基于多种群遗传算法的路径柔性车间调度问题

Shop Scheduling Problem with Flexible Path Based on Multiple Population Genetic Algorithm

以路径柔性车间调度问题为研究对象,提出一种多种群遗传算法(MPGA)。针对路径柔性车间调度问题的特点,给出了相应的解决问题的遗传算法具体实现过程。该算法采用整数编码方式进行编码、并设计合理的自适应交叉算子和变异算子,保证个体的可行性和有效性;采用移民算子进行多种群间的协同优化,从而改善早熟收敛问题并提高优化效率。通过与文献实例仿真比较,结果表明该算法的可行性,实际调度的应用证明了该算法的有效性和实际应用价值。

The path flexible job shop scheduling problem as the research object,a multi-population genetic algorithm （MPGA） is proposed.For the characteristics of the job shop scheduling problem with flexible path,the implementation process of genetic algorithms to solve the problem is presented.The algorithm uses the integer coding method and designs rational adaptive crossover operator and mutation operator,to ensure individual feasibility and validity,using immigrant operator to collaborative optimization between populations,so as to improve premature convergence and optimize efficiency.Comparison with the literature examples of simulation shows that the algorithm is feasible and practical scheduling application verifies the effectiveness of the algorithm.