Can someone indicate or explain the possibility of the function randomWhere overlooking the whole population when looking for an agent. In other words, does randomWhere sample the agents' population in any case?
I am matching agents base on age conditions using randomWhere. However, I have noticed that the function returns null despite the availability of candidate agents. My naïve workaround to this issue is to call the function more than once to maximise the matching process. Any suggestions to this issue?
Regards,
S
As per the help documentation, randomWhere does sample across the entire population.
https://anylogic.help/anylogic/stochastic/selecting-random.html#element-from-collection
If your function is returning null then most likely there is something wrong in the logic or the setup
Related
Benjamin, thanks for your reply. I really appreciate your answer because you know how desperate one gets when one has no idea what to try.
I did it using the suggestion to select the nodes and right-click, create collection. I must be forgetting something because it didn't work for me.
COLLECTION DEFINITION
Let's see if I'm understanding, in the Anylogic example, they use it to create pedestrians on the train platform, at the 32 doors.
I want to use that tool to avoid having to make a programming line for each room (cr) to evacuate pedestrians to a safe point.
I include the screenshot of the PedSource. I'm looking at what the light bulb has... so I should start with self or ped. But, the Anylogic model has no self and no ped. Any additional ideas?
New PedSource
XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX
I have been studying several of Anylogic's examples. In this case, the "Subway Platform".
SUBWAY PLATFORM LOGIC
Anylogic uses a collection of doors to simulate the train doors where pedestrians will appear, using target lines. In the PedSource, they describe it as Target Line: doors1.get(index)
SP PEDSOURCE
I want to achieve the same thing using nodes. My collection is called cr_Ele and I have written in the Ped Source > Node: cr_Ele.get(index).
My COLLECTION OF NODES
MY PEDSOURCE
Running it gives me the following error, "index cannot be resolved to a variable". Does anyone have any idea what might be causing the error?
This is my first question on this platform, I hope I did it right!
great question, well done :)
The problem is that your collection is defined as a group of Object elements, see your "Element class" entry. Compare to what AnyLogic did: Their element class is set to "Other" specifying the actual type:
You can do the same thing. Easiest is to select several of your nodes, right-click, select "Create collection". This will give you this collection:
Alternatively, you can use code-complete and learn the object types from the API.
Now, you can access individual PolygonalNode elements from your collection using `myCollection.get(0)´ (or any other index)
I've built a model in which a fleet of trucks delivers multiple orders to different customers. This model works fine when I perform one simulation experiment. However, when I try to run a parameter variation, the following error occurs: 'Error in the model during iteration x'. A snapshot of that particular error can be found in 2.
A question about this topic is earlier asked here:
NullPointerException during Parameter Variation Experiment with agent statistics
I have tried the tips given in that post but none of them seems to solve the problem.
I have replaced all the conditional transitions with messages in my state chart (see figure).
My data sets are stored in the database, so that cannot be the problem.
I can't get my head around why the model works with some seed values and with some not. I understand that finding the modelling flaw from just the snapshot is hard, but any tips on how I could find the mistake could be helpful.
PS: I have the learning edition so there is no debugger
Edit:
The error happens at a specific line of code written in the transitions pointing towards the state from the state "movingToClient1". The line that seems to cause the error is:
Order order = orderStore.myOrdercollection.get(0);
the iterations seem to work. However, I need it to be equal to one (to specifically measure certain KPIs of the last route). Hopefully, this helps in finding a solution.
The most likely thing to cause the problem is that your arraylist called collectionOfOrders is missused.
so at some point on the "on enter" of one of your states, you do :
collectionOfOrders.get(something)
when collectionOfOrders is actually empty.
sometimeswhat happens is that multiple things happen at the same time in your model, and when you ask if collectionOfOrders==1, another of your truck agents does the same and they both return true, which means that one of them will get the issue.
This happens only with certain seeds, because it occurs with a very low probability.
This is my guess, with the current information provided
Due to the insight given by Felipe and Benjamin, I found the problem in my model. My model starts with an import order with a specific arrival rate of one in a source block. The rate of 1 is equivalent to exponentially distributed interarrival time with mean = 1/ratedefined (https://anylogic.help/library-reference-guides/process-modeling-library/source.html). This means that it is possible for some seed values that the orders are generated at the same time. Therefore, changing the setting from 'rate' to 'interarrival time' solved the problem.
My team has a plan to apply optaplanner to existing system.
Existing system has its own rule-sets.
it tries own rule-sets one by one and pick the one as best result.
We want to start from its result as heuristics
and start to solve the problem as meta-heuristics.
We have reviewed optaplanner manual especially in repeated planning section.
but we can't find the way.
Is there a way to accept existing system's result?
your cooperation would be highly appreciated
Best regards.
For OptaPlanner, it makes no difference where the input solution comes from. Consider the following code:
MyPlanningSolution solution = readSolution();
Solver<MyPlanningSolution> solver = SolverFactory.create(...)
.buildSolver();
solver.solve(solution);
Notice how solution comes from a custom method, readSolution(). Whether that method generates the initial solution randomly, reads it from a file, from a database etc., that does not matter to the solver. It also does not matter if it is initialized or not - construction heuristic, if configured, will just skip the initialized entities.
