I have 100 cubic meter tank and I have 133,333 bottles to fill. When tank get fill I am using
source.injact(133,333)
My first issue was a lack of memory, which I resolved, and now agents are being produced; however, once the source began producing, the simulation stuck and run very slow. How can I solve this issue?
if you have 100,000 agents and if all of them are using the fluid library, there's a chance that things will be very slow.
That's all i can say with the information provided.
I wouldn't use the fluid library inside a bottle agent... I would model that differently (if you are doing that, which i don't know if you are)
It can also be that your computer is slow
Related
I run an infectious disease spread model similar to "VIRUS" model in the model library changing the "infectiousness".
I did 20 runs each for infectiousness values 98% , 95% , 93% and the Maximum infected count was 74.05 , 73 ,78.9 respectively. (peak was at tick 38 for all 3 infectiousness values)
[I took the average of the infected count for each tick and took the maximum of these averages as the "maximum infected".]
I was expecting the maximum infected count to decrease when the infectiousness is reduced, but it didn't. As per what I understood this happens, because I considered the average values of each simulation run. (It is like I am considering a new simulation run with average infected count for each tick ).
I want to say that, I am considering all 20 simulation runs. Is there a way to do that other than the way I used the average?
In the Models Library Virus model with default parameter settings at other values, and those high infectiousness values, what I see when I run the model is a periodic variation in the numbers three classes of person. Look at the plot in the lower left corner, and you'll see this. What is happening, I believe, is this:
When there are many healthy, non-immune people, that means that there are many people who can get infected, so the number of infected people goes up, and the number of healthy people goes down.
Soon after that, the number of sick, infectious people goes down, because they either die or become immune.
Since there are now more immune people, and fewer infectious people, the number of non-immune healthy grows; they are reproducing. (See "How it works" in the Info tab.) But now we have returned to the situation in step 1, ... so the cycle continues.
If your model is sufficiently similar to the Models Library Virus model, I'd bet that this is part of what's happening. If you don't have a plot window like the Virus model, I recommend adding it.
Also, you didn't say how many ticks you are running the model for. If you run it for a short number of ticks, you won't notice the periodic behavior, but that doesn't mean it hasn't begun.
What this all means that increasing infectiousness wouldn't necessarily increase the maximum number infected: a faster rate of infection means that the number of individuals who can infected drops faster. I'm not sure that the maximum number infected over the whole run is an interesting number, with this model and a high infectiousness value. It depends what you are trying to understand.
One of the great things about NetLogo and some other ABM systems is that you can watch the system evolve over time, using various tools such as plots, monitors, etc. as well as just looking at the agents move around or change states over time. This can help you understand what is going on in a way that a single number like an average won't. Then you can use this insight to figure out a more informative way of measuring what is happening.
Another model where you can see a similar kind of periodic pattern is Wolf-Sheep Predation. I recommend looking at that. It may be easier to understand the pattern. (If you are interested in mathematical models of this kind of phenomenon, look up Lotka-Volterra models.)
(Real virus transmission can be more complicated, because a person (or other animal) is a kind of big "island" where viruses can reproduce quickly. If they reproduce too quickly, this can kill the host, and prevent further transmission of the virus. Sometimes a virus that reproduces more slowly can harm more people, because there is time for them to infect others. This blog post by Elliott Sober gives a relatively simple mathematical introduction to some of the issues involved, but his simple mathematical models don't take into account all of the complications involved in real virus transmission.)
EDIT: You added a comment Lawan, saying that you are interested in modeling COVID-19 transmission. This paper, Variation and multilevel selection of SARS‐CoV‐2 by Blackstone, Blackstone, and Berg, suggests that some of the dynamics that I mentioned in the preceding remarks might be characteristic of COVID-19 transmission. That paper is about six months old now, and it offered some speculations based on limited information. There's probably more known now, but this might suggest avenues for further investigation.
If you're interested, you might also consider asking general questions about virus transmission on the Biology Stackexchange site.
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 would like to analyse the electrical energy consumed by a furnace in a conveyor system. Is there a method or specific function to do this using Anylogic?
