I would like to determine the time my forklifts are storing the pallets into my pallet rack. So for example the overall storing time for 50 pallets should last 30 minutes. Currently I define the overall storing time trough the speed of the forklifts but it is to inaccurate.
This is not possible as you cannot predict the path they will take to store something (else you could work out the speed from your required time).
PS: As mentioned before, it appears you are modelling things backwards: you take observed data and try to push it into a model. But simulation works the other way around: Take real-world performance (speed of your forklifts) and learn the performance of the system as an output.
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
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 !!
When committing to/setting SLAs for a service, what time period should the SLA be calculated over?
For example, if I wanted all the services in my organization to commit to P95 latency, and one of the services commits to 500ms, what is the time window - because the P95 will be different based on the time window we look at.
Depends on in what cycles your latency fluctuates.
No daily / hourly peaks? A couple thousand samples do just fine.
Daily fluctuations (e.g. peak usage, concurrent backups etc.)? Then you will need to measure at least a whole day.
Weekly fluctuations (e.g. tied to work hours or evening activities etc.)? Then you will need to sample over a full week.
There is no strict requirement to sample everything over the chosen time window, but your time window better be representative or you may be held liable. Also make sure to be fair when you under-sample.
If you want to be on the safe side, take the worst-case-scenario in your load cycle, and within that scenario take a full minute worth of samples. That gives you a good estimate what will be held against you.
I have a model with agents: cars, passengers and petrol stations. The population size of the cars is 500 and there are 5 petrol stations. The passengers here are generated randomly and I have capped the passengers on the map to be at a maximum of 500 at any point in time (the limit here is set to be the same as the population size of the cars).
However, the simulation speed is running really slow (like 1-2s/sec) on virtual mode. Could this be due to the complicated model built or are there any ways on how we can speed up the running time of the simulation?
Many factors can slow your model, here are just some ideas:
Do you use Pedestrian agent types for your passengers? Try not to, unless ped dynamics are crucial
do you use conditional transitions? They are evualated at every event in the model and can slow it. You can always replace them with message-based transitions easily
Do you use the road traffic library? Again, only apply it if car dynamics are really necessary, else revert to the process library
Do you read/write a lot of data during the sim run to the dbase or an external file? Avoid that and do it at the start/end
Also, it could just be inefficient coding. You can check that using a Java profiler. Easiest is to use the one that comes with any Java JDK.
Good luck
I got the problem of the pedestrian library. I would like to use the pedestrian library to simulate the behavior of the vehicle, therefore, I would like to set the comfortable speed of the agent to be, say 70km/h. However, there is an error saying the speed has to be within [0mps, 10mps]. Is there any way to set whatever speed I want?
Thank you,
Jiannan
First why don't you use the road traffic library? But you must have your reasons.
You can't change the maximum speed value, but what you can do, which is not a very elegant solution, but a solution nonetheless, is to change the time scale of your model. There is no direct option for that, so you basically have to create your own clock that runs your way.
So for instance, if you want your car to move at 20mps, you choose 10 mps as the comfortable speed and you have the transformation customTime(time())=0.5*time(), where custom time is your own function.
So every time 1 second passes in the simulation, in your custom clock only 0.5 seconds will pass... You just have to take care of the statistics you are collecting so they fit your time scale.
Also, you will have to change everything according to your clock in your model, because Anylogic will continue showing you things in normal seconds.
Hope that helps :)