I am having trouble with resource priority in simpy. Consider the following code:
import simpy
env = simpy.Environment()
res = simpy.PriorityResource(env, capacity = 1)
def go(id):
with res.request(priority = id) as req:
yield req
print id,res
env.process(go(3))
env.process(go(2))
env.process(go(4))
env.process(go(5))
env.process(go(1))
env.run()
Lower number means higher priority, so I should get 1,2,3,4,5. But instead i am getting 3,1,2,4,5. So the first output is wrong, after that its sorted!
Thanks in advance for your help.
This is correct. When "3" requests the resource, it is empty so it gets the
slot. The remaining processes have to queue and will get the resource in the
order 1, 2, 4, 5.
If you use the PreemptiveResource instead (like request(priority=id,
preempt=True)), 3 will still get the resource first but will be preempted by
2. 2 will then get preempted by 1. 2 and 3 would then have to request the
resource again to gain access to it.
Even I had the same problem where I was supposed to make a factory FIFO. At that time I assigned a reaction time to a part and made it to follow the previous part. That is only if the previous part had got into service of resource, I made the next part request. It solved the problem objectively but seemed like it slowed down the simulation little and also gave a rexn time to the part. It was basically a revamp of the factory process. But I would love to see a feature when the part doesn't have to request again.
Can it be done in the present version?
Related
I have a pretty simple Anylogic DE model where POs are launched regularly, and a certain amount of material gets to the incoming Queue in one shot (See Sample Picture below). Then the Manufacturing process starts using that material at a regular rate, but I want to check if the material in the queue gets outdated, so I'm using the TimeOut option of that queue, in order to scrap the outdated material (older than 40wks).
The problem is that every time that some material gets scrapped through this Timeout exit, the downstream Manufacturing process "stops" pulling more material, instead of continuing, and it does not get restarted until a new batch of material gets received into the Queue.
What am I doing wrong here? Thanks a lot in advance!!
Kindest regards
Your situation is interesting because there doesn't seem to be anything wrong with what you're doing. So even though what you are doing seems to be correct, I will provide you with a workaround. Instead of the Queue block, use a Wait block. You can assign a timeout and link the timeout port just like you did for the queue (seem image at the end of the answer).
In the On Enter field of the wait block (which I will assume is named Fridge), write the following code:
if( MFG.size() < MFG.capacity ) {
self.free(agent);
}
In the On Enter of MFG block write the following:
if( self.size() < self.capacity && Fridge.size() > 0 ) {
Fridge.free(Fridge.get(0));
}
And finally, in the On Exit of your MFG block write the following:
if( Fridge.size() > 0 ) {
Fridge.free(Fridge.get(0));
}
What we are doing in the above, is we are manually pushing the agents. Each time an agent is processed, the model checks if there is capacity to send more, if yes, a new agent is sent.
I know this is an unpleasant workaround, but it provides you with a solution until AnyLogic support can figure it out.
I have 2 storages (called storageA & storageB) and I want to move an agent (pallet) from one to the other via forklifts. I have set up the following.
A pallet is created at a node and is moved to storageA via 'store'. This part works fine. The pallet is then moved to storageB via 'store1' after a delay. This is when the following error occurs:
Exception during discrete event execution:
root.store1.seizeTrans.freeSpaceSendTo:
Path not found! {agent=2, source={level=level, pos=(1673.3333333333333, 3245.0, 0.0)}, target={level=level, pos=(1857.25, 3160.4845, 0.0)}}
It works if I replace 'store1' with a retrieve block and send it to a node first. However I would like to send the pallet directly to another storage rather than via another location. Is this possible?
Please let me know if I have not provided enough information.
Thanks
yeah unfortunately you can't do that as far as I know, the solution I use is the following, which is actually not a super robust solution... but has been ok in applications so far
Place a retrieve block between your delay and your store1
Use the agent you pick up as destination:
on the on seize action of the retrieve block do:agent.transporter=unit;
4.On the store1 block put the highest priority for the task
5. ON the store1 block use resource custom transporter choice: agent.transporter.equals(unit)
6. The dispatching policy should be nearest to the agent in store1, but doing all the above ensures that the resource continues doing the task no matter what... by only using the dispatch policy your model will work 99.999999% of the time... the problem occurs only if another task with higher priority occurs at the exact same time as the transporter is released in the retrieve block, which is rare, but can happen.
I had the same question today so I landed here. But luckily, only after the second step written above, the whole process needed did already work for my case. We can move an agent from one storage to another by simply set the destination of the 'retrieve' block to the coordinate of the agent and the move to independently instead of by fleets or resources. after that we put the 'store' block.
Destination is: (x,y,z)
X: agent.getX()
Y: agent.getY()
Z: agent.getZ()
after agents being retrieved to a specified coordinate, it seems that fleets do not comply paths in the network anymore
I'm trying to create an agent statechart where a transition should happen every day at 4 pm (except weekends).
