摘要
动态作业车间存在资源状态的不确定性和任务的随机性,难以寻找适用于多种复杂生产情景的通用调度规则。提出一种基于超启发式遗传规划的动态车间调度规则自动化发现方法,以最大完工时间和平均加权迟到时间为优化目标,利用机器排序规则的自动化发现,来提高不同生产情景下车间调度的动态适应性。通过对演化调度规则的语义分析,分析了GP树终端属性对不同优化目标的作用。实验结果表明,所提算法能够针对不同生产场景,生成适合的调度规则,且性能优于人工设计的基准调度规则。
The dynamic job shop has the uncertainty of resource state and the randomness of tasks, so it is difficult to find the common dispatching rules applicable to a variety of complex production scenarios. A method for automatic discovery of dynamic shop dispatching rules based on Hyper-Heuristic genetic programming is proposed, with makespan and average weighted tardiness as the optimization goals, is improved by using the automatic discovery of machine sequencing rules and the dynamic adaptability of workshop scheduling under different production scenarios. Through the semantic analysis of dispatching rules, the function of terminators on different optimization objectives is analyzed. The experiment result shows that the proposed algorithm can effectively generate appropriate dispatching rules which is obviously better than the manual designed benchmark rules for different production scenarios.
作者
张苏雨
王艳
纪志成
Zhang Suyu;Wang Yan;Ji Zhicheng(Engineering Research Center of Internet of Things Technology Applications Ministry of Education,Jiangnan University,Wuxi 214122,China)
出处
《系统仿真学报》
CAS
CSCD
北大核心
2020年第12期2494-2506,共13页
Journal of System Simulation
基金
国家自然科学基金(61973138)
国家重点研发计划(2018YFB1701903)。