I've found little material surrounding this, so pointing in the right direction would be great.
Simple answer: There is no build-in functionality. But you can easily do this yourself.
If you want very high accuracy, you should check system dynamics (but this slows your model quite a bit).
If you want a simple approach, I can think of this:
create a variable elecConsumptionPerMin in your furnace agent
create an event consumeElec in your furnace that cyclically (every minute) adds to the total furnace consumption (another variable)
on model startup, ideally set myFurnace.consumeElec.suspend()
when the furnace starts producing, call ´myFurnace.consumeElec.resume()` to start consuming enegery
Obviously, you can refine this to the nth degree and you might also want to experiment with state charts. But this is the simple approach
I am taking part in a programming competition where the objective is writing a bot that can play a specific game.
The objective of the game is to earn a certain amount of points. You control multiple airships, that you move around, capture islands and navigate drones that carry treasure. You play against one opponent, turns happen simultaneously, and there is a time limit. You can move multiple ships and drones in one turn. You can program your bot in Python, Java or C#.
The exact details don‘t matter, just that each ship has around 15 options each turn (moving and shooting) and overall you have around 10000 different options for each turn (different configurations of airship movements and shooting)
Up until now I approached this competition naively, and haven‘t done anything exceptionally clever (for example, if near enemy, shoot). I have read about minimax algorithms, and I would really like to apply it here (or something similar), you can assume that I can tell the value of a state. My problem is the mass of options for each turn - which create an enourmous branching factor that doesnt let me get very deep.
Question 1: Is there a better, applicable approach to this problem? Perhaps deep-learning or something similar?
Question 2: Is there a way to minimize the branching factor? I`ve read about alpha-beta and similar algorithms, but nothing seems to do the job.
Any help would be much appreciated
The minimax algorithm seems to be natural for these kinds of problems. At first, the game will be modelled in a abstract way and then a solver is used to find the path from current situation to a gamestate which maximize the amount of points. A similar approach to minimax is GOAP, which was implemented in the 1970'er for Shakey the robot under the name STRIPS. But, GOAP and minimax has two problems: first, a abstract model of the game is needed (perhaps in PDDL or in Game Description Language) and second the state-space is to big.
An better alternative to planning is to use a Behavior Tree. Thats a static program which describes the behavior of an agent. No solver is needed and no complete modelling of the game is needed. Instead, a bottom up approach is used with multiple edit-compile-run iterations for finding the optimal behavior tree (Test-driven-development). To implement such programming approach a so called "reactive planner" has to be implemented first which is another word for a realtime scheduler. Thats a module whichs maps a behavior tree onto a gantt-chart for executing an action at a specific moment in time. As introduction, the unity3d Engine is a good starting point, which has a full behaviortree implementation out-of-the-box.
Some general Modelica advice?
We've built a model with ~2000 equations and three vectors of input from measured data. Using OpenModelica, attempts at simulation have begun to hang in the translation stage (which runs for hours where it used to take less than a minute) and now I regularly "lose connection to omc.exe." Is there perhaps something cumulative occurring that's degrading translation/compilation performance?
In general, are there any good rules of thumb for keeping simulations lighter and faster? I realize that, depending on the couplings, additional equations could be exponentially increasing the size of the resulting system of equations - could this be a problem?
Thanks for your thoughts!
It shouldn't take that long. Seems like a bug.
You can report this bug here:
https://trac.openmodelica.org/OpenModelica (New Ticket).
If your model is public you can post it there, if not you can contact the OpenModelica team privately.
I did some cleaning in the code; and got the part that repeats 12x (the module) down to ~180 equations; in the process I reduced the size of my input vectors (and also a 2D look-up table the module refers to) by quite a bit - they're both down to a few hundred values. It's working now--simulations run in reasonable time, a few minutes each.
Since all these tables were defined within Modelica functions (as you pointed out, Mr. Tiller) perhaps shrinking them helped to improve the performance. I had assumed that all that data just got spread out in a memory array, without going through any real processing, but maybe that's not the case...time to know more about what's going on under the hood in this environment (as always).
Thanks for the help!