面向流程的MES模型和生产调度优化研究
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摘要
在市场经济不断发展的今天,传统的ERP/MRPⅡ已无法满足企业的需求,企业的战略性计划跟不上市场的变化,采用MES来提高企业竞争力是当今流程行业研究的热点之一。MES可以实现对生产过程的监督,将生产计划更加精细化,实时采集生产数据将其传递给需要的部门,不但可以提高生产的效率,而且可以增加企业的效益,改善产品的质量,是实现企业信息集成行之有效的措施。
本文对MES模型的总体方案进行设计,并研究其起核心作用的生产调度问题。主要研究内容如下：
在分析了流程行业特点的基础上,结合国际上的ISA SP95标准和MESA功能模型,用一套标准的术语、一致的概念和模型,构建出适合国内流程行业的MES模型。此模型完整的分配了整个企业的信息,增强了各个模块之间的连续性。管理部门可以充分利用各个模块提供的生产数据进行各种生产分析,加强了企业的生产管理。
深入研究了生产调度模块,将完整的生产调度问题根据炼油企业生产的特点分解为原油混炼调度、生产装置调度和油品调和调度。分析其生产的有关参数和约束条件,以炼油厂利润最大化为调度总目标,建立三个模型,通过这样的有效分解,减小调度模型的规模。三个模型之间既相互联系,又相互制约,实现生产全过程的优化。
在基本粒子群算法的基础上,引入变异算子到动态调整惯性权重的粒子群优化算法,将其运用在原油混炼调度、生产装置调度和油品调和调度模型上,使迭代中的惯性权重ω随粒子的速度和位置的更新而变化。与基本粒子群算法相比较,提高了全局搜索能力,使目标函数逼近最优解,提高了炼油企业的利润,并通过实例验证了算法的有效性。
With the continuous development of market economy, the traditional ERP/MRP II has been unable to meet the demands of enterprises whose strategic planning can not keep up with changes of market. To improve the competitiveness of enterprises, MES is one of the hottest study of modern flow process. MES can be used to supervise the production process, refine the production planning and collect production data real-time to pass it to the departments where it's needed. In this way, it can not only improve production efficiency, but also it can increase benefit and improve product quality. MES is an effective measure to integrate enterprise information.
The overall scheme of MES model is designed in this paper. In addition, production scheduling, which plays a central role in the MES is discussed. The important contents included are as follows:
Based on analyzing characteristics of flow process industry, MES model, combined with international ISA SP95 standards and function model of MESA is built with a standard set of terminology and unified concepts. This model completes distribution of the whole business information to enhance the continuity of each module. Management department can make full use of the production data of each module to analyze production process, as well as to strengthen the production management.
By deep study of production scheduling model, complete production scheduling is decomposed into scheduling of mixed crude oil, production equipment scheduling and mediation oil according to characteristics of oil production. By analyzing relational parameters and constrains, three models are built, taking maximum benefit as the overall goal. The effective decomposition can reduce the scale of scheduling model. The three components are mutual interrelated and restraint to realize optimization of whole production process.
Mutation operator is introduced to dynamic adjustment of the inertia weight PSO. The algorithm is used to crude oil mixing scheduling model, production equipment scheduling model and oil harmonic scheduling model. The inertia weight is dynamic adjusted with speed and position update of particle. Compared with the particle swarm algorithm, this algorithm improves the convergence rate, finds the optimal solution of the objective function and increases the profits of oil refining enterprises. Examples show the effectiveness of the algorithm.
本文对MES模型的总体方案进行设计,并研究其起核心作用的生产调度问题。主要研究内容如下：
在分析了流程行业特点的基础上,结合国际上的ISA SP95标准和MESA功能模型,用一套标准的术语、一致的概念和模型,构建出适合国内流程行业的MES模型。此模型完整的分配了整个企业的信息,增强了各个模块之间的连续性。管理部门可以充分利用各个模块提供的生产数据进行各种生产分析,加强了企业的生产管理。
深入研究了生产调度模块,将完整的生产调度问题根据炼油企业生产的特点分解为原油混炼调度、生产装置调度和油品调和调度。分析其生产的有关参数和约束条件,以炼油厂利润最大化为调度总目标,建立三个模型,通过这样的有效分解,减小调度模型的规模。三个模型之间既相互联系,又相互制约,实现生产全过程的优化。
在基本粒子群算法的基础上,引入变异算子到动态调整惯性权重的粒子群优化算法,将其运用在原油混炼调度、生产装置调度和油品调和调度模型上,使迭代中的惯性权重ω随粒子的速度和位置的更新而变化。与基本粒子群算法相比较,提高了全局搜索能力,使目标函数逼近最优解,提高了炼油企业的利润,并通过实例验证了算法的有效性。
With the continuous development of market economy, the traditional ERP/MRP II has been unable to meet the demands of enterprises whose strategic planning can not keep up with changes of market. To improve the competitiveness of enterprises, MES is one of the hottest study of modern flow process. MES can be used to supervise the production process, refine the production planning and collect production data real-time to pass it to the departments where it's needed. In this way, it can not only improve production efficiency, but also it can increase benefit and improve product quality. MES is an effective measure to integrate enterprise information.
