请使用Jmetal解决一下调度车间问题,给出代码

调度车间问题是经典的组合优化问题，可以使用JMetal框架求解。下面是使用JMetal求解调度车间问题的示例代码：

import org.uma.jmetal.algorithm.singleobjective.geneticalgorithm.GeneticAlgorithm;
import org.uma.jmetal.algorithm.singleobjective.geneticalgorithm.SteadyStateGeneticAlgorithm;
import org.uma.jmetal.operator.CrossoverOperator;
import org.uma.jmetal.operator.MutationOperator;
import org.uma.jmetal.operator.impl.crossover.PMXCrossover;
import org.uma.jmetal.operator.impl.mutation.SwapMutation;
import org.uma.jmetal.problem.Problem;
import org.uma.jmetal.problem.singleobjective.SchedulingProblem;
import org.uma.jmetal.solution.PermutationSolution;
import org.uma.jmetal.util.AlgorithmRunner;
import org.uma.jmetal.util.JMetalException;
import org.uma.jmetal.util.evaluator.impl.SequentialSolutionListEvaluator;
import org.uma.jmetal.util.factory.AlgorithmFactory;
import org.uma.jmetal.util.factory.CrossoverOperatorFactory;
import org.uma.jmetal.util.factory.MutationOperatorFactory;
import org.uma.jmetal.util.factory.ProblemFactory;

import java.util.List;

public class SchedulingProblemExample {

    public static void main(String[] args) throws JMetalException {
        Problem<PermutationSolution<Integer>> problem;
        GeneticAlgorithm<PermutationSolution<Integer>> algorithm;
        CrossoverOperator<PermutationSolution<Integer>> crossover;
        MutationOperator<PermutationSolution<Integer>> mutation;

        problem = ProblemFactory.createProblem("SchedulingProblem");
        crossover = CrossoverOperatorFactory
                .<PermutationSolution<Integer>>getOperator("PMXCrossover");
        mutation = MutationOperatorFactory.<PermutationSolution<Integer>>getOperator("SwapMutation");

        algorithm = new SteadyStateGeneticAlgorithm<>(problem, 100, 20, crossover, mutation,
                new SequentialSolutionListEvaluator<>());

        AlgorithmRunner algorithmRunner = new AlgorithmRunner.Executor(algorithm)
                .execute();

        List<PermutationSolution<Integer>> population = algorithm.getResult();
        long computingTime = algorithmRunner.getComputingTime();

        System.out.println("Total execution time: " + computingTime + "ms");

        PermutationSolution<Integer> solution = population.get(0);
        int[] schedule = ((SchedulingProblem) problem).getSchedule(solution);

        System.out.println("Optimal Schedule: ");
        for (int i = 0; i < schedule.length; i++) {
            System.out.println("Job " + i + " starts at " + schedule[i]);
        }
    }
}

在这个示例代码中，我们使用JMetal的`ProblemFactory`创建一个调度车间问题，然后使用`CrossoverOperatorFactory`和`MutationOperatorFactory`创建交叉和变异算子。我们使用`SteadyStateGeneticAlgorithm`作为优化算法，并使用`SequentialSolutionListEvaluator`对种群进行评估。

最后，我们使用`AlgorithmRunner`执行算法，并输出最优解对应的调度方案。