数字孪生驱动的工业园区“产—运—存”联动决策架构、模型与方法
|
摘要
小批量及定制化生产模式下,由资源、流程不确定性引发的复杂动态生产环境对大型多单元生产系统的运行控制精度提出了更高的要求。如何面向随机产生的动态性,对生产单元的执行状态进行精准建模与实时反映,并通过多单元在线联动决策实现对系统的适应性、迭代式、精细化管控来支持各环节高效协同运作是大型多单元生产物流系统过程控制的关键挑战。以工业园区"产—运—存"三类典型单元的实时联动运作为研究对象,在数字孪生的框架下,提出跨单元的数字孪生联动决策信息架构:通过"物理环境—虚拟模型"的动态精准映射与实时反馈控制,并采用目标级联法对虚拟层中多个具有独立决策且联动运作的"产—运—存"决策单元以全局优化、分布控制的模式进行系统性协调,实现大型系统的在线联动决策。最后以某大型化工生产企业的多单元联动生产过程为例,基于其实际生产数据对所建立的联动决策模型及目标级联法定量联动方法进行仿真和对比分析,结果表明所提方法能够有效实现多单元同步运作,减少生产总成本和订单在库时间。
Under the small batch and customized production mode,the complex and dynamic production environment caused by uncertainty of resources and processes requires higher control accuracy for operation and control of large multi-unit systems.It is a key challenge for process control of large multi-unit production logistics system that how to accurately model and real-time reflect the execution status of production unit and implement the adaptive,iterative and accurate management and control of system to support the efficient collaborative operation of each segment through multi-unit online synchronized decision-making.By taking the real-time synchronized operation of typical industrial park system composed of three typical units of production,transportation and storage as the research object,and under the Digital Twin(DT)framework,the cross-unit synchronized decision-making information architecture was proposed to implement the online synchronized control of large system through the dynamic and precise mapping and real-time feedback control of"physical environment-virtual model".Meanwhile,the Analytical Target Cascading(ATC)method was adopted to synchronize and control the system systematically in the mode of global optimization and distributed control.By taking the multi-unit synchronized production process of a large chemical production enterprise as an example,the synchronized decision-making model and ATC quantitative synchronized method were simulated,compared and analyzed based on the actual production data.The results showed that the proposed method could effectively realize the multi-unit synchronized operation and reduce the total production cost and the instock time of orders.
Under the small batch and customized production mode,the complex and dynamic production environment caused by uncertainty of resources and processes requires higher control accuracy for operation and control of large multi-unit systems.It is a key challenge for process control of large multi-unit production logistics system that how to accurately model and real-time reflect the execution status of production unit and implement the adaptive,iterative and accurate management and control of system to support the efficient collaborative operation of each segment through multi-unit online synchronized decision-making.By taking the real-time synchronized operation of typical industrial park system composed of three typical units of production,transportation and storage as the research object,and under the Digital Twin(DT)framework,the cross-unit synchronized decision-making information architecture was proposed to implement the online synchronized control of large system through the dynamic and precise mapping and real-time feedback control of"physical environment-virtual model".Meanwhile,the Analytical Target Cascading(ATC)method was adopted to synchronize and control the system systematically in the mode of global optimization and distributed control.By taking the multi-unit synchronized production process of a large chemical production enterprise as an example,the synchronized decision-making model and ATC quantitative synchronized method were simulated,compared and analyzed based on the actual production data.The results showed that the proposed method could effectively realize the multi-unit synchronized operation and reduce the total production cost and the instock time of orders.
引文
[1]SILVEIRA G D,BORENSTEIN D,FOGLIATTO F S.Mass customization:Literature review and research directions[J].International Journal of Production Economics,2001,72(1):1-13.
[2]ZHANG Xia,LU Deyin,CAI Gennv.The Revelation and summarizeof the theory on industry section based on industry cluster[J].Science&Technology Progress and Policy,2005,22(4):171-173(in Chinese).[张霞,鲁德银,蔡根女.基于产业集群的工业园区研究的理论综述及其启示[J].科技进步与对策,2005,22(4):171-173.]
[3]QIU X,HUANG G Q.Supply Hub in Industrial Park(SHIP):the value of freight consolidation[J].Computer&Industrial Engineering,2013,65(1):16-27.
[4]QIU X,HUANG G Q.Transportation service sharing and replenishment/delivery scheduling in Supply Hub in Industrial Park(SHIP)[J].International Journal of Production Economics,2016,175:109-120.
[5]MA Shihua,GONG Fengmei,LIU Fenghua.Production and distribution collaborative decision-making based on supply hub[J].Computer Intergrated Manufacturing Systems,2008,14(12):2421-2430(in Chinese).[马士华,龚凤美,刘风华.基于集配中心的生产和配送协同决策研究[J].计算机集成制造系统,2008,14(12):2421-2430.]