The overall scheme of MES model is designed in this paper. In addition, production scheduling, which plays a central role in the MES is discussed. The important contents included are as follows:
Based on analyzing characteristics of flow process industry, MES model, combined with international ISA SP95 standards and function model of MESA is built with a standard set of terminology and unified concepts. This model completes distribution of the whole business information to enhance the continuity of each module. Management department can make full use of the production data of each module to analyze production process, as well as to strengthen the production management.
By deep study of production scheduling model, complete production scheduling is decomposed into scheduling of mixed crude oil, production equipment scheduling and mediation oil according to characteristics of oil production. By analyzing relational parameters and constrains, three models are built, taking maximum benefit as the overall goal. The effective decomposition can reduce the scale of scheduling model. The three components are mutual interrelated and restraint to realize optimization of whole production process.
Mutation operator is introduced to dynamic adjustment of the inertia weight PSO. The algorithm is used to crude oil mixing scheduling model, production equipment scheduling model and oil harmonic scheduling model. The inertia weight is dynamic adjusted with speed and position update of particle. Compared with the particle swarm algorithm, this algorithm improves the convergence rate, finds the optimal solution of the objective function and increases the profits of oil refining enterprises. Examples show the effectiveness of the algorithm.
引文
[1]曹江辉,王宁生,解放.制造执行系统现状与发展趋势.高技术通讯,2003.13(006)：100-105.
[2]M. McClellan, Applying manufacturing execution systems.1997:CRC.
[3]D. Scott. Comparative advantage through manufacturing execution systems.2002:IEEE.
[4]J.M. Sim o, P.C. StadziszG. Morel, Manufacturing execution systems for customized production. Journal of Materials Processing Technology,2006.179(1-3):268-275.
[5]顾佳晨,刘晓强,孙彦产.流程工业MES的现状与发展.冶金自动化,2003.27(004)：9-12.
[6]冯毅,萍荣冈.流程工业MES体系结构及模型设计.CONTROL AND INSTRUMENTS IN CHEMICAL INDUSTRY,2006.33(1).
[7]曹乐.面向大规模定制的扩展制造执行系统及其关键技术研究.2008,重庆大学.
[8]M. Hori, T. KawamuraA. Okano. OpenMES:scalable manufacturing execution framework based on distributed object computing.2002:IEEE.
[9]叶锋.流程工业生产管理系统研究.2004,大连理工大学.
[10]胡春,李平.连续工业生产与离散工业生产MES的比较.化工自动化及仪表,2003.30(005)：1-4.
[11]胡春,李平.制造执行系统体系结构中功能模型的研究.信息与控制,2002.31(006)：561-566.
[12]朱传军,饶运清.基于CORBA的可重构制造执行系统研究.中国机械工程,2004.15(023)：2097-2101.
[13]樊春江.兰州石化公司MES系统设计及应用研究.2007,兰州大学.
[14]柏强.化工企业生产管理系统的分析与设计.2006,中国地质大学(北京).
[15]E. FukudaH. Okutani, Semiconductor product manufacturing execution system and semiconductor product manufacturing method.2000, Google Patents.
[16]P. ValckenaersH. Van Brussel, Holonic manufacturing execution systems. CIRP Annals-Manufacturing Technology,2005.54(1):427-432.
[17]C.Y. Huang, Distributed manufacturing execution systems:A workflow perspective. Journal of intelligent manufacturing,2002.13(6):485-497.
[18]彭瑜.制造执行系统(MES)的发展和挑战.可编程控制器与工厂自动化(PLC FA),2004(006)：5-13.
