炼油过程动态优化调度研究
|
摘要
炼油生产企业是流程工业的典型代表,激烈的市场竞争使炼油企业提高利润和减小成本的压力越来越大。生产调度作为炼油企业过程综合自动化系统(Computer Integrated Processing System, CIPS)的中间环节,处于企业运营的核心位置,是企业信息集成和任务集成的核心,是企业在现有条件下提高资源利用率、降低能耗、节约成本、提高效益、增强竞争能力的重要手段。目前的炼油过程调度往往忽略了由于生产活动改变而导致的炼油装置长过渡过程,得到的是粗放化的调度结果。因而,研究炼油过程动态优化和调度,对提高生产调度的质量和企业效益具有重要的意义。
炼油生产过程具有连续、平稳、高能耗、装置复杂且有较长的过渡过程等突出特点。当物料切换、装置开停等计划内事件或原料中断、装置故障等计划外随机事件发生时都会引起装置的过渡过程。本文研究了计划内事件引起的装置过渡过程,在调度模型中包含装置过渡过程,从而实现了调度与控制的有机衔接。对计划外随机事件,如何有效结合调度专家的经验,在保证调度方案可行的同时,具有一定的全局最优性,是目前炼油企业动态调度问题中的一个难点,也是本文的另一个研究点。
本文的主要内容包括以下几个方面：
(1)介绍了炼油企业生产调度在综合自动化系统体系结构中的地位和作用,指出调度与控制的结合点在于稳态优化。概括了炼油过程中各生产环节的生产调度研究现状,并对当前炼油企业生产调度建模和优化的方法进行了比较研究。认为将数学规划、启发式方法和人工智能相结合,在优化性和实时性之间找到一个令人满意的平衡点,是解决动态优化调度的关键问题。并结合炼油过程的特点,指出了炼油厂过程生产调度建模中存在的主要问题。
(2)根据对装置过渡过程的分析,给出了面向调度的装置过渡过程模型的一般形式。对模型的特性分析表明,过渡过程模型可实现调度与控制之间的不同时间域转换,能够得到优化控制的设定值,可以得到调度所需要的动态收率,完全能够满足调度对模型的输入输出要求。以催化裂化装置为例,在七集总模型基础上化简得到催化裂化装置的过渡过程模型,并验证了其有效性。通过催化裂化过渡过程模型,得到了部分指导装置优化控制的指令,为实现调度与控制之间的连接打下了基础。
(3)炼油装置生产活动的改变常常存在比较长的过渡过程,根据过渡过程的这一特点,采用连续时间建模方式,以最大利润为目标,建立起包含过渡过程的生产调度模型。通过开关变量的设置可以在平稳态时将调度模型中的很多约束简化处理,不会增加模型的复杂性。仿真结果表明,所建模型可以实现更加精细化的调度,有效提高企业经济效益。根据模型的特点,基于对惯性权重和加速系数的改进策略,设计了改进的两阶段PSO算法。仿真实验表明该算法在求解TSP问题时具有较好的稳定性和鲁棒性。
(4)针对动态调度中规则获取困难的问题,基于粗集在知识获取方面的独特优势,把变精度粗集应用于生产调度中,描述了在大量历史经验数据中获取调度规则的过程及其应用实例。对调度中存在的多决策属性并存的决策表分解问题,提出了有因果关系决策属性的决策表分解方法。针对粗集应用中的决策表属性约简问题,提出了有约束指导的属性约简算法。对现场调研和根据生产数据得到的炼油厂调度规则进行了归纳和总结,并举例说明了规则在动态调度中的应用。
(5)对于流程工业来说,由于生产工艺基本固定,物料流动的先后顺序基本确定,装置也没有频繁的切换。基于流程模拟的思想,结合现代计算机软件技术,将数学规划、启发式方法和人工智能有效结合,设计了动态调度优化系统,在一定程度上解决了石油炼制行业动态调度的建模困难,并在保证调度实时性的同时获得较好的调度优化性。由调度人员先根据经验确定好装置的选择和物料生产顺序,再根据软件内嵌的优化模型进行优化,从而保证调度可行基础上的优化。动态调度优化系统已经编制成软件,在齐鲁石化炼油厂试用并获得好评。
Refinery production enterprise is a typical representative of process industry, which meets the growing pressure of improving profits and reducing cost from market competition. As the intermediate links of a Computer Integrated Processing System (CIPS), refinery production scheduling is the core of enterprise information integration and task integration, as well as the core of business operations. Also, scheduling plays an important role in improving resource utilization, reducing energy consumption, saving costs, improving efficiency and enhancing competitiveness for the enterprises in current conditions. At present, the long transition process of refinery unit, results from changes in production, and is often overlooked in refining scheduling, which results in an extensive scheduling result. Therefore, it is important to research on dynamic optimization and scheduling of the refining process to improve the scheduling quality and business benefits.
There are many salient features in the refining process, such as continuous, smooth, high energy consumption, installation of complex and long transition process, etc. And transition process will be brought on when the material switches, devices stop or interruption of raw materials, equipment failures and other unplanned occurrence of random events. This paper studies the device transition process caused by the planned events, and achieves organic convergence of scheduling and control through including the device transition process in the scheduling model. Generally speaking, it is difficult to globally optimize scheduling combined the easily experience of experts effectively when unforeseen events happen, which is also a research point in this paper.
This article includes the following aspects:
(1) It is pointed out that steady-state optimization is the point of combining scheduling with control after introducing the roles of scheduling. Current modeling and optimization methods of refinery production scheduling are compared and their research status is outlined in various refining process. The key issue of dynamic optimal scheduling is to combine mathematical programming, heuristic methods and artificial intelligence together to find a satisfactory balance point between optimization and real-time. The main problems in production scheduling modeling are pointed out after the characteristics of the refining process are analyzed.
(2) The general form of the transition process model for scheduling is achieved based on the analysis of the transition process unit. Analysis shows that the transition process model can realize the different time-domain conversion between scheduling and control, optimize the control settings, get the dynamic yield that scheduling requires, and fully be able to meet the scheduling requirements of the model input and output. The transition process model of fluid catalytic cracking unit (FCC) is achieved based on the seven lumped model, whose validity is verified by an example. Part of the optimal control instruction has been obtained through the transition process model of FCC, which lays foundation for the combination between scheduling and control.
(3) A long transition process often happens when a production activity is changed for oil refining unit. According to the characteristics of the transition process, the production scheduling model including the transition process is established as the goal of maximum profit, using continuous-time modeling approach. Through the switch variable settings, scheduling model will be simplified owing to a lot of restrictions in a stable state, which will not increase the complexity of the model. The simulation results show that the model can achieve a more refined scheduling and effectively improve the economic efficiency of enterprises. According to the model's characteristics, an improved two-stage PSO algorithm (TSPSO) is designed based on the improved strategy of inertia weight and acceleration coefficients. Simulation results show that the algorithm has a good stability and robustness ability in solving the TSP problem.
(4) As it is difficult to acquire rules in dynamic scheduling, the rough set was applied to production scheduling based on its unique advantages in the knowledge acquisition. The process of acquiring production scheduling rules from a large number of data based on rough set is described and an example is given. A decision-making table decomposition method is proposed for the causal relationship between decision attribute, which solved the co-existence of multi-attribute in scheduling decision table. Applications for the rough set decision table attribute reduction problem, we introduced the reduction algorithm under instruction. Found the scheduling rules in the historical production data solve the problems of rule-based scheduling. Scheduling rules were summed up and concluded which were obtained from on-site research and production data, and illustrates the application of rules in dynamic scheduling.
(5) Since the production process is fixed basically, and there are no frequent device switches, the order of material flow is generally fixed in the process industry. The mathematical programming, heuristic methods, and artificial intelligence are effectively integrated to design dynamic scheduling optimization system, based on the thought of process simulation and modern computer software technology. At a certain extent, the system can solve the problem of modeling for dynamic scheduling in oil refining industry, and ensure to obtain better optimization and satisfy the real-time requirement. According to the optimization model embedded in the software, the system ensures optimal scheduling on a viable basis, after determining the device choice and material sequence based on personnel experience. Dynamic scheduling optimization system has been developed into trial software in the Qilu Petrochemical refinery and received high praise.
