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对多目标柔性作业车间调度问题进行研究,分别以生产车间中的最大完工时间、机器总负载、加工生产成本和总拖期时间为性能指标,建立多目标柔性作业车间调度模型。针对多目标柔性作业车间调度问题的自身特点,设计一种扩展的基于工序编码及自动调度解码机制。考虑粒子最大、最小收敛速度及相应边界条件,设计一种应用于解决柔性生产调度的多目标粒子群算法。利用该算法求解柔性作业车间调度问题得到一组Pareto解集。通过基准实验测试与实际生产实例,验证该算法的可行性与有效性。
The multi-objective flexible job shop scheduling problem is studied. The multi-objective flexible job shop scheduling model is established based on the maximum completion time, the total machine load, the production cost and the total tardiness time in the workshop. Aiming at the characteristics of multi-objective flexible job shop scheduling problem, an extended process coding and automatic scheduling decoding mechanism is designed. Considering the maximum and minimum convergence speed of particles and the corresponding boundary conditions, a multi-objective particle swarm optimization algorithm is proposed to solve the problem of flexible production scheduling. This algorithm is used to solve the flexible job shop scheduling problem to get a set of Pareto solution set. Through the benchmark test and actual production examples, the feasibility and effectiveness of the algorithm are verified.