I have already tried:
1. a conditional transition (condition: getHourOfDay() == 16)
2: A timeout transition that will "reinsert" my agent into the chart every 1 s and check if time = 16.
My code is still not running, does anyone have any idea how to solve it?
This is my statechart view. Customer is a single resource that is supposed to "get" the products out of my stock everyday at 4pm. It is supposed to happen in the "Active" state.
I have set a timeout transition (from Active-Active) that runs every 1s.
Inside my "Active" state in the "entrance action" i wrote my code to check if it is 4 pm and run my action if so.
I thought since i set a timeout transition it would check my condition every 1s, but apparently it is not working.
Your agent does not enter the Active state via an internal transition.
Redraw the transition to actually go out of the Active state and then enter it again as below:
Don't use condition-based transitions, for performance reasons. In your case, it also never triggers because it is only evaluated when something happens in the model. Incidentally, that is not the case at 4pm.
re your timeout approach: Why would you "reinsert" your agent into its own statechart? Not sure I understand...
Why not set up a schedule or event with your recurrence requirement and make it send a message to the statechart: stateChart.fireEvent("trigger!");. In your statechart, add a message-based transition that waits for this message. This will work.
Be careful to understand the difference between the Statechart.fireEvent() and the Statechart.receiveMessage() functions, though.
PS: and agree with Felipe: please start using SOF the way it is meant, i.e. also mark replies as solved. It helps us but also future users to quickly find solutions :-) cheers
I have many data and I have experimented with partitions of cardinality [20k, 200k+].
I call it like that:
from pyspark.mllib.clustering import KMeans, KMeansModel
C0 = KMeans.train(first, 8192, initializationMode='random', maxIterations=10, seed=None)
C0 = KMeans.train(second, 8192, initializationMode='random', maxIterations=10, seed=None)
and I see that initRandom() calls takeSample() once.
Then the takeSample() implementation doesn't seem to call itself or something like that, so I would expect KMeans() to call takeSample() once. So why the monitor shows two takeSample()s per KMeans()?
Note: I execute more KMeans() and they all invoke two takeSample()s, regardless of the data being .cache()'d or not.
Moreover, the number of partitions doesn't affect the number takeSample() is called, it's constant to 2.
I am using Spark 1.6.2 (and I cannot upgrade) and my application is in Python, if that matters!
I brought this to the mailing list of the Spark devs, so I am updating:
Details of 1st takeSample():
Details of 2nd takeSample():
where one can see that the same code is executed.
As suggested by Shivaram Venkataraman in Spark's mailing list:
I think takeSample itself runs multiple jobs if the amount of samples
collected in the first pass is not enough. The comment and code path
at GitHub
should explain when this happens. Also you can confirm this by
checking if the logWarning shows up in your logs.
// If the first sample didn't turn out large enough, keep trying to take samples;
// this shouldn't happen often because we use a big multiplier for the initial size
var numIters = 0
while (samples.length < num) {
logWarning(s"Needed to re-sample due to insufficient sample size. Repeat #$numIters")
samples = this.sample(withReplacement, fraction, rand.nextInt()).collect()
numIters += 1
}
However, as one can see, the 2nd comment said it shouldn't happen often, and it does happen always to me, so if anyone has another idea, please let me know.
It was also suggested that this was a problem of the UI and takeSample() was actually called only once, but that was just hot air.
I added the ORKOrderedTask.fitnessCheckTaskWithIdentifier Task and it renders find in the UI. But unlike other simpler tasks containing scale/choice/date questions, I was not able to find the exact way to read the sensor data collected via ORKOrderedTask.fitnessCheckTaskWithIdentifier.
I have used the following:
private var walkingTask : ORKTask {
return ORKOrderedTask.fitnessCheckTaskWithIdentifier("shortWalkTask", intendedUseDescription: "Take a short walk", walkDuration: 10, restDuration: 5, options: nil)
}
upon task completion the task view controller delegate below is hit.
//ORKTaskViewControllerDelegate
func taskViewController(taskViewController: ORKTaskViewController, didFinishWithReason reason: ORKTaskViewControllerFinishReason, error: NSError?)
is there a way to drill down into the result object contained in task view controller (taskViewController.result) to get the step count? Or will i have to go through health kit or something and then query the required observation? Request help from anyone who has used this task before and can provide some input on how to fetch the pedometer data (step count specifically) for the duration the task was active?
I'm using swift.
The step count is not reflected in the result objects per se. Instead, one of the child ORKFileResult objects, generated from the pedometer recorder, will contain the pedometer records queried from CoreMotion, serialized to JSON.
However, exposing the step count on a result object, sounds like a useful extension / improvement, and we should see if it generalizes to other recorders too. Please open an issue on GitHub and we will see what we can do!