炼油生产过程具有连续、平稳、高能耗、装置复杂且有较长的过渡过程等突出特点。当物料切换、装置开停等计划内事件或原料中断、装置故障等计划外随机事件发生时都会引起装置的过渡过程。本文研究了计划内事件引起的装置过渡过程,在调度模型中包含装置过渡过程,从而实现了调度与控制的有机衔接。对计划外随机事件,如何有效结合调度专家的经验,在保证调度方案可行的同时,具有一定的全局最优性,是目前炼油企业动态调度问题中的一个难点,也是本文的另一个研究点。
本文的主要内容包括以下几个方面：
(1)介绍了炼油企业生产调度在综合自动化系统体系结构中的地位和作用,指出调度与控制的结合点在于稳态优化。概括了炼油过程中各生产环节的生产调度研究现状,并对当前炼油企业生产调度建模和优化的方法进行了比较研究。认为将数学规划、启发式方法和人工智能相结合,在优化性和实时性之间找到一个令人满意的平衡点,是解决动态优化调度的关键问题。并结合炼油过程的特点,指出了炼油厂过程生产调度建模中存在的主要问题。
(2)根据对装置过渡过程的分析,给出了面向调度的装置过渡过程模型的一般形式。对模型的特性分析表明,过渡过程模型可实现调度与控制之间的不同时间域转换,能够得到优化控制的设定值,可以得到调度所需要的动态收率,完全能够满足调度对模型的输入输出要求。以催化裂化装置为例,在七集总模型基础上化简得到催化裂化装置的过渡过程模型,并验证了其有效性。通过催化裂化过渡过程模型,得到了部分指导装置优化控制的指令,为实现调度与控制之间的连接打下了基础。
(3)炼油装置生产活动的改变常常存在比较长的过渡过程,根据过渡过程的这一特点,采用连续时间建模方式,以最大利润为目标,建立起包含过渡过程的生产调度模型。通过开关变量的设置可以在平稳态时将调度模型中的很多约束简化处理,不会增加模型的复杂性。仿真结果表明,所建模型可以实现更加精细化的调度,有效提高企业经济效益。根据模型的特点,基于对惯性权重和加速系数的改进策略,设计了改进的两阶段PSO算法。仿真实验表明该算法在求解TSP问题时具有较好的稳定性和鲁棒性。
(4)针对动态调度中规则获取困难的问题,基于粗集在知识获取方面的独特优势,把变精度粗集应用于生产调度中,描述了在大量历史经验数据中获取调度规则的过程及其应用实例。对调度中存在的多决策属性并存的决策表分解问题,提出了有因果关系决策属性的决策表分解方法。针对粗集应用中的决策表属性约简问题,提出了有约束指导的属性约简算法。对现场调研和根据生产数据得到的炼油厂调度规则进行了归纳和总结,并举例说明了规则在动态调度中的应用。
(5)对于流程工业来说,由于生产工艺基本固定,物料流动的先后顺序基本确定,装置也没有频繁的切换。基于流程模拟的思想,结合现代计算机软件技术,将数学规划、启发式方法和人工智能有效结合,设计了动态调度优化系统,在一定程度上解决了石油炼制行业动态调度的建模困难,并在保证调度实时性的同时获得较好的调度优化性。由调度人员先根据经验确定好装置的选择和物料生产顺序,再根据软件内嵌的优化模型进行优化,从而保证调度可行基础上的优化。动态调度优化系统已经编制成软件,在齐鲁石化炼油厂试用并获得好评。
Refinery production enterprise is a typical representative of process industry, which meets the growing pressure of improving profits and reducing cost from market competition. As the intermediate links of a Computer Integrated Processing System (CIPS), refinery production scheduling is the core of enterprise information integration and task integration, as well as the core of business operations. Also, scheduling plays an important role in improving resource utilization, reducing energy consumption, saving costs, improving efficiency and enhancing competitiveness for the enterprises in current conditions. At present, the long transition process of refinery unit, results from changes in production, and is often overlooked in refining scheduling, which results in an extensive scheduling result. Therefore, it is important to research on dynamic optimization and scheduling of the refining process to improve the scheduling quality and business benefits.
There are many salient features in the refining process, such as continuous, smooth, high energy consumption, installation of complex and long transition process, etc. And transition process will be brought on when the material switches, devices stop or interruption of raw materials, equipment failures and other unplanned occurrence of random events. This paper studies the device transition process caused by the planned events, and achieves organic convergence of scheduling and control through including the device transition process in the scheduling model. Generally speaking, it is difficult to globally optimize scheduling combined the easily experience of experts effectively when unforeseen events happen, which is also a research point in this paper.
This article includes the following aspects:
(1) It is pointed out that steady-state optimization is the point of combining scheduling with control after introducing the roles of scheduling. Current modeling and optimization methods of refinery production scheduling are compared and their research status is outlined in various refining process. The key issue of dynamic optimal scheduling is to combine mathematical programming, heuristic methods and artificial intelligence together to find a satisfactory balance point between optimization and real-time. The main problems in production scheduling modeling are pointed out after the characteristics of the refining process are analyzed.
(2) The general form of the transition process model for scheduling is achieved based on the analysis of the transition process unit. Analysis shows that the transition process model can realize the different time-domain conversion between scheduling and control, optimize the control settings, get the dynamic yield that scheduling requires, and fully be able to meet the scheduling requirements of the model input and output. The transition process model of fluid catalytic cracking unit (FCC) is achieved based on the seven lumped model, whose validity is verified by an example. Part of the optimal control instruction has been obtained through the transition process model of FCC, which lays foundation for the combination between scheduling and control.
(3) A long transition process often happens when a production activity is changed for oil refining unit. According to the characteristics of the transition process, the production scheduling model including the transition process is established as the goal of maximum profit, using continuous-time modeling approach. Through the switch variable settings, scheduling model will be simplified owing to a lot of restrictions in a stable state, which will not increase the complexity of the model. The simulation results show that the model can achieve a more refined scheduling and effectively improve the economic efficiency of enterprises. According to the model's characteristics, an improved two-stage PSO algorithm (TSPSO) is designed based on the improved strategy of inertia weight and acceleration coefficients. Simulation results show that the algorithm has a good stability and robustness ability in solving the TSP problem.
(4) As it is difficult to acquire rules in dynamic scheduling, the rough set was applied to production scheduling based on its unique advantages in the knowledge acquisition. The process of acquiring production scheduling rules from a large number of data based on rough set is described and an example is given. A decision-making table decomposition method is proposed for the causal relationship between decision attribute, which solved the co-existence of multi-attribute in scheduling decision table. Applications for the rough set decision table attribute reduction problem, we introduced the reduction algorithm under instruction. Found the scheduling rules in the historical production data solve the problems of rule-based scheduling. Scheduling rules were summed up and concluded which were obtained from on-site research and production data, and illustrates the application of rules in dynamic scheduling.
(5) Since the production process is fixed basically, and there are no frequent device switches, the order of material flow is generally fixed in the process industry. The mathematical programming, heuristic methods, and artificial intelligence are effectively integrated to design dynamic scheduling optimization system, based on the thought of process simulation and modern computer software technology. At a certain extent, the system can solve the problem of modeling for dynamic scheduling in oil refining industry, and ensure to obtain better optimization and satisfy the real-time requirement. According to the optimization model embedded in the software, the system ensures optimal scheduling on a viable basis, after determining the device choice and material sequence based on personnel experience. Dynamic scheduling optimization system has been developed into trial software in the Qilu Petrochemical refinery and received high praise.
引文
[1]柴天佑,金以慧,任德祥等.基于三层结构的流程工业现代集成制造系统[J].控制工程,2002,9(3)：1-6.
[2]李永康.关于管控一体化的中央控制室设计方突的探讨[J].石油化工自动化,1999,(4)：8-10.
[3]侯凯锋,叶向东.大型化炼油厂的自动化和信息集成[J].石油化工自动化,2002,(5)：1-4.
[4]王树青.先进控制技术及应用[M].北京：化学工业出版社,2002.
[5]Nystrom R H, Harjunkoski I, Kroll A. Production optimization for continuously operated processes with optimal operation and scheduling of multiple units[J]. Computers and Chemical Engineering,2006,30: 392-406.
[6]Sebastian T M, Antonio F T, Grossmann I E. Simultaneous cyclic scheduling and optimal control of polymerization reactors[J]. American Institute of Chemical Engineers,2007,53(9):2300-2315.
[7]Sebastian T M, Antonio F T, Grossmann I E. Lagrangean heuristic for the scheduling and control of polymerization reactors[J]. American Institute of Chemical Engineers,2008,54(1):162-182.
[8]Chatzidoukas C, Pistikopoulos S, Kiparissides C. A hierarchical optimization approach to optimal production scheduling in an industrial continuous olefin polymerization reactor[J]. Macromolecular Reaction Engineering,2009,3:36-46.
[9]Abraham J N, Rao K S. Integration of generative and evaluative models for production scheduling of lube oil plants in a petroleum refinery [J]. Applied Mathematical Modelling,2009,33:1213-1227.
[10]Ramage M P, etc. Development of mobil's kinetic reforming model[J]. Chemical Engineering Science,1980,35(1):41-48.
[11]王建平,许先焜,翁惠新等.渣油催化裂化14集总动力学模型的建立. 化工学报[J].2007,58(1)：86-94.
[12]Abraham J N, Rao K S. Integration of generative and evaluative models for production scheduling of lube oil plants in a petroleum refinery[J]. Applied Mathematical Modelling,2009,33(3):1213-1227.
[13]Bonner & Moore. RPMS (Refinery and Petrochemical Modeling System): a system descriptive [M]. Houston, TX, USA:Bonner & Moore Management Science,1979.
[14]PIMS system reference, version 11.0 [M]. Cambridge, MA, USA:Aspen Technology Inc.1999.
