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Drools Planner optimizes automated planning by combining metaheuristic search algorithms with rule engine powered score calculation. This is the drools-planner-examples module which contains examples on how to use Drools Planner.

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<?xml version="1.0" encoding="UTF-8"?>
<solver>
  <!--<environmentMode>DEBUG</environmentMode>-->
  <solutionClass>org.drools.planner.examples.manners2009.domain.Manners2009</solutionClass>
  <planningEntityClass>org.drools.planner.examples.manners2009.domain.SeatDesignation</planningEntityClass>
  <scoreDrl>/org/drools/planner/examples/manners2009/solver/manners2009ScoreRules.drl</scoreDrl>
  <scoreDefinition>
    <scoreDefinitionType>SIMPLE</scoreDefinitionType>
  </scoreDefinition>
  <termination>
    <!--<terminationCompositionStyle>OR</terminationCompositionStyle>-->
    <!--<maximumMinutesSpend>1</maximumMinutesSpend>-->
    <scoreAttained>0</scoreAttained>
  </termination>
  <customSolverPhase>
    <customSolverPhaseCommandClass>org.drools.planner.examples.manners2009.solver.solution.initializer.Manners2009StartingSolutionInitializer</customSolverPhaseCommandClass>
  </customSolverPhase>
  <localSearch>
    <selector>
      <moveFactoryClass>org.drools.planner.examples.manners2009.solver.move.factory.SeatDesignationSwitchMoveFactory</moveFactoryClass>
    </selector>
    <acceptor>
      <completeSolutionTabuSize>1000</completeSolutionTabuSize>
    </acceptor>
    <forager>
      <pickEarlyType>NEVER</pickEarlyType>
      <!-- Real world problems require to use of <minimalAcceptedSelection> -->
    </forager>
  </localSearch>
</solver>