how many pieces flow through the end of the simulation.
Any ideas on how to directly measure WIP without using Little's law?
Thank you!
For little's law you count the arrivals, not the exits... but maybe it doesn't make a difference...
Otherwise.. There are so many ways
you can count the number of agents inside your system using a RestrictedAreaStart block and use the entitiesInside() function
You can just have a variable that adds +1 if something enters and -1 if something exits
No matter what, you need to add the information into a dataset or a statistics object and you get the mean of agents in your system
Little's Law defines the relationship between:
Work in Process =(WIP)
Throughput (or Flow rate)
Lead Time (or Flow Time)
This means that if you have 2 of the three you can calculate the third.
Since you have a simulation model you can record all three items explicitly and this would be my advice.
Little's Law should then be used to validate if you are recording the 3 values correctly.
You can record them as follows.
WIP = Record the average number of items in your system
Simplest way would be to count the number of items that entered the system and subtract the number of items that left the system. You simply do this calculation every time unit that makes sense for the resolution of your model (hourly, daily, weekly etc) and save the values to a DataSet or Statistics Object
Lead Time = The time a unit takes from entering the system to leaving the system
If you are using the Process Modelling Library (PML) simply use the timeMeasureStart and timeMeasureEnd Blocks, see the example model in the help file.
Throughput = the number of units out of the system per time unit
If you run the model and your average WIP is 10 units and on average a unit takes 5 days to exit the system, your throughput will be 10 units/5 days = 2 units/day
You can validate this by taking the total units that exited your system at the end of the simulation and dividing it by the number of time units your model ran
if you run a model with the above characteristics for 10 days you would expect 20 units to have exited the system.
I've used 2 Uniform Random Number blocks in my simulation model, but every time I run the program they generate last numbers (exactly the same). I need to test the model with new generated numbers. what should I do?
thanks for your helps in advance
The fact that random number generators generate the same random numbers "from the start" is a feature, not a bug. It allows for reproducible testing. You need to initialize your random number generator with a "random seed" in order to give a different result each time - you could use the current time, for example. When you do, it is recommended that you store the seed used - it means you can go back and run exactly the same code again.
For initializing a random seed, you can use the methods given in this earlier answer
In that answer, they are setting the seed to 0 - this is the opposite of what you are trying to do. You will want to generate a non-random number (like the date), and use that. A very useful article can be found here. To quote:
If you look at the output from rand, randi, or randn in a new MATLAB
session, you'll notice that they return the same sequences of numbers
each time you restart MATLAB. It's often useful to be able to reset
the random number generator to that startup state, without actually
restarting MATLAB. For example, you might want to repeat a calculation
that involves random numbers, and get the same result.
They recommend the command
rng shuffle
To generate a new random seed. You can access the seed that was used with
rng.seed
and store that for future use. So if you co
rng shuffle
seedStore = rng.seed;
Then next time you want to reproduce results, you set
rng(seedStore);