[19]荣冈,张泉灵.MES的现状及发展.AUTOMATION PANORAMA,2008.25(3).
[20]F.T. Cheng, C.F. ChangS.L. Wu, Development of holonic manufacturing execution systems. Journal of intelligent manufacturing,2004.15(2):253-267.
[21]林巨广.面向装配过程的可视化制造执行系统及其关键技术的研究.2007,合肥工业 大学.
[22]潘操,杨小英.基于S95标准的流程工业制造执行系统的设计与实现.COMPUTERS AND APPLIED CHEMISTRY,2009.26(8).
[23]ANSI/ISA-95.00.01-2000, "Enterprise-Control System" Integration Part 1:Models and Terminology.2000.
[24]ANSI/ISA-95.00.02-2001, "Enterprise-Control System" Integration Part 2:Data Structures and Attributes.2001.
[25]ANSI/ISA-95.00.03-2002, "Enterprise-Control System" Integration Part 3:Activity Models of Manufacturing Operations Management.2002.
[26]ANSI/ISA-95.00.04-2003, "Enterprise-Control System" Integration Part 4:Object Models and Attributes of Manufacturing Operations Management.2003.
[27]ANSI/ISA-95.00.05-2007, "Enterprise-Control System" Integration Part 5:Business to Manufacturing Transactions.2007.
[28]肖佩,肖念,刘诏书.基于S95标准的装饰布企业车间生产调度信息流程分析.成组技术与生产现代化,2006.23(3).
[29]冷杉,黄榕华.企业与控制系统集成标准研究.制造执行系统(MES)在管控一体化中的作用研讨会论文集.7：1—4.
[30]雷建,王润孝.流程工业生产调度与控制集成系统研究.计算机应用,2006.26(5)：1193-1195.
[31]谢五峰.MES生产调度系统的研究.2007,北京交通大学.
[32]赵小强,荣冈.流程工业生产调度问题综述.化工自动化及仪表,2004.31(006)：8-13.
[33]J. KennedyR.C. Eberhart. Particle swarm optimization.1995:Perth, Australia.
[34]S.Y. Chu, Particle swarm optimization.2009.
[35]R.C. EberhartY. Shi. Particle swarm optimization:developments, applications and resources.2001:Piscataway, NJ, USA:IEEE.
[36]R. Poli, Analysis of the publications on the applications of particle swarm optimisation. Journal of Artificial Evolution and Applications,2008.2008:1-10.
[37]W. Jie-sheng, W. Jin-chengW. Wei, Self-tuning of PID parameters based on particle swarm optimization [J]. Control and Decision,2005.1.
[38]王凌,刘波.微粒群优化与调度算法.2008：清华大学出版社.
[39]D. Brandl. Enterprise/Control System Integration Using the ISA95 Standards.2001.
[40]李德芳,蒋白桦,王宏安.石化行业MES应用研究.
[41]陆凤海,刘斌,王吉勤.流程型与离散型工业生产计划与控制的比较研究.EQUIPMENT MANUFACTURING TECHNOLOGY,2009.4.
[42]D.Y. LeeF. DiCesare, Scheduling flexible manufacturing systems using Petri nets and heuristic search. Robotics and Automation, IEEE Transactions on,2002.10(2):123-132.
[43]王继东.炼油厂生产计划与调度系统研究.2005,浙江工业大学.
[44]赵小强.炼厂生产调度问题研究.2005,浙江大学.
[45]孟志青,胡奇英,杨晓琪.一种求解整数规划与混合整数规划非线性罚函数方法.控制与决策,2002.17(3)：310-314.
[46]刘涛,王华.面向MES的炼油厂调度建模策略研究.石油规划设计,2009.20(3).
[47]李雷,江燕斌.多应用集成的原油混炼操作支持系统.炼油技术与工程,2003.33(001)：55-57.
[48]兰鸿森,丁锋.基于炼油过程仿真的原油混炼比优化.石油炼制与化工,1997.28(011)：50-54.
[49]杜祜康,赵英凯.基于遗传算法的原油混合优化研究.化工自动化及仪表,(001).
[50]王继东,王万良.基于遗传算法的汽油调和生产优化研究.化工自动化及仪表,2005.32(001)：6-9.
