基于本体案例匹配的扰动作业车间智能调度辅助决策
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摘要
为了快速响应扰动事件,恢复生产稳定,创新性地提出了多类型扰动事件本体与车间生产调度案例本体智能匹配技术。通过引入本体技术(ontology technology),将以往生产动态调度的成功案例进行规范化和标准化处理,构建动态调度本体化的成功案例库和案例库自我完善的学习机制。结合案例推理(case-based reasoning,CBR)技术及相似度理论,将新扰动的生产车间调度问题与案例库的成功案例进行匹配,依据相似度所在阈值区间,实现扰动车间智能调度辅助决策,从而达到提高决策过程时效性,缩短决策时间的目的。通过仿真结果表明:与传统CBR及混合驱动调度策略相比,在本体-CBR的方法下比传统CBR的案例检索精度大约提高了6%,比混合驱动调度策略相比,在决策时间上要快25 min,故有效地提高了案例检索的精度与响应时间。
In order to respond quickly to disturbance events and restore production stability, the intelligent matching technology of multi-type perturbation event ontology and workshop production scheduling case ontology were proposed.Firstly, the successful cases of dynamic scheduling in the past were standardized through the introduction of ontology technology, and the successful ontological case libraries and the self-improvement learning mechanism of the case libraries constructed. Then, the problem of the new perturbed production shop scheduling with the success stories of the case database were matched by Case-Based Reasoning(CBR) technology and similarity theory, and perturbation workshop intelligent auxiliary decision actualized based on the similarity degree of the threshold interval, which achieved the purpose of improving the decision-making process timeliness and shortening the decision-making time. Finally, the simulation results show that, compared with the traditional CBR and hybrid-driven scheduling strategy, the case retrieval accuracy of the ontology-CBR method is about 6% higher than that of the traditional CBR, and the decision time is 25 mins faster than that of the hybrid-driven scheduling strategy, so the case retrieval accuracy and response time are effectively improved.
In order to respond quickly to disturbance events and restore production stability, the intelligent matching technology of multi-type perturbation event ontology and workshop production scheduling case ontology were proposed.Firstly, the successful cases of dynamic scheduling in the past were standardized through the introduction of ontology technology, and the successful ontological case libraries and the self-improvement learning mechanism of the case libraries constructed. Then, the problem of the new perturbed production shop scheduling with the success stories of the case database were matched by Case-Based Reasoning(CBR) technology and similarity theory, and perturbation workshop intelligent auxiliary decision actualized based on the similarity degree of the threshold interval, which achieved the purpose of improving the decision-making process timeliness and shortening the decision-making time. Finally, the simulation results show that, compared with the traditional CBR and hybrid-driven scheduling strategy, the case retrieval accuracy of the ontology-CBR method is about 6% higher than that of the traditional CBR, and the decision time is 25 mins faster than that of the hybrid-driven scheduling strategy, so the case retrieval accuracy and response time are effectively improved.
引文
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[15]BERGMANN R,STAHL A.Similarity measures for object-oriented case representations[J].Lecture Notes in Computer Science,1998,1488:25-36.
[2]JENSEN M T.Robust and flexible scheduling with evolutionary computation[D].PhD Dissertation,Department of Computer Science University of Aarhus,2001,Denmark.
[3]HE W,SUN D H.Scheduling flexible job shop problem subject to machine breakdown with route changing and right-shift strategies[J].International Journal of Advanced Manufacturing Technology,2013,66(1-4):501-514.
[4]喻明让,张英杰,陈琨.考虑调整时间的作业车间调度与预防性维修集成方法[J].西安交通大学学报,2015,49(6):16-21.YU Mingrang,ZHANG Yingjie,CHENG Kun.Job-shop scheduling and preventive maintenance considering setup time[J].Journal of Xi'an Jiaotong University,2015,49(6):16-21.
[5]刘爱军,杨育,邢青松,等.柔性作业车间多目标动态调度[J].计算机集成制造系统,2011,17(12):2629-2637.LIU Aijun,YANG Yu,XING Qingsong,et al.Dynamic Scheduling on multi-objective flexible Job Shop[J].Computer Integrated Manufacturing Systems,2011,17(12):2629-2637.
[6]李平,唐秋华,夏绪辉,等.基于双层遗传编码的柔性作业车间自适应重调度研究[J].中国机械工程,2013,24(16):2195-2201.LI Ping,TANG Qiuhua,XIA Xuhui,et al.Self-adaptively rescheduling flexible job shop with double genetic code[J].China Mechanical Engineering,2013,24(16):2195-2201.
[7]王秀英,柴天佑,郑秉霖.炼钢-连铸智能调度软件的开发及应用[J].计算机集成制造系统,2006,12(08):1220-1226+1234.WANG Xiuying,CAI Tianyou,ZHENG Binglin.Intelling scheduling software&its application in steelmaking and continuous casting[J].Computer Integrated Manufacturing Systems,2006,12(08):1220-1226+1234.
[8]杜辉,叶文华,楼佩煌.飞机工装制造执行系统的设计与实现[J].中国机械工程,2010,21(21):2578-2583.DU Hui,YE Wenhua,LOU Peihuang.Design and implementation of manufacturing execution system for aircraft tools[J].China Mechanical Engineering,2010,21(21):2578-2583.
[9]黄辉,柴天佑,郑秉霖,等.面向铁钢对应的两级案例推理铁水动态调度系统[J].化工学报,2010,61(08):2021-2029.HUANG Hui,CHAI Tianyou,ZHENG Binglin,et al.Two-stage case-based reasoning for molten iron dynamic scheduling system oriented iron-steel correspondence[J].CIESC journal,2010,61(08):2021-2029.
[10]郭渊,周敬勇.集成本体和CBR的数据挖掘建模技术及其在工艺规划中应用研究[J].机械科学与技术,2017,36(4):579-585.GUO Yuan,ZHOU Jinyong.Data mining modeling and its application in CAPP by integrating ontology and CBR[J].Mechanical Science and Technology for Aerospace Engineering,2017,36(4):579-585.
[11]文家富,郭伟,邵宏宇.基于领域本体和CBR的案例知识检索方法[J].计算机集成制造系统,2017,23(07):1377-1385.WEN Jiafu,GUO Wei,SHAO Hongyu.Case retrieval methodology based on domain ontology and case-based reasoning[J].Computer Integrated Manufacturing Systems,2017,23(07):1377-1385.
[12]高晓荣,郭小阳,徐英卓.基于本体和CBR的钻井工程风险决策模型研究[J].计算机工程与应用,2015,51(03):265-270.GAO Xiaorong,GUO Xiaoyang,XU Yingzhuo.Research on drilling engineering risk decision model based on ontology and CBR[J].Computer Engineering and Applications,2015,51(03):265-270.
[13]阎红灿.本体建模与语义Web知识发现[M].北京:清华大学出版社,2015.
[14]刘晓文,胡克瑾.融合CBR和本体的决策支持系统框架及应用[J].计算机工程与应用,2009,45(03):235-238.LIU Xiaowen,HU Kejin.DSS framework based on ontology and CBR[J].Computer Engineering and Applications,2009,45(03):235-238.
[15]BERGMANN R,STAHL A.Similarity measures for object-oriented case representations[J].Lecture Notes in Computer Science,1998,1488:25-36.