[15]吴倚天.寻找钢铁行业信息化的“最佳实践”[J].信息化建设,2006,9：56-56.
[16]Lasschuit W, Thijssen N. Supporting supply chain planning and scheduling decisions in the oil and chemical industry [J]. Computers and Chemical Engineering,2004,28(6-7):863-870.
[17]Roshan S G, Viswanadham N. Strategic sourcing and collaborative clanning in internet-enabled supply chain networks producing multigeneration products[J]. IEEE Transactions on Automation Science and Engineering,2005,2(1):54-66.
[18]林惠萍,范玉顺,吴澄.基于分层调度模型的生产计划和调度集成研究[J].计算机集成制造系统一CIMS,2002,8(8)：602-607.
[19]罗春鹏.炼油企业生产调度研究[D].浙江杭州：浙江大学信息科学与工程学院,2008.12.
[20]Jia Z, Ierapetritou M. Efficient short-term scheduling of refinery operations based on a continuous time formulation[J]. Computers and Chemical Engineering,2004,28:1001-1019.
[21]Rejowski R, Pinto J M. Scheduling of a Multiproduct Pipeline System[J]. Computers and Chemical Engineering,2003,27:1229-1246.
[22]Shan N. Mathematical programming techniques for crude oil scheduling [J]. Computers and Chemical Engineering,1996,20:1227-1232.
[23]Lee H, Pinto J M, Grossmamn I E, Park S.Mixed-Integer programming model for refinery short term scheduling of crude oil unloading with inventory management[J]. Industrial and Engineering Chemistry Research,1996,35:1630-1641.
[24]Reddy P C P, Karimi I A, Srinivasan R. A new continuous-time formulation for scheduling crude oil operations[J]. Chemical Engineering Science,2004,59:1325-1341.
[25]Jia Z, Ierapetritou M. Refinery Short-Term Scheduling Using Continuous Time Formulation:Crude-Oil Operations[J]. Industrial Engineering and Chemistry Research,2003,42(13):3085-3097.
[26]Kelly J D, Mann J L. Crude oil blend scheduling optimization:An application with multi-million dollar benefits:Part I [J]. Hydrocarbon Processing,2003,47-53.
[27]Kelly J D, Mann J L. Crude oil blend scheduling optimization:An application with multi-million dollar benefits:Part II[J]. Hydrocarbon Processing,2003,72-79.
[28]Moro, Lincoln F L, Pinto, J M. Mixed-Integer Programming Approach for Short-Term Crude Oil Scheduling [J]. Industrial and Engineering Chemistry Research,2004,43(1):85-94.
[29]Dahal K P, Aldridge C J, Mcdonald J R, Burt G M. A GA-based technique for the scheduling of storage tanks[J]. Proceeding of Congress of Evolutionary Computation,1999:2199-2206.
[30]Paolucci M, Sacile R, Boccalatte A. A llocating crude oil supply to port and refinery tanks:a simulation-based decision support system[J]. Decision Support System,2002,33(1):39-54.
[31]龙伟灿,许明春,炼油厂原油罐调度系统的研究开发[J].石油炼制与化工,2000,31(11)：38-41.
[32]Adhitya A, Srinivasan R, Karini I A. Heuristic rescheduling of crude oil operationsto manage abnormal supply chain events[J]. AICHE Journal, 2007,53:397-422.
[33]Paolucci M, Sacile R, Boccalatte A. Allocating crude oil supply to port and refinery tanks:a simulation-based decision support system[J]. Decision Support System,2002,33(1):39-54.
[34]王志勇,姚建初.一个面向原油储运调度的多AGENT应用系统实现[J].计算机工程与应用,2001,22：132-135.
[35]Rejowski J R, Pinto J M. An MILP formulation for the scheduling of multiproduct pipeline systems [J]. Brazilian Journal of Chemical Engineering,2002,19(4):467-474.
[36]Glismann K, Gruhnt G. Short-term scheduling and recipe optimization of lending processes[J]. Computersand Chemical Engineering,2001,25: 627-634.
[37]Jia Z, Ierapetritou, M. Mixed-integer linear programming for gasoline blending and distribution scheduling [J]. Industrial Engineering and Chemistry Research,2003,42(4):825-835.
[38]蒋峥,戴连奎,吴铁军.区间非线性规划问题的确定化描述及其递阶求解[J].系统工程理论与实践,2005,25(1)：110-116.
[39]宋洁蔚,荣冈.成品油配送中时间窗的确定及运输的安排[J].系统工程理论与实践,2003,23(4)：63-69.
[40]Shobrys D E, White D C. Planning, scheduling and control system:Why cannot they work together [J]. Computers and Chemical Engineering, 2002,26:149-160.
[41]Xiong G, Nyberg T R. Push/pull production plan and schedule used in modern refinery CIMS[J]. Robotics and Computer Integrated Manufacturing,2000,16 (6):397-410.
[42]Pinto J M, Joly M and Moro L F L. Planning and Scheduling Models for Refinery Operations[J]. Computers and Chemical Engineering,2000,24: 2259-2276.
[43]Wenkai L, Chi W H. Scheduling crude oil unloading, storage, and processing [J]. Industrial and Engineering Chemistry Research,2002,41: 6723-6734.
[44]Gothe M, Lundgren J T. An optimization model forrefinery production scheduling [J]. International Journal of Production Economics,2002,78: 255-270.
[45]Mendez C A, Grossmann I E, Harjunkoski I, Kabore P, A simultaneous optimization approach for off-line blending and schedul ing of oil-refinery operations[J]. Computers and Chemical Engineering,2006, 30:614-634.
[46]Perssony J A, Gothe M, Lundgren J T. A tabu search heuristic for scheduling the production processes at an oil refinery [J]. International Journal of Production Economics,2004,42:445-471.
[47]苏志同,蒲红斌,王恩波.炼油厂生产调度计划模型的研究[J].北方工业大学学报,1999,11(1)：1-4.
[48]Zhang N, Zhu X X. A novel modelling and decomposition strategy for overall refinery optimization[J]. Computers and Chemical Engineering, 2000,24:1543-1548.
[49]Slaback, Dale D. A surrogate model approach to refinery-wide optimization [D]. Texas Tech University,2004.
[50]Collinot A, Pape C L, Pinoteau G. SONIA:A Knowledge-based scheduling system [J]. International journal for artificial intelligence in engineering,1988,3(2):86-94.
[51]Bensana E, Dubois D. OPAL:A multi knowledge-based system for industrial job-shop scheduling [J]. International Journal of Production Research,1988,26(5):795-819.
[52]王景芳,邬书跃,郭武.生产调度决策优化系统Orion[J].石油化工自动化,2006,3：4-7.
[53]Kondili E, Pantelides G C, Sargent R W H. A general algorithm for short-term scheduling of batch operation 0-1 MILP formulation[J]. Computers and Chemical Engineering,1993,17(2):211-227.
[54]Pantelides C C. Unified Frameworks for the Optimal Process Planning and Scheduling[C]. Proceedings on the Second Conference on Foundations of Computer Aided Operations,1994:253-274.
[55]Crooks C. Synthesis of operating procedures for chemical plants[D]. University of London. Ph. D Dissertation,1992.
[56]Mendez C A, Henning G P, Cerda J. Optimal scheduling of batch plants satisfying multiple product orders with different due-dates[J]. Computers and Chemical Engineering,2000,24:2223-2245.
[57]Lundgren M G, Lundgren J T, Persson J A. An optimization model for refinery production scheduling[J]. International Journal of Production Economics,2002,78:255-270.
[58]Zhao X Q, Rong G. Blending scheduling under uncertainty based on particles warm optimization[J]. Chinese Journal of Chemical Engineering, 2005,13(4):535-541.
[59]Harjunkoski I, Grossmann I E. A decomposition approach for the scheduling of a steel plant production[J]. Computers and Chemical Engineering,2001,25:1647-1660.
[60]Lin X X, Janak S L, Floudas C A. A new robust optimization approach for scheduling under uncertainty:I. Bounded uncertainty [J]. Computers and Chemical Engineering 2004,28(7):1069-1085.
[61]李歧强.生产过程的智能决策与调度[D].浙江杭州：浙江大学控制系,1998,6.
[62]Jia Z, Ierapetritou M G. Short-term scheduling under uncertainty using MILP sensitivity analysis[J]. Industrial and Engineering Chemistry Research,2004,43:3782-3795.
[63]徐震浩,顾幸生.不确定条件下的中间存储时间有限的FlowShop提前／拖期调度问题[J].控制理论与应用,2006,23(3)：480-486.
[64]丁然.不确定条件下鲁棒性生产调度的研究[D].山东济南：山东大学 控制学院,2006,12.
[65]方剑,席裕庚.基于遗传算法的滚动调度策略[J].控制理论与应用,1997,14(4)：1-8.
[66]Jehoon S, Park M H, Park S W, Grossmann, I E. A rolling horizon approach with aggregation for supply chain optimization of refinery industry[C].2005 AIChE Spring National Meeting, Conference Proceedings, Atlanta, GA, United States,2005,1311-1321.
