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
Related
We’re generating the data that we might get from a shop floor to run, test, and validate our machine learning models. We first have here a discrete event simulation model for our manufacturing system. Each production order is seen as an agent, which then goes through different processes with a queue (waiting time) and delays (firstly production time, secondly logistics time).
enter image description here
But sometimes we have one process, for example, printing (code 5A, after the second Select5Output), with three different machines, which do not have a particular capacity. It’s time when we divide our order into parts and send them to those machines (very randomly, subjectively).
The data we take is from flowchart_process_states_log in Database.
The data we take is from flowchart_process_states_log in Database.
My questions here are:
How can we define the number of products in each order? Ex. we’re printing card, for one order it may be 10k, for another 8k or 33k. Can we define it as agent’s parameter? Then how can we vary them (stochastically, no exact number needed).
How can we split those 10k cards into three different machines? And then how to get back an complete agent with 10k? The Agent ID should remain the same as we trace and analyse them in ML model. Is it reasonable to see an order as an agent?
How can we multiply the number of our agent after a process? Ex. After cutting 10k pieces we have 20k.
We have the distribution for delay ex. triangle distribution. But we want some disturbances, when it suddenly takes 2 days for that delay instead of 3-4 hours as normal. How to do it?
Thank you in advance for your effort. Every help is highly appreciated, because we're here and learning together. Thank you !!
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.
So Im trying to determine whether another vending machine is required in the gas station (it's an exercise not a real life problem). The only thing that Im given is the fact that each minute a customer is trying to use the vending machine and on average it takes 0.95 min for a customer to buy and pay for what he bought. Im having trouble with "arrivals defined by" field. The exercise says that I absolutely must use interarrival rate. It also says that the probability distribution is unknown and that it is most definitely not exponential. My question is the following, is there any way to define interarrival rate without using a distribution function. I tried inputing the number on it's own and the simulation doesn't work. I considered using rate even though Im not suppose to but it just didn't make sense since rate already considers the distribution to be exponential which isn't the case in my simulation.
Based on your requirement I absolutely must use interarrival rate I understand that you need to use an interarrival time, but not necessarily exponential. In this case you can choose any other distribution from a list of distributions (See below).
If you want those arrivals to be uniformly distributed, use the uniform distribution.
Or if you want them to arrive exactly every 5 minutes, create a bulk of the agents, delay them for 5 minutes and let them in to the system.
I used MATLAB to simulate the cascading failure of two interdependent networks/Layers (I generated the two layers based on small world - watts strogatz algorithm).
My code works fine but it is not time dependent.
I want to have time steps, for example, the initial attack on one node happens at t1 then after some time the next vulnerable nodes get failed at different time t2 and so on for the other failure events. My code emulates only telecommunication nodes ( all events happen instantaneously), I want it to work for other logistic networks, say social networks for example, where the timestamp for every event might be in minutes or hours. Your thoughts and ideas would appreciated.
Note: I can provide my code if this helps.
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.