[6]TAO Fei,LIU Weiran,LIU Jianhua,et al.Digital twin and its potential application exploration[J].Computer Intergrated Manufacturing Systems,2018,24(1):1-18(in Chinese).[陶飞,刘蔚然,刘检华,等.数字孪生及其应用探索[J].计算机集成制造系统,2018,24(1):1-18.]
[7]SCHROEDER G N,STEINMETZ C,PEREIRA C E,et al.Digital twin data modeling with AutomationML and a communication methodology for data exchange[J].IFAC-Papers OnLine,2016,49(30):12-17.
[8]AITOR M,GORKA V,AITOR A,et al.Virtualisation process of a sheet metal punching machine within the Industry4.0vision[J].International Journal on Interactive Design&Manufacturing,2016,11(2):1-9.
[9]UHLEMANN T H J,SCHOCK C,LEHMANN C,et al.The digital twin:demonstrating the potential of real time data acquisition in production systems[J].Procedia Manufacturing,2017,9:113-120.
[10]WANG X V,KEMENY Z,VANCZA J,et al.Human-robot collaborative assembly in cyber-physical production:Classification framework and implementation[J].CIRP AnnalsManufacturing Technology,2017,66(1):5-8.
[11]BAZILEVS Y,DENG X,KOROBENKO A,et al.Isogeometric fatigue damage prediction in large-scale composite structures driven by dynamic sensor data[J].Journal of Applied Mechanics,2015,82(9):091008-091008-12.
[12]BIELEFELDT B,HOCHHALTER J,HARTL D,et al.Computationally efficient analysis of SMA sensory particles embedded in complex aerostructures using a substructure approach[C]//Proceeding of ASME 2015Conference on Smart Materials,Adaptive Structures and Intelligent System.New York,N.Y.,USA:ASME,2015:V001T02A007.DOI:10.1115/SMASIS 2015-8975.
[13]CORONADO P D U,LYNN R,LOUHICHI W,et al.Part data integration in the shop floor digital twin:mobile and cloud technologies to enable a manufacturing execution system[J].Journal of Manufacturing Systems,2018,48(C):25-33.DOI:10.1016/j.jmsy.2018.02.0021.
[14]SODERBERG R,WARMEFJORD K,CARLSON J S,et al.Toward a digital twin for real-time geometry assurance in individualized production[J].CIRP Annals-Manufacturing Technology,2017,66(1):137-140.
[15]BANERJEE A,MITTALR S,DALAL R,et al.Generating digital twin models using knowledge graphs for industrial production lines[C]//Proceedings of the 2017ACM on Web Science Conference.New York,N.Y.,USA:ACM,2017:425-430.DOI:10.1145/3091478.3162383.
[16]TAO Fei,CHENG Ying,CHEN Jianfeng,et al.Theories and technologies for cyber-physical fusion in digital twin shop-floor[J].Computer Intergrated Manufacturing Systems,2017,23(8):1603-1611(in Chinese).[陶飞,程颖,程江峰,等.数字孪生车间信息物理融合理论与技术[J].计算机集成制造系统,2017,23(8):1603-1611.]
[17]TAO Fei,ZHANG Meng,CHENG Jiangfeng,et al.Digital twin workshop:a new paradigm for future workshop[J].Computer Intergrated Manufacturing Systems,2017,23(1):1-9(in Chinese).[陶飞,张萌,程江峰,等.数字孪生车间:一种未来车间运行新模式[J].计算机集成制造系统,2017,23(1):1-9.]
[18]QU Ting,ZHANG Kai,LUO Hao,et al.Internet-of-things based dynamic synchronization of production and logistics:mechanism,system and case study[J].Journal of Mechanical Engineering,2015,51(20):36-44(in Chinese).[屈挺,张凯,罗浩,等.物联网驱动的“生产-物流”动态联动机制、系统及案例[J].机械工程学报,2015,51(20):36-44.]
[19]QU Ting,ZHANG Kai,LI Congdong,et al.Synchronization decision-making and control method for the high-dynamic production system operation in opti-stage under environment of Internet of things[J].Journal of Mechanical Engineering,2018,54(16):24-33(in Chinese).[屈挺,张凯,李从东,等.物联网环境下面向高动态性生产系统优态运行的联动决策与控制方法[J].机械工程学报,2018,54(16):24-33.]
[20]KIM H M,MICHELENA N F,PAPALAMBROS P Y,et al.Target cascading in optimal system design[J].Journal of Mechanical Design,2003,125(3):474-480.
[2]ZHANG Xia,LU Deyin,CAI Gennv.The Revelation and summarizeof the theory on industry section based on industry cluster[J].Science&Technology Progress and Policy,2005,22(4):171-173(in Chinese).[张霞,鲁德银,蔡根女.基于产业集群的工业园区研究的理论综述及其启示[J].科技进步与对策,2005,22(4):171-173.]
[3]QIU X,HUANG G Q.Supply Hub in Industrial Park(SHIP):the value of freight consolidation[J].Computer&Industrial Engineering,2013,65(1):16-27.