[67]王冰,席裕庚,谷寒雨.一类单机动态调度问题的改进滚动时域方法[J].控制与决策,2005,20(3)：257-265.
[68]Abuaizar R J, Svestka J A. Rescheduling job shops under random disruptions[J]. International Journal of Production Research,1997, 35(7):2065-2082.
[69]李建祥,唐立新,吴会江,庞哈利.基于规则的热轧钢管调度[J].钢铁,2004,39(9)：39-42
[70]Huercio A, Espuna A, Puigjaner L. Incorporating on-line scheduling strategies in integrated batch production control[J]. Computers and Chemical Engineering,1995,19(Suppl), S609-S615.
[71]Sanmarti E, Huercio A, Espuna A, Puigjaner L. A combined scheduling-reactive scheduling strategy to minimize the effect of process operations uncertainty in batch plants[J]. Computers and Chemical Engineering,1996,20(Suppl):s1263-s1268.
[72]Minseok K, Lee I B. Rule-based reactive rescheduling system for Multi-purpose batch processes[J]. Computers and Chemical Engineering, 1997,21(1):197-202.
[73]Sadeh N, Fox M S. Variable and value ordering heuristics for the job shop scheduling constraint satisfaction problem[J]. Artificial Intelligence, 1996,86:1-41.
[74]Tadei R, Trubian M, Avendano J L. Aggregate planning and scheduling in the food industry:a case study [J]. European Journal of Operation Research,1995,87(3):564-573.
[75]Chern C C, Liu Y L. Family-based scheduling rules of a sequence dependent wafer fabrication system[J]. IEEE Transactions on Semiconductor Manufacturing,2003,16(1):15-24.
[76]Sun R L, Ding H, Xiong Y L, Du R S. Iterative learning scheduling:A combination of optimization and dispatching rules[J]. Journal of Manufacturing Technology Management,2004,15(3):298-305.
[77]高红,熊光楞.决策规则在仿真调度中的应用[J].控制与决策.1995,10(2)：114-118.
[78]李霄峰,徐立云,邵惠鹤.炼钢连铸系统的动态调度模型和启发式调度算法[J].上海交通大学学报,2001,35(11)：1658-1662
[79]王波兴,姚建初,肖人彬.炼油生产调度的混合Petri网建模[J].计算机工程与应用,2003,39(32)：204-205.
[80]张劲松.基于受控混杂Petri网的连续过程生产调度建模及优化方法[D].山东济南：山东大学控制学院,2008.6.
[81]常春光,汪定伟,胡琨元,郑秉霖,李慧莹.基于两级CBR的钢铁企业动态调度系统研究[J].控制与决策,2004,19(10)：1109-1116.
[82]Paolucci M, Sacile R, Boccalatte A. Allocating crude oil supply to port and refinery tanks:a simulation-based decision support system[J]. Decision Support System,2002,33(1):39-54.
[83]Artiba A, Riane F. An application of a planning and scheduling multi-model approach in the chemical industry[J]. Computer in Industry, 1998,36:209-229.
[84]Pfeiffer A, Kadar B, Monostor L. Stability-oriented evaluation of rescheduling strategies, by using simulation [J]. Computers in Industry, 2007,58:630-640.
[85]赵小强.炼油企业生产调度问题研究[D].浙江杭州：浙江大学信息科学与工程学院.2005：77-89.
[86]姚建初,周济,刘伯龙,蒋白桦,郭武.炼油生产调度专家系统的应用研究[J].计算机工程与应用,2000,36(10)：174-176.
[87]苏华,伍乃骐,白丽平,刘志军.炼油短期生产计划辅助决策支持系统[J].工业工程,2005,4：70-73.
[88]庞新富,俞胜平,刘炜,郑秉霖,柴天佑.炼钢连铸动态智能调度系统的研究与开发[J].控制工程,2005,12(6)：553-556.
[89]王军.炼油企业Aspen Orion系统开发与应用[J].石油化工自动化,2004,5：69-73.
[90]王凌,郑大钟.基于遗传算法的Job Shop调度研究进展.控制与决策[J].2001,16(11)：641-646.
[91]江盛树,杨春节,李平.基于遗传算法的造纸排产优化系统设计与实现[J].化工自动化及仪表,2003,30(5)：22-24.
[92]Santos A, Dourado A. Global optimization of energy and production in process industries:a genentic algorithm application[J]. Control Engineering Practice,1999,7(4):549-554.
[93]Kim M, Jung J H, Lee I B. Intelligent scheduling and monitoring for multi-product networked batch process[J]. Computers and Chemical Engineering,1996,20:1149-1154.
[94]李艳君,吴铁军.用于柔性流程工业生产调度的并行多目标遗传算法[J].系统工程理论与实践,2001,(6)：7-12.
[95]Shaw K J, Lee P L, Nott H P, Thompson M. Genetic algorithms for multiobjective scheduling of combined batch/continuous process plants[C]. Proceedings of the Evolutionary Computation,2000,7: 293-300.
[96]Ahneida M R, Hamacher S, Pacheco M A C, Vellasco M B R. The energy minimization method:a multiobjective fitness evolution technique and its application to the production scheduling in a petroleum refinery[C]. Proceedings of the Evolutionary Computation,2001,5:560-567.
[97]Dahal K P, Aldridge C J, McDonald J R, Burt G M. A GA-based technique for the scheduling of storage tanks[C]. Proceeding of Congress of Evolutionary Computation,1999:2199-2206.
[98]Chiang T C, Huang A C, Fu L C. Modeling, scheduling, and performance evaluation for wafer fabrication:A queueing colored petri-net and GA-based approach[J]. IEEE Transactions on Automation Science and Engineering,2006,3(3):330-337.
[99]Sadrieh S A, Ghaeli M, Bahri P A, Lee P L. An integrated Petri net and GA based approach for scheduling of hybrid plants[J]. Computers in Industry,2007,58(6):519-530.
[100]Chien C F, Chen C H. Using genetic algorithms(GA) and a coloured timed Petri net(CTPN) for modelling the optimization-based schedule generator of a generic production scheduling system[J]. International Journal of Production Research,2007,45(8):1763-1789.
[101]周永华,毛宗源.求解约束优化问题的分组比较遗传算法[J].华南理工大学学报(自然科学版),2003,31(2)：38-43.
[102]Kennedy J, Eberhart R C. Particle Swarm Optimization[C]. IEEE International Conference on Neural Networks, Piscataway,1995: 1942-1948.
[103]Lee K Y, El-Sharkawi M A. Modern Heuristic Optimization Techniques With Applications to Power Systems [M]. IEEE Power Engineering Society,2002,45-51.
[104]Jerald J, Asokan P, Prabaharan G, Saravanan R. Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm[J]. International Journal of Advanced Manufacturing Technology,2005,25(9-10):964-971.
[105]Carlisle A, Dozier G. Adapting particle swarm optimization to dynamic environments[C]. Proceedings of International Conference on Artificial Intelligence, Las Vegas, Nevada, USA,2000:429-434.
[106]Carlisle A, Dozier G. Tracking changing extrema with particle swarm optimizer[R].Technical Report CSSE01-08, Auburn University,2001.
[107]Parsopoulos K E, Vrahatis M N. Particle swarm optimization method in multiobjective problems[C]. Proceedings of the SAC,2002:579-599.
[108]Shi Y H, Eberhart R C.A modified particle swarm optimizer[C]. IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, May,1998:69-73.
[109]Zheng Y L, Ma L H, Zhang L Y, et al. Empirical study of particle swarm optimizer with an increasing inertia weight[C]. Proceedings of 2003 Congress on Evolutionary Computation, Canberra Australia,2003: 221-226.
[110]Zheng Y L, Ma L H, Zhang L Y, et al. On the convergence analysis and parameter selection in particle swarm optimization[C]. Proceedings of the second international conference of machine learning and cybernetics, Xi'an,2003:1802-1807.
[111]Shi Y H, Eberhart R C. Fuzzy adaptive particle swarm optimization[C]. Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, Korea,2001:101-106.
[112]Eberhart R C, Shi Y H. Tracking and optimizing dynamic systems with particle swarms[C]. Proceedings of 2001 Congress on Evolutionary Computation, Seoul, Korea,2001:94-100.
[113]Lovbjerg M, Rasmussen T K, Krink T. Hybrid particle swarm optimiser with breeding and subpopulations[C]. Proceedings of the Genetic and Evolutionary Computation Conference,2001:147-153.
[114]Suganthan P N. Particle swarm optimiser with neighborhood operator[C]. Proceedings of 1999 Congress on Evolutionary Computation, Washington, D.C. USA,1999:1958-1961.
[115]Kennedy J. Small worlds and mega-minds:effects of neighborhood topology on particle swarm performance[C]. Proceedings of 1999 Congress on Evolutionary Computation, Washington, D.C. USA,1999: 1931-1938.
[116]Valle Y, Venayagamoorthy G, Mohagheghi S, Hernandez J, Harley G R. Particle swarm optimization:basic concepts, variants and applications in power systems[J]. IEEE Transactions on Evolutionary Computation. 2008,4(12):171-195.
