In the opta planner configuration ,there is a provision to specify the termination time out.
Is there a better way to handle the termination time out strategy? For example , my problem size is small and I have set the termination time out as 10 sec.
But I can see from the logs that the best score is obtained well within 2 - 3 seconds. Is there any means to exit once the best score is reached ?
Or should the program always run till the timeout is reached and then output the best score.
Take a look at the Termination chapter in the OptaPlanner documentation.
What you are referring to is called BestScoreTermination but it might not be what you actually want -- do note that OptaPlanner has no way of knowing if the score is "the optimal score"... unless you configure Exhaustive Search (which doesn't scale well).
Therefore, if you misjudge your problem and set the BestScoreTermination to something "better" than the optimal value, OptaPlanner will run until it tries out all combinations (which might take effectively forever on big problems). If you're looking for a compromise, take a look at "termination composition"
Related
When multiple developers are continually applying changes to databases
within an organization, there may be no structured change log. Then
it's difficult to find out what change caused the database performance
to slow down. In a call center, where results must be instantaneous
for customers waiting on the phone, an adverse change can be
detrimental to the business. The DEA tests a change that might have an
impact, and compares it to the unchanged database. You apply the A/B
testing method to analyze one change at a time, helping you to clearly
know its effect. The change might mean an improvement to performance,
degradation, or maybe no performance difference, which is equally
acceptable.
I am looking for an example to understand the following statement from the above quote: The DEA tests a change that might have an impact, and compares it to the unchanged database. You apply the A/B testing method to analyze one change at a time, helping you to clearly know its effect.
For example - suppose a setting is currently having the value as 0 and I want to set it to 1. How will the above A/B testing help me exactly?
You got two environments and the only difference between them is the setting you specified. And then you split the traffic to the DB into two (50% vs. 50% for example), and part of them goes to the first enviroment and part of them goes to another. You monitor the metrics (performance or something else) and if the difference is statistically significant, you would have high confidence that setting is the change that moves the metric.
I want to have a function like if the calculation time get too long, we abort routing calculation and submit the best solution at the point of time. Is there such a function in optaplanner ?
For example in a GUI application, you would start solving on a background (worker) thread. In this scenario you can stop solver asynchronously by calling solver.terminateEarly() from another thread, typically the UI thread when you click a stop button.
If this is not what you're looking for, read on.
Provided that by calculation you actually mean the time spent solving, you have several options how to stop solver. Besides asynchronous termination described in the first paragraph, you can use synchronous termination:
Use time spent termination if you know how much time you want dedicate to solving beforehand.
Use unimproved time spent termination if you want to stop solving if the solution doesn't improve for a specified amount of time.
Use best score termination if you want to stop solving after a certain score has been reached.
Synchronous termination is defined before starting the solver and it's done either by XML solver configuration or using the SolverConfig API. See OptaPlanner documentation for other termination conditions.
Lastly, in case you're talking about score calculation and it takes too long to calculate score for a single move (solution change) then you're most certainly doing something wrong. For OptaPlanner to be able to search the solution space effectively, the score calculation must be fast (at least 1000 calculations per second).
For example in vehicle routing problem, driving time or road distances must be known at the time when you start solving. You shouldn't slow down score calculation with a heavy computation that can be done beforehand.
I am trying to solve a problem similar to employee rostering. The problem I am facing is every time I run the solver, it generates a different assignment. This makes it harder to debug why a particular case was picked over another. Why is this the case?
P.S. My assignment has many hard constraint and all of them may not be satisfied (most cases I still see some negative hard score). So my termination strategy is based on unimprovedSecondsSpentLimit. Could this be the reason?
Yes, it's likely the termination. OptaPlanner's default environmentMode guarantees the exact same solution at the exact same step (*). But CPU cycles differ a lot from run to run, so that means you get more or less steps per run. Use DEBUG logging to see that.
Use stepCountLimit or unimprovedStepCountLimit termination.
(*) Unless specified otherwise in the docs. Simulated Annealing for example will be different even in the exact same step if used with time bound terminations.
I built an application which implements a similar function as task assignment. I thought it works well until recently I noticed the solutions are not optimal. In details, there is a score table for each possible pair between machines and tasks, and usually the number of machines is much less than the number of tasks. I used hard/medium/soft rules, where the soft rule is incremental based on the score of each assignment from the score table.
However, when I reviewed the results after 1-2 hours run, I found out of the unassigned tasks there are many better choices (would achieve higher soft score if assigned) than current assignments. The benchmark reports indicate that the total soft score reached plateau within a hour and then stuck at that score level.
I checked the logic of rules - if the soft rule working perfectly, it should eventually find a way of allocation which achieves the highest overall soft score, whereas meeting the other hard/medium rules, isn't it?
I've been trying various things such as tuning algorithm parameters, scaling the score table, etc. but none delivers the optimal solution.
One problem is that you might be facing a score trap (see docs). In that case, make your constraint score more fine grained to deal with that.
If that's not the case, and you're stuck in a local optima, then I wouldn't play too much with the algorithm parameters - they will probably fix it, but you'll be overfitting on that dataset.
Instead, figure out the smallest possible move that gets you of that local optima and a step closer to the global optimum. Add that kind of moves as a custom move. For example if a normal swap move can't help, but you see a way of getting there by doing a 3-swap move, then implement that move.
I am trying to use real time planning using a custom SolverThread implementing SolverEventListener and the daemon mode.
I am not interested in inserting or deleting entities. I am just interested in "updating" them, for example, changing the "priority" for a particular entity in my PlanningEntityCollectionProperty collection.
At the moment, I am using:
scoreDirector.beforeProblemPropertyChanged(entity);
entity.setPriority(newPriority);
scoreDirector.afterProblemPropertyChanged(entity);
It seems that the solver is executed and it manages to improve the actual solution, but it only spends a few ms on it:
org.optaplanner.core.impl.solver.DefaultSolver: Real-time problem fact changes done: step total (1), new best score (0hard/-100medium/-15soft).
org.optaplanner.core.impl.solver.DefaultSolver: Solving restarted: time spent (152), best score (0hard/-100medium/-15soft), environment mode (REPRODUCIBLE),
Therefore, the solver stops really soon, considering that my solver has a 10 seconds UnimprovedSecondsSpentLimit. So, the first time the solver is executed, it stops after 10 seconds, but the following times, it stops after a few ms and doesn't manage to get a good solution.
I am not sure I need to use "beforeProblemPropertyChanged" when the planning entity changes, but I can't find any alternative because "beforeVariableChanged" is used when the planning variable changes, right? Maybe optaplanner just doesn't support updates in the entities and I need to delete the old one using beforeEntityRemoved and inserted it again using beforeEntityAdded?
I was using BRANCH_AND_BOUND, however, I have changed to local search TABU_SEARCH and it seems that the scheduler uses 10 seconds now. However, it seems stuck in a local optima because it doesn't manage to improve the solution, even with a really small collection (10 entities).
Anyone with experience with real time planning?
Thanks
The "Solving restarted" always follows very shortly after "Real-time problem fact changes done", because real-time problem facts effectively "stop & restart" the solver. The 10 sec unimproved termination only starts counting again after the restart.
DEBUG logging (or even TRACE) will show you what's really going on.