[4]QIU X,HUANG G Q.Transportation service sharing and replenishment/delivery scheduling in Supply Hub in Industrial Park(SHIP)[J].International Journal of Production Economics,2016,175:109-120.
[5]MA Shihua,GONG Fengmei,LIU Fenghua.Production and distribution collaborative decision-making based on supply hub[J].Computer Intergrated Manufacturing Systems,2008,14(12):2421-2430(in Chinese).[马士华,龚凤美,刘风华.基于集配中心的生产和配送协同决策研究[J].计算机集成制造系统,2008,14(12):2421-2430.]
[6]TAO Fei,LIU Weiran,LIU Jianhua,et al.Digital twin and its potential application exploration[J].Computer Intergrated Manufacturing Systems,2018,24(1):1-18(in Chinese).[陶飞,刘蔚然,刘检华,等.数字孪生及其应用探索[J].计算机集成制造系统,2018,24(1):1-18.]
[7]SCHROEDER G N,STEINMETZ C,PEREIRA C E,et al.Digital twin data modeling with AutomationML and a communication methodology for data exchange[J].IFAC-Papers OnLine,2016,49(30):12-17.
[8]AITOR M,GORKA V,AITOR A,et al.Virtualisation process of a sheet metal punching machine within the Industry4.0vision[J].International Journal on Interactive Design&Manufacturing,2016,11(2):1-9.
[9]UHLEMANN T H J,SCHOCK C,LEHMANN C,et al.The digital twin:demonstrating the potential of real time data acquisition in production systems[J].Procedia Manufacturing,2017,9:113-120.
[10]WANG X V,KEMENY Z,VANCZA J,et al.Human-robot collaborative assembly in cyber-physical production:Classification framework and implementation[J].CIRP AnnalsManufacturing Technology,2017,66(1):5-8.
[11]BAZILEVS Y,DENG X,KOROBENKO A,et al.Isogeometric fatigue damage prediction in large-scale composite structures driven by dynamic sensor data[J].Journal of Applied Mechanics,2015,82(9):091008-091008-12.
[12]BIELEFELDT B,HOCHHALTER J,HARTL D,et al.Computationally efficient analysis of SMA sensory particles embedded in complex aerostructures using a substructure approach[C]//Proceeding of ASME 2015Conference on Smart Materials,Adaptive Structures and Intelligent System.New York,N.Y.,USA:ASME,2015:V001T02A007.DOI:10.1115/SMASIS 2015-8975.
[13]CORONADO P D U,LYNN R,LOUHICHI W,et al.Part data integration in the shop floor digital twin:mobile and cloud technologies to enable a manufacturing execution system[J].Journal of Manufacturing Systems,2018,48(C):25-33.DOI:10.1016/j.jmsy.2018.02.0021.
[14]SODERBERG R,WARMEFJORD K,CARLSON J S,et al.Toward a digital twin for real-time geometry assurance in individualized production[J].CIRP Annals-Manufacturing Technology,2017,66(1):137-140.
[15]BANERJEE A,MITTALR S,DALAL R,et al.Generating digital twin models using knowledge graphs for industrial production lines[C]//Proceedings of the 2017ACM on Web Science Conference.New York,N.Y.,USA:ACM,2017:425-430.DOI:10.1145/3091478.3162383.
[16]TAO Fei,CHENG Ying,CHEN Jianfeng,et al.Theories and technologies for cyber-physical fusion in digital twin shop-floor[J].Computer Intergrated Manufacturing Systems,2017,23(8):1603-1611(in Chinese).[陶飞,程颖,程江峰,等.数字孪生车间信息物理融合理论与技术[J].计算机集成制造系统,2017,23(8):1603-1611.]
[17]TAO Fei,ZHANG Meng,CHENG Jiangfeng,et al.Digital twin workshop:a new paradigm for future workshop[J].Computer Intergrated Manufacturing Systems,2017,23(1):1-9(in Chinese).[陶飞,张萌,程江峰,等.数字孪生车间:一种未来车间运行新模式[J].计算机集成制造系统,2017,23(1):1-9.]
[18]QU Ting,ZHANG Kai,LUO Hao,et al.Internet-of-things based dynamic synchronization of production and logistics:mechanism,system and case study[J].Journal of Mechanical Engineering,2015,51(20):36-44(in Chinese).[屈挺,张凯,罗浩,等.物联网驱动的“生产-物流”动态联动机制、系统及案例[J].机械工程学报,2015,51(20):36-44.]
[19]QU Ting,ZHANG Kai,LI Congdong,et al.Synchronization decision-making and control method for the high-dynamic production system operation in opti-stage under environment of Internet of things[J].Journal of Mechanical Engineering,2018,54(16):24-33(in Chinese).[屈挺,张凯,李从东,等.物联网环境下面向高动态性生产系统优态运行的联动决策与控制方法[J].机械工程学报,2018,54(16):24-33.]
[20]KIM H M,MICHELENA N F,PAPALAMBROS P Y,et al.Target cascading in optimal system design[J].Journal of Mechanical Design,2003,125(3):474-480.