[117]Eberhart R C, Shi Y H. Particle swarm optimization:development, applications and resources[C]. Proceedings of 2001 Congress on Evolutionary Computation, Seoul, Korea,2001:81-86.
[118]Frans van den Bergh, Andries P Engelbrecht. A cooperative approach to particle swarm optimization[C]. Proceedings of 2004 IEEE Transactions on evolutionary Computation,2004,8(3):225-239.
[119]Kennedy J, Eberhart R C.A discrete binary version of the particle swarm algorithm[C]. Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics,1997:4104-4109.
[120]Janson S, Middendorf M. A hierarchical particle swarm optimizer[C]. Proceedings of 2003 Congress on Evolutionary Computation, Canberra Australia,2003:770-776.
[121]Gary G Y, Lu H M. Dynamic population assisted particle swarm optimization[C]. Proceedings of the 2003 IEEE International Symposium on Intelligent Control, Houston, Texas,2003:697-702.
[122]Cui Z H, Zeng J C, Cai X J. A new stochastic particle swarm optimizer[C]. Proceedings of 2004 Congress on Evolutionary Computation, Oregon, Portland,2004:316-319.
[123]Parsopoulos K E, Vrahatis M N. Particle swarm optimization method in multiobjective problems[C]. Proceedings of the 2002 ACM Symposium on Applied Computing,2002:603-607.
[124]Fogel L J. On the organization of intellect[D]. Doctoral Dissertation, UCLA,1964.
[125]Fogel L J, Owens A J, Walsh M J. Artificial intelligence through simulated evolution[M]. New York:John Wiley,1966.
[126]石立宝,徐国禹.自适应进化规划及其在多目标最优潮流中的应用 (I)-自适应进化规划算法[J].电力系统自动化,2000,24(7)：23-25.
[127]张纪会,徐新和.一种新的进化规划算法及其应用研究[J].系统工程与电子技术,2000,22(9)：80-83.
[128]高永超,李歧强.退火进化规划算法及其收敛性[J].系统仿真学报,2006,18(3)：577-580.
[129]Bybee K. Promoting Real-Time Optimization of Hydrocarbon-Producing Systems [J]. Journal of Petroleum Technology,2003, 55(12):34-35.
[130]Ancheyta J, Centeno G, Trejo F, et al. Changes in asphaltene properties during hydrotreating of heavy crudes [J]. Energy & Fuels,2003,17(5): 1233-1238.
[131]Jara J A, Garea A, Irabien A. Simulation and optimization of the production process of phthalic anhydride using Aspen Plus [C].16th International Congress of Chemical and Process Engineering. (2004): 5019-5025.
[132]Li M J. Increasing propene yield in the propene rectification tower by using PRO/II software [J]. Petroleum Refinery Engineering,1996,26(1): 61-66.
[133]Kadma J V, Schlegel M, Marquardt W, et al. A Two-level strategy of intergrated dynamic optimization and control of industrial Poreesses-a case study[C]. European Symposium on Computer Aided Process Engineering (ESCAPE-12), Hague, Netherlands,2002:511-516.
[134]赵晓光,何小荣,陈丙珍.神经元网络用于建立油品质量模型的研究.石油炼制,1993,24(9)：9-14.
[135]Sneesby M G. Two-point Control of a Reactive Distillation Column for Composition and Conversion[J]. Journal of Process Control,1999,9(1): 19～31.
[136]任杰,翁惠新,刘馥英.渣油催化裂化反应集总动力学模型的研究[J],石油学报(石油加工),1994,10(4)：1-8.
[137]张结喜,齐艳华,邱建章.催化裂化关联模型的研究[J].计算机与应用化学,2007,24(11)：1519-1522.
[138]Abrahan J N, Rao K S. Integration of generative and evaluative models for production scheduling of lube oil plants in a petroleum refinery[J]. Applied Mathematical Modelling,2009,33:1213-1227.
[139]Rola A, Abou J, Yash P G.Constrained multivariable control of fluidized catalytic cracking process using linear programming[J]. Institution of Chemical Engineers,2001,79(4):274-282.
[140]Monfared M A S, Yang J B. Design of integrated manufacturing planning, scheduling and control systems:A new framework for automation[J]. International Journal of Advanced Manufacturing Technology,2007,33(5):545-559.
[141]Blanding F H. Reaction rates in catalytic cracking of petroleum. Industry Engineering Chemical.1953,45(6):1186-1197.
[142]Weekman J V W. Model of catalytic cracking conversion in fixed, moving, and fluid bed reactor [J]. Industry Engineering Chemical,1968, 7(1):90-95.
[143]王建平,许先琨,翁慧新等.加氢渣油催化裂化七集总动力学模型的建立[J].石油学报(石油加工),2006,22(4)：44-48.
[144]郭文忠,陈国龙.求解TSP问题的模糊自适应粒子群算法[J].计算机科学,2006,33(6)：161-185.
[145]邹桂娟,熊玉萍,马庆生.应用ASPEN PLUS建立常减压装置的模拟系统[J].炼油与化工,2002,15(2)：32-34.
[146]Johnson S M. Optimal two-and three-stage production schedules with setup times included[J]. Naval Research Logistics Quarterly,2006, 1(1):61-68.
[147]Panwalker S S, Iskander Wafik. A survey of scheduling rules[J]. Operations Research,1977,25(1):45-61.
[148]胡培祥.面向问题的知识表示支持系统研究[J].哈尔滨工业大学学报. 1999,31(1)：1-5.
[149]Quinlan J R, C4.5:Programs for Machine Learning[M]. Morgan Kaufmann Publishers Inc. San Francisco, CA USA,1992.
[150]Grzymala J W, Chien P, Wang B. Classificationand rule induction based on rough sets[J]. IEEE International Conference on Fuzzy Systems, 1996,2:744-747.
[151]Grzymala J W. LERS-A system for learning from examples based on rough sets[J]. Intelligent Decision Support,1992:3-18.
[152]Khoo L P. A Rough-Set-Based Approach for Classification and Rule Induction[J]. International Journal of Advanced Manufacturing Technology,1999,15(6):438-444.
[153]Pawlak Z.Rough sets[J]. International Journal of Computer and Information Sciences,1982,11:341-356.
[154]张文修,吴伟志.粗糙集理论介绍和研究综述[J].模糊系统与数学,2000,14(4)：2-12.
[155]Ziarko W. Variable precision rough set model [J]. Journal of Computer and System Sciences,1993,46:39-59.
[156]王国胤.Rough集理论与知识获取[M].西安：西安交通大学出版社,2003.
[157]Kryszkiewicz M, Rybinski H. Reducing information systems with uncertain attributes[C]. The 9th International Symposium on Foundations of Intelligent Systems, Zakopane, Poland, June (9-13),1996.285-294.
[158]于景元.系统科学与系统工程的发展[J].复杂系统与复杂性科学,2004.7,1(3)：4-9.
[159]Komorowski O J. ROSETTA-A rough set toolkit for analysis of data[C]. Fifth International Workshop on Rough Sets and Soft Computing, Durham,1997.403-407.
[160]韩祯祥,张琦,文福拴.粗糙集理论及其应用[J].信息与控制,1998,27(1)：37-45.
[161]赵小强,李战明,刘军.炼油生产装置调度模型与算度研究[C].Proceedings of the 26th Chinese Control Conference, Zhangjiajie, China, July 26-31,2007:748-752.
[162]陈昌龄,宗学军,孙鹏,邵惠鹤.单阶段多产品批处理过程的短期调度一模型的简化[J].信息与控制,2002,31(3)：202-210.
[163]周卫东,杨加敏,贾磊,李歧强.一种Petri网结合遗传算法的优化方法及应用[J].山东大学学报(工学版),2005,30(4)：59-63.
[164]Zhuang T, LI Q Q, Guo Q Q, et al. A Two-Stage Particle Swarm Optimizer. Proceedings of the IEEE Congress on Evolutionary Computation (OEC 2008). Hong Kong, China, June 1-6,2008:557-563.
[1]Pawlak Z.Rough sets[J].International Journal of Computer and Information Sciences,1982,11:341-356.
[2]Ning Zhong,Ju-Zhen Dong,Setsuo Ohsuga.Meningitis data mining by cooperatively using GDT-RS and RSBR. Pattern Recognition Letters 24 (2003) 887-894.
[3]Salvatore Greco, Benedetto Matarazzo, Roman Slowinski.Rough sets theory for multicriteria decision analysis. European Journal of Operational Research 129 (2001)1-47.
[4]Pawlak Z, Wong S K M, Ziarko W. Rough Sets:Probabilistic versus Deterministic Approach. Int J Man-Machine Studies,1998,29:81-95.
[5]Richard Jensen and Qiang Shen, Fuzzy-rough attributesreduction with application to web categorization, Fuzzy Sets and Systems 141(2004)469-485.
[6]Tsang, G.C.Y, Chen Degang,et al. On attributes reduction with fuzzy rough sets. Systems, Man and Cybernetics, IEEE International Conference (2005):2775-2780.
