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中文摘要：
为了在不确定的车间信息环境下做出正确的调度策略，提出了一种支持多目标和多优先级车间调度策略的随机规划模型，并给出了求解算法。该模型的求解通过包含3个步骤的混合智能算法来实现，首先利用随机仿真生成近似的样本数据，然后利用神经网络进行不确定目标和约束函数的逼近，并用遗传算法最终完成对多目标优化解的搜索。最后，通过一个汽车企业模具制造车间中调度问题的实例，验证了该模型和算法的有效性及实用性。
英文摘要：
To make the correct executive decision under uncertain job--shop information environment, a stochastic programming model supporting multi objective and multi priority for job-- shop scheduling was proposed and a hybrid intelligent algorithm consisting of three steps was designed to solve this problem. Firstly, some approximate data samples were obtained by stochastic simulation. Secondly, a neural network model was constructed to approach the uncertain function of objectives and constraints. Then the genetic algorithm was used to search for the optimal solution, Finally, a case study of a scheduling problem in a job--shop for die manufacturing in a motor company was used to illustrate the feasibility and practicability of this model and algorithm.
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