解动态Job-shop调度问题的遗传算法

研究了动态Job-shop调度问题，并设计了一种改进的遗传算法。该算法借鉴启发式算法，使用向量进行遗传编码；遗传计算中引入种群间竞争，实现种群向更高层的平衡态进化，进而全局优化调度方案；利用调度评价函数及负荷表，对动态的生产调度进行了有效地评价。仿真实验表明了此法用于复合调度问题的优越性。

Genetic Algorithm of Solving Dynamic Job-shop Scheduling Problem

The problem of dynamic Job-shop scheduling is studied and an improved Genetic Algorithm (GA) for solving it is proposed. Firstly, the genetic code is encoded with vector based on heuristic algorithm. Secondly, the competition between genetic races is introduced into the GA so that races can evolve and develop into more advanced balance state. In this way, the performance of the scheduling is optimized. Thirdly, the dynamic production scheduling is valued by scheduling function and production ability chart. Finally, the simulation results show that the improved GA above is good in complex scheduling.