[7]Jing-kai Liang, Yang Zhang, Yan-bin Qu. A heuristic algorithm of attribute reduction in rough set. Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, (2005):3139-3142.
[8]Ziarko W. Variable Precision Rough Set Model. Journal of Computer and System Sciences,1993,46:39-59.
[9]Komorowski O J,Showron A,Synak P. The ROSETTA System. In:Plokowki L, Skowron A, ed.Rough Sets in Kowlege Discovery, Physica-Verlag,19:572-576.
[10]Torres G L,Quintana VH,da Silva A P A,et al. Classification of Power System Operation Point using Rough Set Techniques. IEEE International Conference on Systems, Man and Cybernetics, Information Intelligence and Systems, 1996:1898-1903.
[1]Moro L.F.L., Process technology in the petroleum refinery industry-current situation and future trends, Computers and Chemical Engineering.27(2003) 1303-1305.
[2]Lee, H., Pinto, J. M., Grossmann, I. E.,& Park, S. (1996). Mixed-integer linear programming model for refinery short-term scheduling of crude oil unloading with inventory management. Industrial and Engineering Chemistry Research,35, 1630-1641.
[3]Pinto J.M., Joly M., Moro L.F.L., Planning and scheduling models for refinery operations, Computers and Chemical Engineering.24 (2000) 2259-2276.
[4]Jia, Z.; Ierapetritou, M. Efficient Short-Term Scheduling of Refinery Operations Based on a Continuous Time Formulation. Comput. Chem. Eng.2004,28, 1001-1019.
[5]Jia, Z.; Ierapetritou, M. Mixed-Integer Linear Programming Model for Gasoline Blending and Distribution Scheduling. Ind. Eng. Chem. Res.2003,42,825-835.
[6]Xiong G., Nyberg T.R., Push/pull production plan and schedule used in modern refinery CIMS, Robotics Comput. Integrat.Manufact.16 (2000) 397-410.
[7]Alle A., Pinto J.M., Mixed-integer programming models for the scheduling and operational optimization of multi-product continuous plants, Industrial and Engineering Chemistry Research,41 (2002) 2689-2704.
[8]Chryssolouris G., Papakostas N., Mourtzis D., Refinery short-term scheduling with tank farm, inventory and distillation management:an integrated simulation-based approach, Eur. J. Oper. Res.166 (2005) 812-827.
[9]Moro L.F.L., Pinto J.M., Mixed-integer programming approach for short-term crude oil scheduling, Ind. Eng. Chem. Res.43 (2004) 85-94.
[10]Susana Relvas, Henrique A. Matos, Ana Paula F. D. Barbosa-Povoa, Joao Fialho, Antonio S. Pinheiro. Pipeline Scheduling and Inventory Management of a Multiproduct Distribution Oil System. Industrial and Engineering Chemistry Research,2006,45,7841-7855.
[11]Carlos A. Mendez, Ignacio E. Grossmann, Iiro Harjunkoski, Pousga Kabor. A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations. Computers and Chemical Engineering.2006,30,614-665.
[12]Magatao, L., Arruda, L. V. R., Neves, F. J. Using CLP and MILP for scheduling commodities in a pipeline. In Proceedings of the 15th European Symposium on Computer-Aided Process Engineering (ESCAPE15), Barcelona, Spain, May 29-June 1,2005; Computer Aided Chemical Engineering, Vol.20; Puigjaner, L., Espuna, A., Eds.; Elsevier:Amsterdam, New York,2005; pp 1027-1032.
[13]Furman, K. C., Jia, Z.,& Ierapetritou, M. G. (2007). A robust event-based continuous time formulation for tank transfer scheduling. Industrial and Engineering Chemistry Research,46, (2007),9126-9136.
[14]Guo Q. Q, Li Q.Q., Ding R. Zhang J.S. Wang X.H.. Dynamic Scheduling of Oil Refinery based on Model-optimization and Process-configuration. ICAL 2007, Jinan, China.2007.8:1117-1121.
[2]李永康.关于管控一体化的中央控制室设计方突的探讨[J].石油化工自动化,1999,(4)：8-10.
[3]侯凯锋,叶向东.大型化炼油厂的自动化和信息集成[J].石油化工自动化,2002,(5)：1-4.
[4]王树青.先进控制技术及应用[M].北京：化学工业出版社,2002.
[5]Nystrom R H, Harjunkoski I, Kroll A. Production optimization for continuously operated processes with optimal operation and scheduling of multiple units[J]. Computers and Chemical Engineering,2006,30: 392-406.
[6]Sebastian T M, Antonio F T, Grossmann I E. Simultaneous cyclic scheduling and optimal control of polymerization reactors[J]. American Institute of Chemical Engineers,2007,53(9):2300-2315.
[7]Sebastian T M, Antonio F T, Grossmann I E. Lagrangean heuristic for the scheduling and control of polymerization reactors[J]. American Institute of Chemical Engineers,2008,54(1):162-182.
[8]Chatzidoukas C, Pistikopoulos S, Kiparissides C. A hierarchical optimization approach to optimal production scheduling in an industrial continuous olefin polymerization reactor[J]. Macromolecular Reaction Engineering,2009,3:36-46.
[9]Abraham J N, Rao K S. Integration of generative and evaluative models for production scheduling of lube oil plants in a petroleum refinery [J]. Applied Mathematical Modelling,2009,33:1213-1227.
[10]Ramage M P, etc. Development of mobil's kinetic reforming model[J]. Chemical Engineering Science,1980,35(1):41-48.
[11]王建平,许先焜,翁惠新等.渣油催化裂化14集总动力学模型的建立. 化工学报[J].2007,58(1)：86-94.
[12]Abraham J N, Rao K S. Integration of generative and evaluative models for production scheduling of lube oil plants in a petroleum refinery[J]. Applied Mathematical Modelling,2009,33(3):1213-1227.
[13]Bonner & Moore. RPMS (Refinery and Petrochemical Modeling System): a system descriptive [M]. Houston, TX, USA:Bonner & Moore Management Science,1979.
[14]PIMS system reference, version 11.0 [M]. Cambridge, MA, USA:Aspen Technology Inc.1999.
[15]吴倚天.寻找钢铁行业信息化的“最佳实践”[J].信息化建设,2006,9：56-56.
[16]Lasschuit W, Thijssen N. Supporting supply chain planning and scheduling decisions in the oil and chemical industry [J]. Computers and Chemical Engineering,2004,28(6-7):863-870.
[17]Roshan S G, Viswanadham N. Strategic sourcing and collaborative clanning in internet-enabled supply chain networks producing multigeneration products[J]. IEEE Transactions on Automation Science and Engineering,2005,2(1):54-66.
[18]林惠萍,范玉顺,吴澄.基于分层调度模型的生产计划和调度集成研究[J].计算机集成制造系统一CIMS,2002,8(8)：602-607.
[19]罗春鹏.炼油企业生产调度研究[D].浙江杭州：浙江大学信息科学与工程学院,2008.12.
[20]Jia Z, Ierapetritou M. Efficient short-term scheduling of refinery operations based on a continuous time formulation[J]. Computers and Chemical Engineering,2004,28:1001-1019.
[21]Rejowski R, Pinto J M. Scheduling of a Multiproduct Pipeline System[J]. Computers and Chemical Engineering,2003,27:1229-1246.
[22]Shan N. Mathematical programming techniques for crude oil scheduling [J]. Computers and Chemical Engineering,1996,20:1227-1232.
[23]Lee H, Pinto J M, Grossmamn I E, Park S.Mixed-Integer programming model for refinery short term scheduling of crude oil unloading with inventory management[J]. Industrial and Engineering Chemistry Research,1996,35:1630-1641.
[24]Reddy P C P, Karimi I A, Srinivasan R. A new continuous-time formulation for scheduling crude oil operations[J]. Chemical Engineering Science,2004,59:1325-1341.
[25]Jia Z, Ierapetritou M. Refinery Short-Term Scheduling Using Continuous Time Formulation:Crude-Oil Operations[J]. Industrial Engineering and Chemistry Research,2003,42(13):3085-3097.
[26]Kelly J D, Mann J L. Crude oil blend scheduling optimization:An application with multi-million dollar benefits:Part I [J]. Hydrocarbon Processing,2003,47-53.
[27]Kelly J D, Mann J L. Crude oil blend scheduling optimization:An application with multi-million dollar benefits:Part II[J]. Hydrocarbon Processing,2003,72-79.
[28]Moro, Lincoln F L, Pinto, J M. Mixed-Integer Programming Approach for Short-Term Crude Oil Scheduling [J]. Industrial and Engineering Chemistry Research,2004,43(1):85-94.
[29]Dahal K P, Aldridge C J, Mcdonald J R, Burt G M. A GA-based technique for the scheduling of storage tanks[J]. Proceeding of Congress of Evolutionary Computation,1999:2199-2206.
[30]Paolucci M, Sacile R, Boccalatte A. A llocating crude oil supply to port and refinery tanks:a simulation-based decision support system[J]. Decision Support System,2002,33(1):39-54.
