I have some issues with gathering statistics on the population level in a model I've been working with.
In the model I have an agent type Company and RawMaterial. Within Company a process flow exists, where on some blocks costs are assigned to a variable in Company upon entry of RawMaterial (e.g. cost = gamma(3, 125, 0);)
To calculate the Company-level cumulative costs I use a Statistics object with cost in the value field of this object.
So far so good it seems.
However, when I want to sum the cumulative costs of all Company agents into one value I run into trouble. Ideally, I want the cumulative costs for each Company agent to be plotted in Main.
I've looked at the Help file (section "Functions to collect statistics on agent population") with no success.
what about doing this in main? (you can even put this function in your time plot)
sum( companies, c->c.cost );
This function calculates the sum of the costs of all company agents (as long as you have a population of agents called companies in main, and not only an agent type)
If you don't have companies as a population of agents, you have to create it, otherwise it's very difficult to calculate anything. How to create it depends on your model.
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).
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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'm working on a family practitioner model, my goal is to model the population of a city and all of the family practitioners in that given city by taking into account the distribution of the treatment time and the arrival schedule of patients etc... So far i've managed to create an agent that contains a bloc diagram to model the whole process
With a source and a delay bloc to generate patients according to a fixed schedule, a service bloc and two sinks.
Now i've put a population of this agent "process" in the main and i've simulated the model for 3 months.
Lets say i've started with a population of 10 process which represents the number of practitioners. My goal now to to collect some Mean statistics, like the Mean number of treated patients (treated patients goes to the sink "Served") of the 10 practitioners, the Mean waiting time of patients at the service bloc, the mean utilization ratio of the resource of the service bloc...
I want to also know if is it possible to limit the total number of Patients (or to at least count them), for exemple, instead of simulating the model for 3 months, i wanna limit the total number of patients that goes to the 10 process to 10k and i want to know how much time does it take to serve all of them. (is it possible with this architecture of the model or do i have to make major changes)
Thank you
I am trying to create a dynamic environment in anylogic with portfolio performance over the time.
The return each year should be dynamic (random) using triangular or normal
Example triangular (-0.5,0.1,0.5) or normal(0.05,0.08)
That means sometimes it is positive and sometimes it's negative
Dynamic variable/parameter is 'Return1'
Flow is 'earn'
Stock is 'portfolio'
I use a variable Return1 = normal(0.05,0.08)
A Flow earn = portfolio*Return1
The stock is called Portfolio and initial value of 100. It accumulates the profit/loss each year
Unfortunately the results in the accumulated portfolio are not correct.
If I use Return1=0.1 or for example -0.05 (fixed prices) it works perfectly.
It seems there are issues in anylogic system dynamics with randomness.
I would appreciate if you can help how I could simulate the portfolio performance in this way
To do what you want, you need to recalculate the random variable in your variable every year. Maybe create an event that runs once per year that changes the value of the variable.
In the event you would have every year:
Return1 = normal(0.05,0.08)
The variable Return1 is not magically updated automatically based on your own desires and wishes, unless you tell Anylogic explicitely.
If return1 is a dynamic variable, then the value will be recalculated every time step, which will lead to unexpected results that will be maybe equivalent to using the average of your random value.
If your time step is 1 year... well in AnyLogic you can't really trust that time step unfortunately.
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 am working on an exercise to build influencer score for each user in my data set. What that means is that a user with higher engagement should get higher score and vice versa. however, i have many different type of engagement variables and i am not sure which one should weight higher.
so, i first did a cluster analysis to divide users into different group based on engagement activity using 5 different types of engagement. Based on this, i found that one of the cluster has high level of engagement across all the different types of engagement variables. This is the group i am interested in. however, it is possible that the group size i get may be smaller than the number of users i want to use in future. so, i want to now use these clusters and create a propensity score.
e.g. in the cluster analysis, say i get 5 clusters c1, c2,c3,c4,c5 and c5 is my cluster of interest. so, i give all users in c5 a value of 1 (= influencer) and i give all users in c1 to c4 a value of 0 (= not influencer). now, i use this binary variable and build a logistic regression model (using same engagement variables as used for clustering) to get propensity for everyone to an influencer. this way, i can change the threshold to reduce or increase the numbers of users i want to select.
Now, the issue i am running in is that one of the engagement variable is able to predict influencer very well and hence my propensity scores are very close to either 1 or 0 which defeats the purpose of why i wanted the propensity score in the first place.
S0, 2 questions -
1) is this approach of building a unsupervised classification and then using this to build supervised classification a sound approach of what i am trying to do?
2) how do i reduce the contribution from the variable that predicts influencer really well to ensure that i get much more smoother curve instead of values near 0 or 1. i don't want to remove this variable from the model as this is important from business perspective.