OptaPlanner | OptaPlanner applications | Computer Vision library

 by   sunil-samuel Java Version: Current License: No License

kandi X-RAY | OptaPlanner Summary

kandi X-RAY | OptaPlanner Summary

OptaPlanner is a Java library typically used in Artificial Intelligence, Computer Vision, OpenCV applications. OptaPlanner has no vulnerabilities and it has low support. However OptaPlanner has 2 bugs and it build file is not available. You can download it from GitHub.

OptaPlanner applications optical-optimal -. Optical Optimal is a GUI based application that provides the functionality to backup directories onto several discs or USB flash drives. The objective is to store as many directories as possible into a single discs or USB flash drive with the following criteria. See the Readme file for the optical-optimal project for more detail.
Support
    Quality
      Security
        License
          Reuse

            kandi-support Support

              OptaPlanner has a low active ecosystem.
              It has 4 star(s) with 1 fork(s). There are no watchers for this library.
              OutlinedDot
              It had no major release in the last 6 months.
              OptaPlanner has no issues reported. There are no pull requests.
              It has a neutral sentiment in the developer community.
              The latest version of OptaPlanner is current.

            kandi-Quality Quality

              OutlinedDot
              OptaPlanner has 2 bugs (1 blocker, 0 critical, 1 major, 0 minor) and 39 code smells.

            kandi-Security Security

              OptaPlanner has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              OptaPlanner code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              OptaPlanner does not have a standard license declared.
              Check the repository for any license declaration and review the terms closely.
              OutlinedDot
              Without a license, all rights are reserved, and you cannot use the library in your applications.

            kandi-Reuse Reuse

              OptaPlanner releases are not available. You will need to build from source code and install.
              OptaPlanner has no build file. You will be need to create the build yourself to build the component from source.
              OptaPlanner saves you 468 person hours of effort in developing the same functionality from scratch.
              It has 1103 lines of code, 91 functions and 15 files.
              It has medium code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed OptaPlanner and discovered the below as its top functions. This is intended to give you an instant insight into OptaPlanner implemented functionality, and help decide if they suit your requirements.
            • Select a root folder
            • Create top level label
            • Create table view
            • Display the final page
            • Returns the hashCode of this file
            • Adds the hashCode to the hashCode
            • Processes the user input
            • Starts the FXML process
            • Compare this folder to another folder
            • Returns a hashCode of the parameters
            • Handler for the user input
            • Change the slider into the slider slider
            • Compares this object s objective solution
            • Opens the dialog
            • Compares this object to another
            • Explain score of the solver
            • Returns a hashCode of the name
            Get all kandi verified functions for this library.

            OptaPlanner Key Features

            No Key Features are available at this moment for OptaPlanner.

            OptaPlanner Examples and Code Snippets

            No Code Snippets are available at this moment for OptaPlanner.

            Community Discussions

            QUESTION

            Can OptaPlanner solve multiple problems concurrently
            Asked 2021-Jun-11 at 05:50

            When OptaPlanner is used in web service, which means an OptaPlanner app is required to solve multiple problems in parallel threads, are there any limitations to prevent OptaPlanner from doing this? Is synchronization required in any OptaPlanner functions? Thanks

            ...

            ANSWER

            Answered 2021-Jun-11 at 05:50

            OptaPlanner supports this: it's a common use case.

            In a single JVM, look at SolverManager to solve mulitple datasets of the same use case in parallel. Even if the constraint weights differ per dataset (see ConstraintConfiguration). So even if some datasets disable/enable some of the constraints while others don't.

            For different use cases in a single JVM, just create multiple SolverFactory or SolverManager instances. This is uncommon because usually each use case is a different app (= microservice?).

            Across multiple JVMs (= pods), there are several good techniques. Our activemq quickstart scales beautifully horizontally. Read Radovan's blog about how the ActiveMQ is used to load balance the work across the solver pods.

            Source https://stackoverflow.com/questions/67929652

            QUESTION

            Why does Optaplanner not allow configuring the decay rate in simulated annealing?
            Asked 2021-Jun-11 at 05:44

            I want to use Simulated Annealing in OptaPlanner, but I am a little baffled by the fact that there is only a setting for the initial temperature and not one for the decay rate. What is the reason for this choice?

