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 !!
Related
I am developing a logistics simulation in the factory by Anylogic. It's a pick up and delivery problem, where the AGVs need to pick up the parcel and deliver to the target location. All the AGVs are traveling following paths. The paths have different speed limits.
My goal is to reduce the time of traffic jam or waiting time for jobs to be picked up.
I have the leading time, job delivered time - job generated time.
But I from here, I want to identify the time of traffic jam or waiting time.
Is there a way to calculate the time from one spot to the other considering different speed limit of paths without waiting time or traffic jam? So that I could subtract this from leading time.
Let me know if I need to clarify something.
There is no build-in way to do this, you have to do it yourself. I have 3 ideas:
You compute this mathematically in the model yourself, i.e. write a function that computes the length of the total path and you have the ideal speed already, voila
You run a separate experiment and turn off all speed limits and other traffic: record the time in that ideal case and use that to compare
Similarly, you could do this in the same experiment during a warmup period: drive a fake transporter along the path and compute the perfect durations
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 am creating a system dynamic and agent-based model for my dissertation.
Numbers generated through the different flows must be added back to the start to continue through the process.
For example, numbers flow from a parameter to stock 1, which goes through a flow process at a specific rate to stock 2. From stock 2, there is another flow process based on a particular rate to stock 3. The numbers from stock 3 need to go back into stock 1 to repeat the process.
Methods I have tried have been adding flows, links, and changing the initial value of stock 1.
Any help or suggestions are greatly appreciated!
Updated:
Added screenshots.
I think it is because of the difference between the two flows, e.g. a -9 based on the difference between flow and flow3 as shown in the screenshot.
Screenshots:
Graph of Stock 1
Model as a whole
In system dynamics, if you want to have a circular system (feedback loop) it needs to contain as a minimum 1 stock inside the loop, which means that there is at least 1 delay in the feedback loop
I will explain your model
stock has an outflow of 20 (flow), and an inflow of 11 (flow3)... this produces a net outflow of 9/timeUnit
This is what you see in the graph... and it doesn't even matter if your system is circular or not, that stock will lose 9/timeUnit forever.
Your wording is very strange when you say "numbers are generated and numbers are flowing"... it's not numbers that flow through your system... you can't really say "i have 3 numbers per minute flowing into a pool of numbers"
In your model, the "numbers" are definitely going back to the initial stock, but system dynamics is like water flowing, it is not a discrete paradigm, so you will not see the same "numbers" going back because system dynamics doesn't differentiate what individual "numbers" are flowing.
It's so weird already to have to use the word numbers to be consistent with your question.
So in order to have a better answer, you will need to specify:
what is the behavior you see in here
what is the behavior you expect, and how it differs from what you see
what would you need to see in the system in order to say "yes, my system in working exactly as expected"
It would help if you let us know what your system represents, and if you use names that represents what is flowing through the system (instead of using numbers, stock and flow, because any explanation becomes confusing)
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.