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Maintenance Scheduling (Java, Quarkus, Maven)

Schedule maintenance jobs to crews over time to reduce both premature and overdue maintenance.

quarkus maintenance scheduling screenshot

Run the application

  1. Git clone the timefold-quickstarts repo and navigate to this directory:

    $ git clone https://github.com/TimefoldAI/timefold-quickstarts.git
...
$ cd timefold-quickstarts/use-cases/maintenance-scheduling

  2. Start the application with Maven:

    $ mvn quarkus:dev

  3. Visit http://localhost:8080 in your browser.

  4. Click on the Solve button.

Then try live coding:

  1. Make some changes in the source code.

  2. Refresh your browser (F5).

Notice that those changes are immediately in effect.

Run the packaged application

When you’re done iterating in quarkus:dev mode, package the application to run as a conventional jar file.

  1. Compile it with Maven:

    $ mvn package

  2. Run it:

    $ java -jar ./target/quarkus-app/quarkus-run.jar
    Note

    To run it on port 8081 instead, add -Dquarkus.http.port=8081.

  3. Visit http://localhost:8080 in your browser.

  4. Click on the Solve button.

Run the application in a container

  1. Build a container image:

    $ mvn package -Dcontainer

    The container image name

  2. Run a container:

    $ docker run -p 8080:8080 timefold-solver-quarkus-maintenance-scheduling-quickstart:1.0-SNAPSHOT

Run it native

To increase startup performance for serverless deployments, build the application as a native executable:

  1. Install GraalVM and gu install the native-image tool

  2. Compile it natively. This takes a few minutes:

    $ mvn package -Dnative -DskipTests

  3. Run a database. By default, application.properties is configured for H2:

    1. Download the H2 database (Platform-independent zip) and unzip it.

    2. Start the H2 server with the option -ifNotExists (not recommended in production):

      $ cd h2/bin
$ java -cp h2*.jar org.h2.tools.Server -ifNotExists

  4. Run the native executable:

    $ ./target/*-runner

  5. Visit http://localhost:8080 in your browser.

  6. Click on the Solve button.

More information

Visit timefold.ai.