不确定信息条件下Job-shop调度的混合智能算法

A Hybrid Intelligent Algorithm for Job-shop Scheduling under Uncertain Information Environment

目前大多数生产调度的研究往往聚焦于经典调度问题的优化算法而忽略了车间中大量存在的不确定性,因而难以应用于实际车间调度。采用随机变量来描述真实车间中存在的一些不确定信息,在基于不确定规划理论的基础上建立了相应的不确定性调度模型,并研究了解决此类问题的混合智能算法。开发了混合智能优化原型系统,并结合仿真工具对该调度模型和混合智能算法进行了验证。

Most present researches on production scheduling concentrate on the optimization algorithms for classical scheduling problems without considering the uncertainties typically existing in the actual job shops, which leads to the difficulty to apply the algorithms in practical shop scheduling. Here, the stochastic variables were used to describe the uncertain information existing in the actual job shops. The corresponding model for scheduling with uncertainty was built on the basis of uncertain programming theory. A hybrid intelligent algorithm for this kind of problem was proposed, and a prototype system of hybrid intelligent optimization was developed. Simulation study was carried out for verifying the proposed scheduling model as well as the hybrid intelligent algorithm.