Optaplanner starting from existing result - merge

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.

Related

How to find $plusargs with same string in different locations

Very general issue in large integration of verification environment.
Our verification development involves large group across different time zone.
Group has preference to use $plusargs instead factory mechanism.
Probably main reason it is hard to set factory from command line processor,
we have more layers of scripts to start simulation.
Recently i found that same string been used in different environment to control behavior of environment. In this case two different score board used same string to disable some checking and test pass. Both those environment some time created at run time. Also some time it is OK to re-use same string, and it will require owner to be involved.
Is there any way to find duplication like this from final elaborated model, and provide locations in code as a warning?
I thought create our own wrapper, but problem that we are integrating some code that we are not owners as in this case was.
Thanks,
This is a perfect example of how people think they can get things done quicker by not following the recommended UVM methodology and instead create time consuming complexity later on.
I see at least two possible options.
Write a script that searches the source code for $plusargs and hopefully they have used string literals for you to trace for duplicates.
You can override $plusargs with PLI code and have it trace duplicates.
The choice depends on wether you are better at writing Perl/Python or C code.

Access the simulation parameter in Modelica

I have a model in my Modelica and I use Dymola to compile this model. In my model I need the simulation information "Output Interval length". I have searched for it but I could not get the useful information. Is there any other possible way we could access simulation information.
If you are simply trying to get the results reported at specific intervals, you can use a sample operator to achieve that. That would force the solution to be computed at specific times without directly specifying something like the time step.
The important point to understand here is that a model where the behavior of the model depends on the numerical integration is highly suspect and I've never seen a case where the behavior couldn't be described without knowledge of the solution method. Said another way, "mother nature" doesn't know anything about "time steps". :-)
You could use a clocked system with an integrator.
For an Example, see File -->Libraries-->Modelica_Synchronous --> Examples --> Systems --> Controlled_mixing_unit in Dymola
There the period (i.e. in this case the timestep of the explicit Euler method) is a parameter of the periodic clock)
Modelica by design prohibits accessing any numerical solver internals, so you cannot access it. The output interval length also cannot be determined by the model in any reliable way since the solver will take internal steps longer than the output interval and then interpolate values for the result file.
You could create a function that reads the dsin.txt file and extracts that information.

How to implement deterministic single threaded network simulation

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.

How to setup multi-level MDO formulation with openmdao 1.x?

As an openmdao 1.x problem handles only one driver and as optimizers are still supposed to be drivers, how a multi-level formulation could be implemented? Should I use two problems? Should I call an optimizer directly within a solve_nonlinear component method? Thanks.
There have been some changes, and Problem is no longer a system. The best way to do this now is to create a Component that contains the sub-problem, tells it when to run, and passes data in and out. See example here:
How to use nested problems in OpenMDAO 1.x?
The planned way to handle this is going to be to use nested Problem instances. This is not implemented yet in the problem class, but it very easy to implement by hand.
All you would need to do is define your own solve_nonlinear method in a SubClass of Problem. if you're going to use analytic derivatives you would also need to implement a Jacobian and apply_linear methods as well and do use post-optimiality sensitivities if you had nested optimizers. Or you could force finite difference to happen in the containing parent group.
Your solve_linear will take in params, unknowns, and resids dictionaries and passes the relevant variables down into the problems vector. Essentially, the framework was designed to not know if you're using nested problems. The top level framework thinks that the inner one is just a regular component.

