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.
Related
I have a problem where I have several events that are occurring in a project, the events happen semi-concurrently, where they do not start at the same time but multiple can still be occurring at once.
Each event is a team of people working on a linear task, starting at the beginning and then working their way to the end. Their progress is based on a physical distance.
I essentially need to figure out each events start time in order for no teams to be at the same location, nor passing eachother, at any point.
I am trying to program this in MATLAB so that the output would be the start and end time for each event. The idea would be to optimize the total time taken for the project.
I am not sure where to begin with something like this so any advice would be greatly appreciated.
If I understand correct, you just want to optimize the "calendar" of events with limited resources (aka space/teams).
This kind of problems are those called NP and there is no "easy" way to search for the best solution.
You here have two options:
Greedy like algorithm: You will have your solution in a resonable time but it won't be the best one.
Brute force like algorithm: You will find the best solution but maybe not in the time you need it.
Usually if the amount of events is low you can go for 2nd option but if don't you may need to go for the first one.
No mather which one you choose first thing you will need to do is to compute if a solution is valid. What does this mean? It means to check for every event wheter if it collisions whith others in time, space and teams.
So lets imagine the problem of making the calendar on a University. There you have to think about:
Students
Teacher
Classroom
So for each event I have to check if another event have same students, teacher or classroom at the same time. First of all I will check the events that match in time with the actual event. Then I will compare the actual event with all the others.
Once you have this done you could just write a greedy algorithm that starts placing events on time just checking if it collides with some other.
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 am solving a variation on vehicle routing problem. The model worked until I implemented a change where certain vehicles and/or stops may remain unassigned because the construction filter does not allow the move due to time window considerations (late arrival not allowed).
The problem size is 2 trucks/3 stops. truck_1 has 2 stops (Stop_1 and Stop_2) assigned to it, and consequently 1 truck and 1 stop remain unassigned since truck_2 will arrive late to Stop_3.
I have the following error:
INFO o.o.c.i.c.DefaultConstructionHeuristicPhase - Construction Heuristic phase (0) ended: step total (2), time spent (141), best score (-164hard/19387soft).
java.lang.IllegalStateException: Local Search phase started with an uninitialized Solution. First initialize the Solution. For example, run a Construction Heuristic phase first.
at org.optaplanner.core.impl.localsearch.DefaultLocalSearchPhase.phaseStarted(DefaultLocalSearchPhase.java:119)
at org.optaplanner.core.impl.localsearch.DefaultLocalSearchPhase.solve(DefaultLocalSearchPhase.java:60)
at org.optaplanner.core.impl.solver.DefaultSolver.runPhases(DefaultSolver.java:213)
at org.optaplanner.core.impl.solver.DefaultSolver.solve(DefaultSolver.java:176)
I tried to set the planning variable to null (nullable = true) but it seems it is not allowed in case of chained variables.
I am using Optaplanner 6.2.
Please help,
Thank you,
Piyush
Your construction filter may be too restrictive, it could prevent the construction heuristic from creating an initialized solution. You should remove the time window constraint from the construction filter and add it as a hard score constraint in your score calculator instead.
From the Optaplanner docs:
Instead of implementing a hard constraint, it can sometimes be built in. For example: If Lecture A should never be assigned to Room X, but it uses ValueRangeProvider on Solution, so the Solver will often try to assign it to Room X too (only to find out that it breaks a hard constraint). Use a ValueRangeProvider on the planning entity or filtered selection to define that Course A should only be assigned a Room different than X.
This can give a good performance gain in some use cases, not just because the score calculation is faster, but mainly because most optimization algorithms will spend less time evaluating unfeasible solutions. However, usually this not a good idea because there is a real risk of trading short term benefits for long term harm:
Many optimization algorithms rely on the freedom to break hard constraints when changing planning entities, to get out of local optima.
Both implementation approaches have limitations (feature compatiblity, disabling automatic performance optimizations, ...), as explained in their documentation.
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.
I read about how FoundationDB does its network testing/simulation here: http://www.slideshare.net/FoundationDB/deterministic-simulation-testing
I would like to implement something very similar, but cannot figure out how they actually did implement it. How would one go about writing, for example, a C++ class that does what they do. Is it possible to do the kind of simulation they do without doing any code generation (as they presumeably do)?
Also: How can a simulation be repeated, if it contains random events?? Each time the simulation would require to choose a new random value and thus be not the same run as the one before. Maybe I am missing something here...hope somebody can shed a bit of light on the matter.
You can find a little bit more detail in the talk that went along with those slides here: https://www.youtube.com/watch?v=4fFDFbi3toc
As for the determinism question, you're right that a simulation cannot be repeated exactly unless all possible sources of randomness and other non-determinism are carefully controlled. To that end:
(1) Generate all random numbers from a PRNG that you seed with a known value.
(2) Avoid any sort of branching or conditionals based on facts about the world which you don't control (e.g. the time of day, the load on the machine, etc.), or if you can't help that, then pseudo-randomly simulate those things too.
(3) Ensure that whatever mechanism you pick for concurrency has a mode in which it can guarantee a deterministic execution order.
Since it's easy to mess all those things up, you'll also want to have a way of checking whether determinism has been violated.
All of this is covered in greater detail in the talk that I linked above.
In the sims I've built the biggest issue with repeatability ends up being proper seed management (as per the previous answer). You want your simulations to give different results only when you supply a different seed to your random number generators than before.
After that the biggest issue I've seen seems tends to be making sure you don't iterate over collections with nondeterministic ordering. For instance, in Java, you'd use a LinkedHashMap instead of a HashMap.