            ...

            ANSWER

            Answered 2021-Jun-11 at 05:44

            The cooldown rate is automatically derived from the timeGradient, which is simply put 0.0 at the start, 0.5 at half the spentTime and 1.0 at all of the spentTime.

            But yes, the classic Simulated Annealing method has 2 parameters (starting temperature and cooldown rate). One could implement such an SA pretty easily by copy-pasting the SimulatedAnnealingAcceptor and configuring it in the AcceptorConfig.

            That being said, tuning 2 parameters is a pain for users. That's why OptaPlanner default SA only has 1 parameter that - together with the termination - is translated into the 2 parameters that SA needs.

            Source https://stackoverflow.com/questions/67929834

            QUESTION

            How to initialize correctly the ConstraintVerifier for testing Optaplanner ConstraintStreams in Kotlin
            Asked 2021-Jun-07 at 13:18

            How can I initialize a ConstraintVerifier in Kotlin without using Drools and Quarkus? I already added the optaplanner-test JAR and the Maven Dependency for Optaplanner 8.6.0.Final and tried it the following way:

            ...

            ANSWER

            Answered 2021-Jun-07 at 13:18

            There are several issues with your test. First of all:

            Source https://stackoverflow.com/questions/67871478

            QUESTION

            OptaPlanner: Is the "constraint match" associated with a score just a semantical thing?
            Asked 2021-Jun-03 at 09:20

            I have a question about OptaPlanner constraint stream API. Are the constraint matches only used to calculate the total score and are meant to help the user see how the score results, or is this information used to find a better solution?

            With "used to find a better solution" I mean the information is used to get the next move(s) in the local search phase.

            So does it matter which planning entity I penalize?

            Currently, I am working on an examination scheduler. One requirement is to distribute the exams of a single student optimally. The number of exams per student varies. Therefore, I wrote a cost function that gives a normalized value, indicating how well the student's exams are distributed.

            Let's say the examination schedule in the picture has costs of 80. Now, I need to break down this value to the individual exams. There are two different ways to do this:

            • Option A: Penalize each of the exams with 10 (10*8 = 80).
            • Option B: Penalize each exam according to its actual impact.=> Only the exams in the last week are penalized as the distribution of exams in week one and week two is fine.

            Obviously, option B is semantically correct. But does the choice of the option affect the solving process?

            ...

            ANSWER

            Answered 2021-Jun-03 at 08:40

            The constraint matches are there to help explain the score to humans. They do not, in any way, affect how the solver moves or what solution you are going to get. In fact, ScoreManager has the capability to calculate constraint matches after the solver has already finished, or for a solution that's never even been through the solver before.

            (Note: constraint matching does affect performance, though. They slow everything down, due to all the object iteration and creation.)

            To your second question: Yes, it does matter which entity you penalize. In fact, you want to penalize every entity that breaks your constraints. Ideally it should be penalized more, if it breaks the constraints more than some other entity - this way, you get to avoid score traps.

            EDIT based on an edit to the question:

            In this case, since you want to achieve fairness per student, I suggest your constraint does not penalize the exam, but rather the student. Per student, group your exams and apply some fairness ConstraintCollector. If you do it like that, you will be able to create a per-student fairness function and use its value as your penalty.

            The OptaPlanner Tennis example shows one way of doing fairness. You may also be interested in a larger fairness discussion on the OptaPlanner blog.

            Source https://stackoverflow.com/questions/67813352

            QUESTION

            Profiling constraint streams score calculation in Optaplanner
            Asked 2021-Jun-01 at 13:54

            I'm looking at profiling the score calculation in my Optaplanner project to find out if there are any hotspots that would benefit from being optimised. However, visualvm shows most of the time to be taken in the self time of org.drools.modelcompiler.constraints.ConstraintEvaluator$InnerEvaluator$_2.evaluate. I therefore assume that this method is what actually runs a lot of the constraint's code. What is the best way to find out what specific pieces of code are taking the most time?

            ...