How to start working with a large decision table

Today I've been presented with a fun challenge and I want your input on how you would deal with this situation.
So the problem is the following (I've converted it to demo data as the real problem wouldn't make much sense without knowing the company dictionary by heart).
We have a decision table that has a minimum of 16 conditions. Because it is an impossible feat to manage all of them (2^16 possibilities) we've decided to only list the exceptions. Like this:
As an example I've only added 10 conditions but in reality there are (for now) 16. The basic idea is that we have one baseline (the default) which is valid for everyone and all the exceptions to this default.
Example:
You have a foreigner who is also a pirate.
If you go through all the exceptions one by one, and condition by condition you remove the exceptions that have at least one condition that fails. In the end you'll end up with the following two exceptions that are valid for our case. The match is on the IsPirate and the IsForeigner condition. But as you can see there are 2 results here, well 3 actually if you count the default.
Our solution
Now what we came up with on how to solve this is that in the GUI where you are adding these exceptions, there should run an algorithm which checks for such cases and force you to define the exception more specifically. This is only still a theory and hasn't been tested out but we think it could work this way.
My Question
I'm looking for alternative solutions that make the rules manageable and prevent the problem I've shown in the example.
Your problem seem to be resolution of conflicting rules. When multiple rules match your input, (your foreigner and pirate) and they end up recommending different things (your cangetjob and cangetevicted), you need a strategy for resolution of this conflict.
What you mentioned is one way of resolution -- which is to remove the conflict in the first place. However, this may not always be possible, and not always desirable because when a user adds a new rule that conflicts with a set of old rules (which he/she did not write), the user may not know how to revise it to remove the conflict.
Another possible resolution method is prioritization. Mark a priority on each rule (based on things like the user's own authority etc.), sort the matching rules according to priority, and apply in ascending sequence of priority. This usually works and is much simpler to manage (e.g. everybody knows that the top boss's rules are final!)
Prioritization may also be used to mark a certain rule as "global override". In your example, you may want to make "IsPirate" as an override rule -- which means that it overrides settings for normal people. In other words, once you're a pirate, you're treated differently. This make it very easy to design a system in which you have a bunch of normal business rules governing 90% of the cases, then a set of "exceptions" that are treated differently, automatically overriding certain things. In this case, you should also consider making "?" available in the output columns as well.
One other possible resolution method is to include attributes in each of your conditions. For example, certain conditions must have no "zeros" in order to pass (? doesn't matter). Some conditions must have at least one "one" in order to pass. In other words, mark each condition as either "AND", "OR", or "XOR". Some popular file-system security uses this model. For example, CanGetJob may be AND (you want to be stringent on rights-to-work). CanBeEvicted may be OR -- you may want to evict even a foreigner if he is also a pirate.
An enhancement on the AND/OR method is to provide a threshold that the total result must exceed before passing that condition. For example, putting CanGetJob at a threshold of 2 then it must get at least two 1's in order to return 1. This is sometimes useful on conditions that are not clearly black-and-white.
You can mix resolution methods: e.g. first prioritize, then use AND/OR to resolve rules with similar priorities.
The possibilities are limitless and really depends on what your actual needs are.
To me this problem reminds business rules engine where there is no known algorithm to define outputs from inputs (e.g. using boolean logic) but the user (typically some sort of administrator) has to define all or some the logic itself.
This might sound a bit of an overkill but OTOH this provides virtually limit-less extension capabilities: you don't have to code any new business logic, just define a new rule set.
As I understand your problem, you are looking for a nice way to visualise the editing for these rules. But this all depends on your programming language and the tool you select for this. Java, for example, has JBoss Drools. Quoting their page:
Drools Guvnor provides a (logically
centralized) repository to store you
business knowledge, and a web-based
environment that allows business users
to view and (within certain
constraints) possibly update the
business logic directly.
You could possibly use this generic tool or write your own.
Everything depends on what your actual rules will look like. Rules like 'IF has an even number of these properties THEN' would be painful to represent in this format, whereas rules like 'IF pirate and not geek THEN' are easy.
You can 'avoid the ambiguity' by stating that you'll always be taking the first actual match, in other words your rules have a priority. You'd then want to flag rules which have no effect because they are 'shadowed' by rules higher up. They're not hard to find, so it's something your program should do.
Your interface could also indicate groups of rules where rules within the group can be in any order without changing the outcomes. This will add clarity to what the rules are really saying.
If some of your outputs are relatively independent of the others, you will also get a more compact and much clearer table by allowing question marks in the output. In that design the scan for first matching rule is done once for each output. Consider for example if 'HasChildren' is the only factor relevant to 'Can Be Evicted'. With question marks in the outputs (= no effect) you could be halving the number of exception rules.
My background for this is circuit logic design, not business logic. What you're designing is similar to, but not the same as, a PLA. As long as your actual rules are close to sum of products then it can work well. If your rules aren't, for example the 'even number of these properties' rule, then the grid like presentation will break down in a combinatorial explosion of cases. Your best hope if your rules are arbitrary is to get a clearer more compact presentation with either equations or with diagrams like a circuit diagram. To be avoided, if you can.
If you are looking for a Decision Engine with a GUI, than you can try this one: http://gandalf.nebo15.com/
We just released it, it's open source and production ready.
You probably need some kind of inference engine. Think about doing it in prolog.