[31]龙伟灿,许明春,炼油厂原油罐调度系统的研究开发[J].石油炼制与化工,2000,31(11)：38-41.
[32]Adhitya A, Srinivasan R, Karini I A. Heuristic rescheduling of crude oil operationsto manage abnormal supply chain events[J]. AICHE Journal, 2007,53:397-422.
[33]Paolucci M, Sacile R, Boccalatte A. Allocating crude oil supply to port and refinery tanks:a simulation-based decision support system[J]. Decision Support System,2002,33(1):39-54.
[34]王志勇,姚建初.一个面向原油储运调度的多AGENT应用系统实现[J].计算机工程与应用,2001,22：132-135.
[35]Rejowski J R, Pinto J M. An MILP formulation for the scheduling of multiproduct pipeline systems [J]. Brazilian Journal of Chemical Engineering,2002,19(4):467-474.
[36]Glismann K, Gruhnt G. Short-term scheduling and recipe optimization of lending processes[J]. Computersand Chemical Engineering,2001,25: 627-634.
[37]Jia Z, Ierapetritou, M. Mixed-integer linear programming for gasoline blending and distribution scheduling [J]. Industrial Engineering and Chemistry Research,2003,42(4):825-835.
[38]蒋峥,戴连奎,吴铁军.区间非线性规划问题的确定化描述及其递阶求解[J].系统工程理论与实践,2005,25(1)：110-116.
[39]宋洁蔚,荣冈.成品油配送中时间窗的确定及运输的安排[J].系统工程理论与实践,2003,23(4)：63-69.
[40]Shobrys D E, White D C. Planning, scheduling and control system:Why cannot they work together [J]. Computers and Chemical Engineering, 2002,26:149-160.
[41]Xiong G, Nyberg T R. Push/pull production plan and schedule used in modern refinery CIMS[J]. Robotics and Computer Integrated Manufacturing,2000,16 (6):397-410.
[42]Pinto J M, Joly M and Moro L F L. Planning and Scheduling Models for Refinery Operations[J]. Computers and Chemical Engineering,2000,24: 2259-2276.
[43]Wenkai L, Chi W H. Scheduling crude oil unloading, storage, and processing [J]. Industrial and Engineering Chemistry Research,2002,41: 6723-6734.
[44]Gothe M, Lundgren J T. An optimization model forrefinery production scheduling [J]. International Journal of Production Economics,2002,78: 255-270.
[45]Mendez C A, Grossmann I E, Harjunkoski I, Kabore P, A simultaneous optimization approach for off-line blending and schedul ing of oil-refinery operations[J]. Computers and Chemical Engineering,2006, 30:614-634.
[46]Perssony J A, Gothe M, Lundgren J T. A tabu search heuristic for scheduling the production processes at an oil refinery [J]. International Journal of Production Economics,2004,42:445-471.
[47]苏志同,蒲红斌,王恩波.炼油厂生产调度计划模型的研究[J].北方工业大学学报,1999,11(1)：1-4.
[48]Zhang N, Zhu X X. A novel modelling and decomposition strategy for overall refinery optimization[J]. Computers and Chemical Engineering, 2000,24:1543-1548.
[49]Slaback, Dale D. A surrogate model approach to refinery-wide optimization [D]. Texas Tech University,2004.
[50]Collinot A, Pape C L, Pinoteau G. SONIA:A Knowledge-based scheduling system [J]. International journal for artificial intelligence in engineering,1988,3(2):86-94.
[51]Bensana E, Dubois D. OPAL:A multi knowledge-based system for industrial job-shop scheduling [J]. International Journal of Production Research,1988,26(5):795-819.
[52]王景芳,邬书跃,郭武.生产调度决策优化系统Orion[J].石油化工自动化,2006,3：4-7.
[53]Kondili E, Pantelides G C, Sargent R W H. A general algorithm for short-term scheduling of batch operation 0-1 MILP formulation[J]. Computers and Chemical Engineering,1993,17(2):211-227.
[54]Pantelides C C. Unified Frameworks for the Optimal Process Planning and Scheduling[C]. Proceedings on the Second Conference on Foundations of Computer Aided Operations,1994:253-274.
[55]Crooks C. Synthesis of operating procedures for chemical plants[D]. University of London. Ph. D Dissertation,1992.
[56]Mendez C A, Henning G P, Cerda J. Optimal scheduling of batch plants satisfying multiple product orders with different due-dates[J]. Computers and Chemical Engineering,2000,24:2223-2245.
[57]Lundgren M G, Lundgren J T, Persson J A. An optimization model for refinery production scheduling[J]. International Journal of Production Economics,2002,78:255-270.
[58]Zhao X Q, Rong G. Blending scheduling under uncertainty based on particles warm optimization[J]. Chinese Journal of Chemical Engineering, 2005,13(4):535-541.
[59]Harjunkoski I, Grossmann I E. A decomposition approach for the scheduling of a steel plant production[J]. Computers and Chemical Engineering,2001,25:1647-1660.
[60]Lin X X, Janak S L, Floudas C A. A new robust optimization approach for scheduling under uncertainty:I. Bounded uncertainty [J]. Computers and Chemical Engineering 2004,28(7):1069-1085.
[61]李歧强.生产过程的智能决策与调度[D].浙江杭州：浙江大学控制系,1998,6.
[62]Jia Z, Ierapetritou M G. Short-term scheduling under uncertainty using MILP sensitivity analysis[J]. Industrial and Engineering Chemistry Research,2004,43:3782-3795.
[63]徐震浩,顾幸生.不确定条件下的中间存储时间有限的FlowShop提前／拖期调度问题[J].控制理论与应用,2006,23(3)：480-486.
[64]丁然.不确定条件下鲁棒性生产调度的研究[D].山东济南：山东大学 控制学院,2006,12.
[65]方剑,席裕庚.基于遗传算法的滚动调度策略[J].控制理论与应用,1997,14(4)：1-8.
[66]Jehoon S, Park M H, Park S W, Grossmann, I E. A rolling horizon approach with aggregation for supply chain optimization of refinery industry[C].2005 AIChE Spring National Meeting, Conference Proceedings, Atlanta, GA, United States,2005,1311-1321.
[67]王冰,席裕庚,谷寒雨.一类单机动态调度问题的改进滚动时域方法[J].控制与决策,2005,20(3)：257-265.
[68]Abuaizar R J, Svestka J A. Rescheduling job shops under random disruptions[J]. International Journal of Production Research,1997, 35(7):2065-2082.
[69]李建祥,唐立新,吴会江,庞哈利.基于规则的热轧钢管调度[J].钢铁,2004,39(9)：39-42
[70]Huercio A, Espuna A, Puigjaner L. Incorporating on-line scheduling strategies in integrated batch production control[J]. Computers and Chemical Engineering,1995,19(Suppl), S609-S615.
[71]Sanmarti E, Huercio A, Espuna A, Puigjaner L. A combined scheduling-reactive scheduling strategy to minimize the effect of process operations uncertainty in batch plants[J]. Computers and Chemical Engineering,1996,20(Suppl):s1263-s1268.
[72]Minseok K, Lee I B. Rule-based reactive rescheduling system for Multi-purpose batch processes[J]. Computers and Chemical Engineering, 1997,21(1):197-202.
[73]Sadeh N, Fox M S. Variable and value ordering heuristics for the job shop scheduling constraint satisfaction problem[J]. Artificial Intelligence, 1996,86:1-41.
[74]Tadei R, Trubian M, Avendano J L. Aggregate planning and scheduling in the food industry:a case study [J]. European Journal of Operation Research,1995,87(3):564-573.
[75]Chern C C, Liu Y L. Family-based scheduling rules of a sequence dependent wafer fabrication system[J]. IEEE Transactions on Semiconductor Manufacturing,2003,16(1):15-24.
[76]Sun R L, Ding H, Xiong Y L, Du R S. Iterative learning scheduling:A combination of optimization and dispatching rules[J]. Journal of Manufacturing Technology Management,2004,15(3):298-305.
[77]高红,熊光楞.决策规则在仿真调度中的应用[J].控制与决策.1995,10(2)：114-118.
[78]李霄峰,徐立云,邵惠鹤.炼钢连铸系统的动态调度模型和启发式调度算法[J].上海交通大学学报,2001,35(11)：1658-1662
[79]王波兴,姚建初,肖人彬.炼油生产调度的混合Petri网建模[J].计算机工程与应用,2003,39(32)：204-205.
[80]张劲松.基于受控混杂Petri网的连续过程生产调度建模及优化方法[D].山东济南：山东大学控制学院,2008.6.
[81]常春光,汪定伟,胡琨元,郑秉霖,李慧莹.基于两级CBR的钢铁企业动态调度系统研究[J].控制与决策,2004,19(10)：1109-1116.
[82]Paolucci M, Sacile R, Boccalatte A. Allocating crude oil supply to port and refinery tanks:a simulation-based decision support system[J]. Decision Support System,2002,33(1):39-54.