That means you have absolute freedom in how you create your initial solution and, to the solver, they all look the same.
At my company we are currently using optaplanner to solve a vehicle routing problem with great results, we built a web app to manage vehicles, clientes, locations, depots and to show a graphic representation of the solution (including showing the locations in a map). We wrapped the solver in a spring java app with a rest interface to receive request and solve the problem. We are using Google maps to get distance-time data. Now we need to implement multirip....
To tackle the multitrip part I am following this approach:
1.- I added readyTime, endOfTrip and dueTime members to Vehicle class
2.- I created a rule to prevent arrivals at customers after vehicle->dueTime
3.- I modified the ArrivalTimeUpdateListener to consider the vehicle->readyTime when calculating the departureTime from a vehicle (using Math.max(depot->readyTime, vehicle->readyTime)
4.- At this point I started using the vehicle class as if it were a vehicle trip instead of a vehicle (I still don´t change the name but that is the idea )
5.- I created a member nextVehicle in vehicle to represent the next trip
6.- For testing purposes I manually link two vehicles (or vehicle trips) before sending it to solver->solve
7.- In the ArrivalTimeUpdatingVariableListener class I extended the method that updates the arrival times to consider updating the nextVehicle->readyTime and by consequence the arrival times of the customers that belong to the next trip (and so on when there are more than two trips)
I am sure this is not the most elegant solution, but I tried other approaches (using custom shadow variable on Vehicle for instance) but it couldn´t make it work.
The problem I am facing right now is that I don´t get to understand the state of the model when ArrivalTimeUpdatingVariableListener is called, maybe someone faced similar problem and can help me. What i found (after try and error) is:
the customer.getVehice() method not always returns a value (distinct from null value), it seems to get updated some time after the previousStandstill change triggers the listener "updateArrivalTime" method.
In construction fase when a customer get assign to a vehicle the customer.getVehice() method returns null (it came from "not assigned")
In construction fase when a customer "is freed" the customer.getVehice() method returns the "previous vehicle"
In local search fase when a customer get assign to a vehicle the customer.getVehice() method returns the "previous vehicle" (original vehicle)
In local search fase when a customer go back to the original vehicle the customer.getVehice() method returns the "previous assign vehicle"
Any thoughts on this? Am I making right assumptions? (because originally I considered customer.getVehicle() as the "actual" vehicle and the solutions were completely wrong...)
The order of triggering the previousStandstill change it´s kind of difficult to understand (for me). I mean when moving customers or swapping them between vehicles...any thoughts or hints on were to find info?
Can I access some variables from the "previous state of the model" when the solver makes a move?, because I am thinking I will need that if I continue with this approach (to update the vehicle->endOfTrip that is the nextVehicle->readyTime when the customer is the last one on the chain for instance)
and finally...am i doing something completely wrong conceptually ?
Any comments will be greatly appreciated (and sorry my grammar, I am native spanish speaker)
The chaotic triggering of shadow vars that you describe should not happen any more in optaplanner 6.3.0.Final or higher because it now gives the guarantee shown below.
But older versions (optaplanner 6.2 and earlier) suffer from that chaotic trigger behaviour (as fixed by PLANNER-252 - yes I know that issue number by heart - and yes, I am not the only one) could drive a developer (who's working on a complex model with multiple shadow variable(s)) insane and provide a one way ticket to the asylum.
Fortunately it has been fixed a few months ago, so upgrade to 6.3.0.Final or later and keep your sanity.
I'd be interested to know what strategies people are using to work backwards from the results of a model (from observed emergent behaviour) to try to answer the question: what is it about individual turtles that has led to this macro behaviour?
Are you asking, "How can you guess what rules govern agents [turtles], given the macro-level behavior?"? Or are you asking, "Given that you have the source code, how to do you figure out what it is about the source that generates the macro-level behavior?"?
Answering the first question is very hard, often. I don't have suggestions.
Answering the second can be hard, too.
One strategy is to experiment with different initial configurations, or experiment with different rules in agents [turtles]. If you have no guesses about what to vary, make arbitrary choices at first, until you begin to have guesses, or even vague intuitions. Then vary the code in ways that will explore whether your guesses are correct, and in ways that will allow you to refine your guesses. This strategy won't always work.
Perhaps it might be useful to try to think through how you could generate the macro-level behavior, if that's what you wanted to produce. You might not have any idea of what an answer might be--this would amount to answering the first question above--but if you do, it might lead to guesses that you could use for the preceding strategy.
I do this when I am debugging because sometimes I get unexpected macro-behaviour and I need to determine whether it is real or an error. Typically what I would do is set the random seed then look at which agents are involved in the behaviour and when (ticks in NetLogo) it occurs. I would then rerun the model and stop it a few ticks beforehand and inspect the agents that I know are going to be involved in the behaviour to see if there's something unusual about them or their environment. Macro behaviour typically occurs because of the interactions of agents, environment and events so I am trying to determine WHAT those elements are, before seeking to explain how they combine to create the behaviour. Once that's done, I can usually trace the code (dropping in a print statement and inspecting the relevant agents) to work out how it occurred.