[51]张建明,冯建华.两群微粒群算法及其在油品调和优化中的应用.化工学报,2008.59(007)：1721-1726.
[52]苏东卫.炼厂生产调度优化模型的建立及求解.2009,中国石油大学.
[53]张晓燕.装配型MES及其生产调度研究.2007,华中科技大学.
[54]R.C. EberhartY. Shi. Comparing inertia weights and constriction factors in particle swarm optimization.2002:IEEE.
[55]A. ChatterjeeP. Siarry, Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Computers & Operations Research,2006.33(3):859-871.
[56]X. HuR. Eberhart, Multiobjective optimization using dynamic neighborhood particle swarm optimization. Proceedings of the Evolutionary Computation on,2002:1677-1681.
[57]王丽,王晓凯.一种非线性改变惯性权重的粒子群算法.计算机工程与应用,2007.43(004)：47-48.
[58]K.E. ParsopoulosM.N. Vrahatis. Particle swarm optimization method in multiobjective problems.2002:ACM.
[59]龙文,梁昔明.动态调整惯性权重的粒子群优化算法.计算机应用,2009.29(08)：2240-2242.
[60]李宁,刘飞,孙德宝.基于带变异算子粒子群优化算法的约束布局优化研究.计算机学报,2004.27(007)：897-903.
[2]M. McClellan, Applying manufacturing execution systems.1997:CRC.
[3]D. Scott. Comparative advantage through manufacturing execution systems.2002:IEEE.
[4]J.M. Sim o, P.C. StadziszG. Morel, Manufacturing execution systems for customized production. Journal of Materials Processing Technology,2006.179(1-3):268-275.
[5]顾佳晨,刘晓强,孙彦产.流程工业MES的现状与发展.冶金自动化,2003.27(004)：9-12.
[6]冯毅,萍荣冈.流程工业MES体系结构及模型设计.CONTROL AND INSTRUMENTS IN CHEMICAL INDUSTRY,2006.33(1).
[7]曹乐.面向大规模定制的扩展制造执行系统及其关键技术研究.2008,重庆大学.
[8]M. Hori, T. KawamuraA. Okano. OpenMES:scalable manufacturing execution framework based on distributed object computing.2002:IEEE.
[9]叶锋.流程工业生产管理系统研究.2004,大连理工大学.
[10]胡春,李平.连续工业生产与离散工业生产MES的比较.化工自动化及仪表,2003.30(005)：1-4.
[11]胡春,李平.制造执行系统体系结构中功能模型的研究.信息与控制,2002.31(006)：561-566.
[12]朱传军,饶运清.基于CORBA的可重构制造执行系统研究.中国机械工程,2004.15(023)：2097-2101.
[13]樊春江.兰州石化公司MES系统设计及应用研究.2007,兰州大学.
[14]柏强.化工企业生产管理系统的分析与设计.2006,中国地质大学(北京).
[15]E. FukudaH. Okutani, Semiconductor product manufacturing execution system and semiconductor product manufacturing method.2000, Google Patents.
[16]P. ValckenaersH. Van Brussel, Holonic manufacturing execution systems. CIRP Annals-Manufacturing Technology,2005.54(1):427-432.
[17]C.Y. Huang, Distributed manufacturing execution systems:A workflow perspective. Journal of intelligent manufacturing,2002.13(6):485-497.
[18]彭瑜.制造执行系统(MES)的发展和挑战.可编程控制器与工厂自动化(PLC FA),2004(006)：5-13.
[19]荣冈,张泉灵.MES的现状及发展.AUTOMATION PANORAMA,2008.25(3).
[20]F.T. Cheng, C.F. ChangS.L. Wu, Development of holonic manufacturing execution systems. Journal of intelligent manufacturing,2004.15(2):253-267.
[21]林巨广.面向装配过程的可视化制造执行系统及其关键技术的研究.2007,合肥工业 大学.
[22]潘操,杨小英.基于S95标准的流程工业制造执行系统的设计与实现.COMPUTERS AND APPLIED CHEMISTRY,2009.26(8).
[23]ANSI/ISA-95.00.01-2000, "Enterprise-Control System" Integration Part 1:Models and Terminology.2000.
[24]ANSI/ISA-95.00.02-2001, "Enterprise-Control System" Integration Part 2:Data Structures and Attributes.2001.
[25]ANSI/ISA-95.00.03-2002, "Enterprise-Control System" Integration Part 3:Activity Models of Manufacturing Operations Management.2002.