            ANSWER

            Answered 2021-Jun-01 at 13:54

            The thing to understand about Constraint Streams is that it is not imperative programming, and therefore traditional performance optimization techniques such as code profiling are not going to be very helpful. Instead, I suggest you think of Constraint Streams as SQL - the way to have fast SQL is to think about how your data flows, how you join and what gets indexed.

            Recently I wrote a blog post explaining the tricks behind making CS run fast. However, CS is internally interpreted by the Drools engine, and therefore studying it may give you some insights too. Not all insights there are applicable to CS, but if you take a look at drools-metric, you should be able to see which constraints are comparatively slow. And then it becomes a game of tweaking.

            Source https://stackoverflow.com/questions/67788278

            QUESTION

            Why does adding a hard constraint produce bad solutions?
            Asked 2021-May-28 at 06:42

            I am trying to use Optaplanner to optimally assign production units to a group of contractors. The goal is to assign the production units so that the real inventory is as close as possible to the optimal inventory. For this, I am using a soft constraint which, in simple terms, follows this basic equation:

            This is the basis for my soft constraint score. The purpose of Optaplanner is to minimize this difference. However, I do have a hard constraint that specifies the minimum production units that can be assigned. Whenever this constraint is introduced to the problem, Optaplanner produces less than optimal solutions. It basically assigns all the available production units to one single contractor so as to not break the minimum production quantity constraint.

            This ensures that no contractor is assigned a small amount of production units (assigning 0 is ok), but it has the unintended consequence that the soft constraint is not optimized at all. Extending the run time basically just moves the entirety of the production units around between different contractors.

            It is not a matter of not having enough production units since I have around 10000 available units while the minimum production quantity is 120. This should be enough to assign it to several contractors and optimize the inventory instead of assigning the 10000 units to one single contractor.

            Is there a way to include this hard constraint without Optaplanner completely ignoring the soft constraint?

            EDIT: To clarify, the hard constraint is only punishing when the production units are bellow 120. It is not rewarding production units above 120. The max possible hard score is 0.

            (For simplicity I said "assign units to contractors" when in reality Optaplanner is "assigning contractors to the units")

            ...

            ANSWER

            Answered 2021-May-28 at 06:42

            It's probably stuck in a deep local optima due to lack of smart enough moves to get out of it (and it's too deep for the metaheuristics to get out of it). If you turn on DEBUG (and later TRACE) logging, you'll see the decisions that optaplanner makes.

            Try adding pillar change and swap moves:

            Source https://stackoverflow.com/questions/67726358

            QUESTION

            Quarkus - Optaplanner tweaking algorithms
            Asked 2021-May-25 at 09:21

            I created a new file known as "solverConfig.xml" under resources. I changed in application.properties, to write the following: quarkus.optaplanner.solver-config-xml=src/main/resources/solverConfig.xml. However, Quarkus does not recognize the classpath. It says: Invalid quarkus.optap lanner.solverConfigXML property (src/main/resources/solverConfig.xml): that classpath resource does not exist. I followed the response of Optaplanner and Quarkus solver config update. But, it does not work.

            The solverConfig.xml is configured as:

            ...

            ANSWER

            Answered 2021-May-25 at 09:21

            The src/main/resources prefix isn't part of the value for that property:

            • Either don't have a quarkus.optaplanner.solver-config-xml property in application.properties, which means it will pick up src/main/resources/solverConfig.xml (recommended, for standardization only)

            • Or set it explicitly to quarkus.optaplanner.solver-config-xml=solverConfig.xml to pick up src/main/resources/solverConfig.xml.

            PS: solverConfig.xml in Quarkus doesn't need a entityClass, solutionClass or constraintProviderClass. It picks that up automatically.

            Source https://stackoverflow.com/questions/67684355

            QUESTION

            VRP with score dependens on more than one job in optaplanner
            Asked 2021-May-24 at 12:11

            We are trying to solve a VRP with Optaplanner. The score calculation runs via constraint streams.

            Now I have two vehicles (A and B) and want to schedule two jobs (J1 and J2). The construction heuristic (FIRST_FIT_DECREASING) schedules J1 to A and J2 to B, what is correct so far.

