一种基于神经网络的生产调度方法

A NEURAL NETWORK BASED APPROACH TO PRODUCTION SCHEDULING

提出解决具有开、完工期限制的约束Ｊｏｂ－ｓｈｏｐ生产调度问题的一种神经网络方法．该方法通过约束神经网络，描述各种加工约束条件，并对不满足约束的开工时间进行相应调节，得到可行调度方案；然后由梯度搜索算法优化可行调度方案，直至得到最终优化可行调度解．理论分析、仿真实验表明了方法的有效性．

An effective neural network based approach to production scheduling is proposed in the paper,which is apt to solving complex job shop scheduling problems with available time and due date constraints.In this approach,a constrained neural network is proposed to describe various kinds of processing restrictions,and an unreasonable starting time is tuned into a feasible scheduling solution;and then a gradient search algorithm is applied to the feasible solution.This process is iterated until a satisfactory scheduling solution is obtained.The theoretic analyses,lots of simulation experiments and practical applications have manifested the approach's effectiveness.