[26]ANSI/ISA-95.00.04-2003, "Enterprise-Control System" Integration Part 4:Object Models and Attributes of Manufacturing Operations Management.2003.
[27]ANSI/ISA-95.00.05-2007, "Enterprise-Control System" Integration Part 5:Business to Manufacturing Transactions.2007.
[28]肖佩,肖念,刘诏书.基于S95标准的装饰布企业车间生产调度信息流程分析.成组技术与生产现代化,2006.23(3).
[29]冷杉,黄榕华.企业与控制系统集成标准研究.制造执行系统(MES)在管控一体化中的作用研讨会论文集.7：1—4.
[30]雷建,王润孝.流程工业生产调度与控制集成系统研究.计算机应用,2006.26(5)：1193-1195.
[31]谢五峰.MES生产调度系统的研究.2007,北京交通大学.
[32]赵小强,荣冈.流程工业生产调度问题综述.化工自动化及仪表,2004.31(006)：8-13.
[33]J. KennedyR.C. Eberhart. Particle swarm optimization.1995:Perth, Australia.
[34]S.Y. Chu, Particle swarm optimization.2009.
[35]R.C. EberhartY. Shi. Particle swarm optimization:developments, applications and resources.2001:Piscataway, NJ, USA:IEEE.
[36]R. Poli, Analysis of the publications on the applications of particle swarm optimisation. Journal of Artificial Evolution and Applications,2008.2008:1-10.
[37]W. Jie-sheng, W. Jin-chengW. Wei, Self-tuning of PID parameters based on particle swarm optimization [J]. Control and Decision,2005.1.
[38]王凌,刘波.微粒群优化与调度算法.2008：清华大学出版社.
[39]D. Brandl. Enterprise/Control System Integration Using the ISA95 Standards.2001.
[40]李德芳,蒋白桦,王宏安.石化行业MES应用研究.
[41]陆凤海,刘斌,王吉勤.流程型与离散型工业生产计划与控制的比较研究.EQUIPMENT MANUFACTURING TECHNOLOGY,2009.4.
[42]D.Y. LeeF. DiCesare, Scheduling flexible manufacturing systems using Petri nets and heuristic search. Robotics and Automation, IEEE Transactions on,2002.10(2):123-132.
[43]王继东.炼油厂生产计划与调度系统研究.2005,浙江工业大学.
[44]赵小强.炼厂生产调度问题研究.2005,浙江大学.
[45]孟志青,胡奇英,杨晓琪.一种求解整数规划与混合整数规划非线性罚函数方法.控制与决策,2002.17(3)：310-314.
[46]刘涛,王华.面向MES的炼油厂调度建模策略研究.石油规划设计,2009.20(3).
[47]李雷,江燕斌.多应用集成的原油混炼操作支持系统.炼油技术与工程,2003.33(001)：55-57.
[48]兰鸿森,丁锋.基于炼油过程仿真的原油混炼比优化.石油炼制与化工,1997.28(011)：50-54.
[49]杜祜康,赵英凯.基于遗传算法的原油混合优化研究.化工自动化及仪表,(001).
[50]王继东,王万良.基于遗传算法的汽油调和生产优化研究.化工自动化及仪表,2005.32(001)：6-9.
[51]张建明,冯建华.两群微粒群算法及其在油品调和优化中的应用.化工学报,2008.59(007)：1721-1726.
[52]苏东卫.炼厂生产调度优化模型的建立及求解.2009,中国石油大学.
[53]张晓燕.装配型MES及其生产调度研究.2007,华中科技大学.
[54]R.C. EberhartY. Shi. Comparing inertia weights and constriction factors in particle swarm optimization.2002:IEEE.
[55]A. ChatterjeeP. Siarry, Nonlinear inertia weight variation for dynamic adaptation in particle swarm optimization. Computers & Operations Research,2006.33(3):859-871.
[56]X. HuR. Eberhart, Multiobjective optimization using dynamic neighborhood particle swarm optimization. Proceedings of the Evolutionary Computation on,2002:1677-1681.
[57]王丽,王晓凯.一种非线性改变惯性权重的粒子群算法.计算机工程与应用,2007.43(004)：47-48.
[58]K.E. ParsopoulosM.N. Vrahatis. Particle swarm optimization method in multiobjective problems.2002:ACM.
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