加工时间为连续随机变量的Job Shop调度问题

针对实际调度问题中存在的不确定现象,提出了加工时间服从正态分布、最大完成时间的期望值作为目标函数的随机Job Shop问题;然后提出了解决该问题的智能优化算法:采用随机模拟的方式产生输入输出数据,利用遗传算法训练神经网络,将训练过的神经网络嵌入到另一遗传算法中,用该遗传算法来优化Job Shop调度问题;最后给出了仿真实验,通过仿真实验证明,该算法对于解决加工时间为随机变量的Job Shop调度问题是行之有效的。

Job Shop scheduling problem with continuous stochastic processing time

Due to the uncertainty in the practical scheduling problem,a stochastic Job Shop scheduling problem is proposed,in which the processing time is in normal distribution and the objective function is the expectation of the make span;Secondly,a intelligent optimization algorithm applied to this problem is promoted,it concludes three steps: obtaining the datum needed in the neural network through stochastic simulation,training the neural network through genetic algorithm,inserting the neural network into another genetic algorithm and applying the second genetic algorithm to Job Shop scheduling problem;Lastly,a example is given and it can prove that the intelligent optimization algorithm is effective.