摘要
针对传统生产作业排程难以实时应对车间异常情况发生的问题,提出了考虑异常因素的机械加工生产智能排程方法。考虑订单、设备、质量等异常因素,建立了应对异常因素的动态排程规则;综合考虑生产时序、加工设备等约束,以最小完工时间和最低能耗量为优化目标,建立了机械加工生产排程的多目标优化模型;采用带精英策略的非支配排序遗传算法,对某重型机械产品实际生产中的完工时间和能耗量进行了仿真,得到了运算结果。研究结果表明:考虑异常因素的机械加工生产智能排程方法在实际生产中能够准确、高效且动态智能地响应异常,提高生产计划制订效率20%以上;该方法能够辅助车间调度人员进行生产计划实时动态调整,满足企业对生产计划制订的需求。
Aiming at the problem that the traditional production scheduling was difficult to deal with the abnormal situation in the workshop in real time,an intelligent scheduling method for machining production considering abnormal factors was proposed.Thinking over the abnormal factors such as order,equipment,quality,etc.,the dynamic scheduling rules to deal with the abnormal factors was founded.Considering comprehensively the constraints of production time sequence and processing equipment,and taking the minimum completion time and minimum energy consumption as the optimization goals,the multi-objective optimization model of machining production scheduling was established.The non-dominated sorting genetic algorithm with elitist strategy was used to simulate the completion time and energy consumption in the actual production of a heavy machinery product,and the calculation results were obtained.The results show that the intelligent scheduling method considering the abnormal factors can accurately,efficiently and dynamically respond to the abnormal conditions in the actual production,and improve the efficiency of production planning by more than 20%.The above method was used to assist the job shop scheduling personnel to adjust the production plan dynamically in real time,and the needs of the enterprise to make the production plan were met.
作者
袁梦阳
杨晓英
张琪
肖博文
YUAN Meng-yang;YANG Xiao-ying;ZHANG Qi;XIAO Bo-wen(School of Mechanical and Electrical Engineering,Henan University of Science and Technology,Luoyang 471003,China)
出处
《机电工程》
CAS
北大核心
2021年第8期1030-1037,共8页
Journal of Mechanical & Electrical Engineering
基金
国家创新方法工作专项资助项目(2016IM030200)。
关键词
机械加工
生产智能排程
遗传算法
异常因素
machining production
production intelligent scheduling
genetic algorithm(GA)
abnormal factors
