Is there a way to create an initial solution for OptaPlanner by hand, in Java code?
I know that I can write the constructionHeuristic in the config XML to create a good initial solution.
But if I can write a better initial solution than OptaPlanner founds, is there a way to start the planning from my solution?
Thanks,
Vilmos
There are several ways of giving OptaPlanner a custom solution:
You can generate any Solution you like ahead of time. If the solution you give to solver.solve(solution) is already initialized at the time, you do not need to call construction heuristics at all.
If you instead wish to take an uninitialized solution and initialize it yourself in OptaPlanner, look into CustomPhaseCommand. Several OptaPlanner examples use that technique, see Machine Reassignment as an example. (However, beware that this is not a public API and we therefore do not guarantee its long-term stability.)
Related
I am using PySCIPOpt and have a MIP with some quadratic constraints (works). Now, I want to implement a Primal Heuristic (should run once before presolving), that fixes certain Variables and optimizes afterwards.
Ìn pseudo-code something like:
For x in ToFIX:
model.fixVar(x, my_guess(x))
model.optimize()
*Any found solution is used as solution of the original problem*
For x in ToFIX:
model.unFixVar(x)
I worked around that problem by creating a second model, solving that, identifying the variables by their name and using model.trySol().
This mostly works but is slow and certainly not the way it is meant to be implemented.
Any hint, which functionalities to use is appreciated.
sorry this took a while to answer.
What you want to implement is a sub-scip heuristic. This is certainly possible, but if you want to do it in PySCIPOpt you might have to wrap some missing methods from the C-API.
I suggest you take a look at heur_rens.c in the SCIP code. The methods you would need to wrap are probably SCIPcopyLargeNeighborhoodSearch and SCIPtranslateSubSol which should save you a lot of trouble. Please refer to the section extending the interface in the PySCIPopt Readme.
I'm using Optaplanner to make a schedule for a school. Every works good, except that it sometimes have gaps between the lessons, which I do not want. I have rules for punishing this but I think the search space is so big that it will take quite a while before it "fixes" this.
Is it possible to tell Optaplanner to try out some "selected/calculated" moves first and then continue with the moves it is supposed to do?
I'm using the time grain patterns with this.
It is possible to do, but not recommended. There is good literature on the subject here: https://www.optaplanner.org/docs/optaplanner/latest/move-and-neighborhood-selection/move-and-neighborhood-selection.html
I would start first by experimenting with configuring the generic moves that are provided out-of-the-box in optaplanner, which you can do by editing the XML file included with the distribution.
If a custom move is really what you need, you can refer to the docs above, but it is much harder to avoid bugs this way and you will want to enable full assert to double-check that score corruption is not occurring after implementation.
I am evaluating OptaPlanner for a planning problem I have. I have seen several responses to this topic, but nothing quite like I am looking for.
I am looking for the capability to extend the problem on the fly; that is, as the planner is solving a problem.
For example, in the CloudComputing example, I would like to be able to add computers on the fly (to a point) while the problem is being solved. The easiest case is that the problem is initially over-constrained and to resolve this I would like to be able to add computers, and then replan.
Or, I would like to be able to add a lecture, or a lecturer in one of the scheduling problems, etc.
It seems like the OptaPlanner requires a static number of entities / variables at solve time.
Any pointers would be appreciated.
Take a look at the Real-time planning section of the OptaPlanner User Guide.
You could also look at the Travelling Salesman Problem example in optaplanner-examples. Specifically, look at the org.optaplanner.examples.tsp.swingui.TspPanel class and traverse down from there. It's a pretty standard implementation of real-time planning AFAIK. I can also recommend to run the TSP example first to "see" how it works.
I am new to this thing and want to use Opta Planner in my project to Solve Vehicle Routing Problem. Can anyone guide me step by step how to use it, it would be great.
Thanks
In the current version (7.7) the docs have a quick start that explains the concepts on another use case and then see the VRP implementation example.
For a future version, we intend to make implementing/modeling VRP even simpler by supporting a Vehicle.visitList style model.
Lately, I've been making use a lot of the Strategy Pattern along with the Factory Pattern. And I really mean a lot. I have a lot of "algorithms" for everything and factories that retrieve algorithms based on parameters.
Even though the code seems very extensible, and it is, having N factories seems a bit of an abuse.
I know this is pretty subjective, and we're talking without seeing code, but is this acceptable in real world code? Would you change something ?
OK- ask yourself a question. does/will this algorithm implementation ever change? If no then remove the strategy.
I am maintence.
I once was forced (by my pattern lovin' boss) to write a set of 16 "buffer interpreter tuxedo services" using an AbstractFactory and a double DAO pattern in C++ (no reflections, no code gen). All up it something like 20,000 lines of the nastiest code I've even seen (not the least because I didn't really know C++ when I started) and it took about three months.
Since my old boss has moved on I've rewritten them using good 'ole "straight up and down" procedural style C++, with couple of funky-macros... each service is like 60 lines of code, times 16... all up less than a 1000 lines of really SIMPLE code; so simple that even I can follow it.
Cheers. Keith.
Whenever I'm implementing code in this fashion, some questions I ask are:
what components do I need to substitute to test ?
what components will I expect users/admins to disable or substitute (e.g. via Spring configs or similar) ?
what components do I expect or suspect will not be required in the future due to (possibly) changing requirements ?
This all drives how I construct object or components (via factories) and how I implement algorithms. The above is vague, but (of course) the requirements can be similarly difficult to pin down. Without seeing your implementation and your requirements, I can't comment further, but the above should act as some guideline to help you determine whether what you've done is overkill.
If you're using the same design pattern all over the place, perhaps you should either switch to a language that has better support for what you're trying to do or rethink your code to be more idiomatic in your language of choice. After all, that's why we have more than one programming language.
Would depends on the kind of software I'm working on.
Maintenance asks for simple code and factories is NOT simple code.
But extensibility asks sometimes for factories...
So you have to take both in consideration.
Just have in mind that most of the time, you will have to maintain a source file MANY times a year and you will NOT have to extend it.
IMO, patterns should only be used when absolutely needed. If you think it can be handy in two years, you are better to use them... in two years.
How complex is the work the factory is handling? Does object creation really need to be abstracted to a different class? A variation of the factory method is having a simple, in-class factory. This really works best if any dependencies have already been injected.
For instance,
public class Customer
{
public Customer CreateNewCustomer()
{
// handle minimally complex create logic here
}
}
As far as Strategy overuse... Again, as #RichardOD explained, will the algorithm ever really change?
Always keep in mind the YAGNI principle. You Aren't Gonna Need It.
Can't you make an AbstractFactory instead off different standalone factories?
AlgorithmFactory creates the algorithms you need based on the concrete factory.