ABSTRACT

In this paper, ant colony optimization algorithm is used, and then the records in the supply and demand documents in the material requirement planning (MRP) are used to simulate the city points that the salesperson moves, so that the artificial ants can move between cities. To find the shortest path through all cities, that is, to find the shortest path of MRP in the main file of supply and demand, to reduce the system execution time, improve the efficiency of related personnel. Experimental results show that compared with other algorithms, ACO algorithm can effectively shorten the deployment time of MRP and greatly improve the implementation efficiency.

中文翻译：

---

使用蚁群优化改进智能制造物料需求计划的执行

摘要

本文采用蚁群优化算法，然后利用物料需求计划（MRP）中供需文件中的记录来模拟销售人员移动的城市点，使人工蚂蚁在城市间移动。 . 通过所有城市找到最短路径，即在供需主文件中找到MRP的最短路径，以减少系统执行时间，提高相关人员的工作效率。实验结果表明，与其他算法相比，ACO算法可以有效缩短MRP的部署时间，大大提高了执行效率。