            Now the two jobs also have an attribute "customer", and I want to assign a penalty if the customer of the two jobs is the same but the vehicles are different.

            For this purpose, I have created a constraint in the ConstraintProvider that filters all jobs via groupBy that have the same customer but different vehicles.

            If I now switch on the FULL_ASSERT_MODE, an IllegalStateException occurs after scheduling J2, because the score that is calculated incrementally is different from the score for the complete calculation. I suspect this is because the VariableListener, which recalculates the times of the jobs, only tells the ScoreDirector about a change to Job J2 for my shadowvariables and therefore only changes the score part that is related to it.

            How can I tell Optaplanner that the score for J1 must also be recalculated? I can't get to job J1 via the VariableListener to tell the ScoreDirector that the score has to be changed here.

            Or does this problem require a different approach?

            ...

            ANSWER

            Answered 2021-May-24 at 12:11

            This is a problem that is a bit hard to explain fully. TLDR version: constraint streams only react to changes to objects which are coming from either from(), join() or ifExists(). Changes on objects not coming through these statements will not be caught, and therefore causing score corruptions. Longer explanation follows.

            Consider a hypothetical Constraint Stream like this:

            Source https://stackoverflow.com/questions/67619480

            QUESTION

            OptaPlanner, build a rule that needs to get information from the solution
            Asked 2021-May-20 at 07:23

            Need some ideas on how to build a rule in my task assignment project. Assign workers to tasks, each task has a happen location, want a soft constraint to make a worker's next task be as close as possible to the fulfilled task. But in DRL how can I know which task is the worker's previous task? the information is in the Solution class. An example is greatly appreciated. Is there any OptaPlanner example that I can refer to? for me to know how to get values from Solution.

            ...

            ANSWER

            Answered 2021-May-20 at 07:23

            There is a task assigning example in the optaplanner-examples module, which shows how to model such a problem. The main idea is that every task points to the next task and to the previous task or the worker. The worker is the first element of such a chain. In this example, one of the goals is to minimize the makespan; your soft constraint about location sounds very similar - instead of penalizing for the amount of time required to complete all the tasks by a single worker, it would focus on the distance between locations associated with each task.

            Source https://stackoverflow.com/questions/67605581

            QUESTION

            OptaPlanner, why break point is not hit when debugging example
            Asked 2021-May-12 at 07:38

            New to OptaPlanner and want to debug the example Task Assigning in Eclipse to learn. Found that the breakpoints are hit only when the code are called by the UI related code, such as

            ...

            ANSWER

            Answered 2021-May-12 at 07:38

            A solution like TaskAssigningSolution is planning cloned (see docs for what this is) through reflection on the fields. See FieldAccessingSolutionCloner. You can write your own solution cloner to avoid that behavior (but that's very error prone to write correctly).

            We have a RFE running to support something like accessFieldsThroughGetterSetters=true to be more JDK 17 friendly, which will force OptaPlanner never to use field access for non-public fields.

            Source https://stackoverflow.com/questions/67496729

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network

            Vulnerabilities

            No vulnerabilities reported

            Install OptaPlanner

            You can download it from GitHub.
            You can use OptaPlanner like any standard Java library. Please include the the jar files in your classpath. You can also use any IDE and you can run and debug the OptaPlanner component as you would do with any other Java program. Best practice is to use a build tool that supports dependency management such as Maven or Gradle. For Maven installation, please refer maven.apache.org. For Gradle installation, please refer gradle.org .

            Support

            For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .
            Find more information at:

            Find, review, and download reusable Libraries, Code Snippets, Cloud APIs from over 650 million Knowledge Items

            Find more libraries
            CLONE
          • HTTPS

            https://github.com/sunil-samuel/OptaPlanner.git

          • CLI

            gh repo clone sunil-samuel/OptaPlanner

          • sshUrl

            git@github.com:sunil-samuel/OptaPlanner.git

          • Stay Updated

            Subscribe to our newsletter for trending solutions and developer bootcamps

            Agree to Sign up and Terms & Conditions

            Share this Page

            share link