论文部分内容阅读
柔性作业车间在实际生产过程中存在多种不确定因素,影响了正常生产和调度目标的实现。针对机器发生故障的因素,结合柔性作业车间调度特点,分别提出了2个鲁棒性指标:一个是考虑初始调度和实际调度最大完工时间偏差的鲁棒性指标;另一个是考虑每台机器的空闲时间和工作负荷的鲁棒性指标。应用遗传算法对其求解,设计了两段式染色体编码,防止产生非法解。通过建立机器发生故障的概率函数,模拟产生机器故障事件,并利用提出的优化方法和鲁棒性指标进行仿真优化。实验结果表明提出的方法可以有效的减少工序延误,避免造成实际调度性能的恶化。
Flexible workshop in the actual production process there are many uncertainties, affecting the normal production and scheduling goals. According to the factors of machine failure and the characteristics of flexible job shop scheduling, two robustness indexes are proposed respectively: one is the robustness index considering the initial schedule and the maximum schedule deviation of the actual schedule; the other is considering each machine’s Idle time and workload robustness indicators. Applying genetic algorithm to solve it, we designed a two-stage chromosome coding to prevent illegal solution. By establishing the probability function of the machine failure, the machine fault events are simulated and simulated and optimized by using the proposed optimization method and robustness index. The experimental results show that the proposed method can effectively reduce the process delay and avoid the deterioration of the actual scheduling performance.