[83]Artiba A, Riane F. An application of a planning and scheduling multi-model approach in the chemical industry[J]. Computer in Industry, 1998,36:209-229.
[84]Pfeiffer A, Kadar B, Monostor L. Stability-oriented evaluation of rescheduling strategies, by using simulation [J]. Computers in Industry, 2007,58:630-640.
[85]赵小强.炼油企业生产调度问题研究[D].浙江杭州：浙江大学信息科学与工程学院.2005：77-89.
[86]姚建初,周济,刘伯龙,蒋白桦,郭武.炼油生产调度专家系统的应用研究[J].计算机工程与应用,2000,36(10)：174-176.
[87]苏华,伍乃骐,白丽平,刘志军.炼油短期生产计划辅助决策支持系统[J].工业工程,2005,4：70-73.
[88]庞新富,俞胜平,刘炜,郑秉霖,柴天佑.炼钢连铸动态智能调度系统的研究与开发[J].控制工程,2005,12(6)：553-556.
[89]王军.炼油企业Aspen Orion系统开发与应用[J].石油化工自动化,2004,5：69-73.
[90]王凌,郑大钟.基于遗传算法的Job Shop调度研究进展.控制与决策[J].2001,16(11)：641-646.
[91]江盛树,杨春节,李平.基于遗传算法的造纸排产优化系统设计与实现[J].化工自动化及仪表,2003,30(5)：22-24.
[92]Santos A, Dourado A. Global optimization of energy and production in process industries:a genentic algorithm application[J]. Control Engineering Practice,1999,7(4):549-554.
[93]Kim M, Jung J H, Lee I B. Intelligent scheduling and monitoring for multi-product networked batch process[J]. Computers and Chemical Engineering,1996,20:1149-1154.
[94]李艳君,吴铁军.用于柔性流程工业生产调度的并行多目标遗传算法[J].系统工程理论与实践,2001,(6)：7-12.
[95]Shaw K J, Lee P L, Nott H P, Thompson M. Genetic algorithms for multiobjective scheduling of combined batch/continuous process plants[C]. Proceedings of the Evolutionary Computation,2000,7: 293-300.
[96]Ahneida M R, Hamacher S, Pacheco M A C, Vellasco M B R. The energy minimization method:a multiobjective fitness evolution technique and its application to the production scheduling in a petroleum refinery[C]. Proceedings of the Evolutionary Computation,2001,5:560-567.
[97]Dahal K P, Aldridge C J, McDonald J R, Burt G M. A GA-based technique for the scheduling of storage tanks[C]. Proceeding of Congress of Evolutionary Computation,1999:2199-2206.
[98]Chiang T C, Huang A C, Fu L C. Modeling, scheduling, and performance evaluation for wafer fabrication:A queueing colored petri-net and GA-based approach[J]. IEEE Transactions on Automation Science and Engineering,2006,3(3):330-337.
[99]Sadrieh S A, Ghaeli M, Bahri P A, Lee P L. An integrated Petri net and GA based approach for scheduling of hybrid plants[J]. Computers in Industry,2007,58(6):519-530.
[100]Chien C F, Chen C H. Using genetic algorithms(GA) and a coloured timed Petri net(CTPN) for modelling the optimization-based schedule generator of a generic production scheduling system[J]. International Journal of Production Research,2007,45(8):1763-1789.
[101]周永华,毛宗源.求解约束优化问题的分组比较遗传算法[J].华南理工大学学报(自然科学版),2003,31(2)：38-43.
[102]Kennedy J, Eberhart R C. Particle Swarm Optimization[C]. IEEE International Conference on Neural Networks, Piscataway,1995: 1942-1948.
[103]Lee K Y, El-Sharkawi M A. Modern Heuristic Optimization Techniques With Applications to Power Systems [M]. IEEE Power Engineering Society,2002,45-51.
[104]Jerald J, Asokan P, Prabaharan G, Saravanan R. Scheduling optimisation of flexible manufacturing systems using particle swarm optimisation algorithm[J]. International Journal of Advanced Manufacturing Technology,2005,25(9-10):964-971.
[105]Carlisle A, Dozier G. Adapting particle swarm optimization to dynamic environments[C]. Proceedings of International Conference on Artificial Intelligence, Las Vegas, Nevada, USA,2000:429-434.
[106]Carlisle A, Dozier G. Tracking changing extrema with particle swarm optimizer[R].Technical Report CSSE01-08, Auburn University,2001.
[107]Parsopoulos K E, Vrahatis M N. Particle swarm optimization method in multiobjective problems[C]. Proceedings of the SAC,2002:579-599.
[108]Shi Y H, Eberhart R C.A modified particle swarm optimizer[C]. IEEE International Conference on Evolutionary Computation, Anchorage, Alaska, May,1998:69-73.
[109]Zheng Y L, Ma L H, Zhang L Y, et al. Empirical study of particle swarm optimizer with an increasing inertia weight[C]. Proceedings of 2003 Congress on Evolutionary Computation, Canberra Australia,2003: 221-226.
[110]Zheng Y L, Ma L H, Zhang L Y, et al. On the convergence analysis and parameter selection in particle swarm optimization[C]. Proceedings of the second international conference of machine learning and cybernetics, Xi'an,2003:1802-1807.
[111]Shi Y H, Eberhart R C. Fuzzy adaptive particle swarm optimization[C]. Proceedings of the 2001 Congress on Evolutionary Computation, Seoul, Korea,2001:101-106.
[112]Eberhart R C, Shi Y H. Tracking and optimizing dynamic systems with particle swarms[C]. Proceedings of 2001 Congress on Evolutionary Computation, Seoul, Korea,2001:94-100.
[113]Lovbjerg M, Rasmussen T K, Krink T. Hybrid particle swarm optimiser with breeding and subpopulations[C]. Proceedings of the Genetic and Evolutionary Computation Conference,2001:147-153.
[114]Suganthan P N. Particle swarm optimiser with neighborhood operator[C]. Proceedings of 1999 Congress on Evolutionary Computation, Washington, D.C. USA,1999:1958-1961.
[115]Kennedy J. Small worlds and mega-minds:effects of neighborhood topology on particle swarm performance[C]. Proceedings of 1999 Congress on Evolutionary Computation, Washington, D.C. USA,1999: 1931-1938.
[116]Valle Y, Venayagamoorthy G, Mohagheghi S, Hernandez J, Harley G R. Particle swarm optimization:basic concepts, variants and applications in power systems[J]. IEEE Transactions on Evolutionary Computation. 2008,4(12):171-195.
[117]Eberhart R C, Shi Y H. Particle swarm optimization:development, applications and resources[C]. Proceedings of 2001 Congress on Evolutionary Computation, Seoul, Korea,2001:81-86.
[118]Frans van den Bergh, Andries P Engelbrecht. A cooperative approach to particle swarm optimization[C]. Proceedings of 2004 IEEE Transactions on evolutionary Computation,2004,8(3):225-239.
[119]Kennedy J, Eberhart R C.A discrete binary version of the particle swarm algorithm[C]. Proceedings of the World Multiconference on Systemics, Cybernetics and Informatics,1997:4104-4109.
[120]Janson S, Middendorf M. A hierarchical particle swarm optimizer[C]. Proceedings of 2003 Congress on Evolutionary Computation, Canberra Australia,2003:770-776.
[121]Gary G Y, Lu H M. Dynamic population assisted particle swarm optimization[C]. Proceedings of the 2003 IEEE International Symposium on Intelligent Control, Houston, Texas,2003:697-702.
[122]Cui Z H, Zeng J C, Cai X J. A new stochastic particle swarm optimizer[C]. Proceedings of 2004 Congress on Evolutionary Computation, Oregon, Portland,2004:316-319.
[123]Parsopoulos K E, Vrahatis M N. Particle swarm optimization method in multiobjective problems[C]. Proceedings of the 2002 ACM Symposium on Applied Computing,2002:603-607.
[124]Fogel L J. On the organization of intellect[D]. Doctoral Dissertation, UCLA,1964.
[125]Fogel L J, Owens A J, Walsh M J. Artificial intelligence through simulated evolution[M]. New York:John Wiley,1966.
[126]石立宝,徐国禹.自适应进化规划及其在多目标最优潮流中的应用 (I)-自适应进化规划算法[J].电力系统自动化,2000,24(7)：23-25.
[127]张纪会,徐新和.一种新的进化规划算法及其应用研究[J].系统工程与电子技术,2000,22(9)：80-83.
[128]高永超,李歧强.退火进化规划算法及其收敛性[J].系统仿真学报,2006,18(3)：577-580.
[129]Bybee K. Promoting Real-Time Optimization of Hydrocarbon-Producing Systems [J]. Journal of Petroleum Technology,2003, 55(12):34-35.
[130]Ancheyta J, Centeno G, Trejo F, et al. Changes in asphaltene properties during hydrotreating of heavy crudes [J]. Energy & Fuels,2003,17(5): 1233-1238.
[131]Jara J A, Garea A, Irabien A. Simulation and optimization of the production process of phthalic anhydride using Aspen Plus [C].16th International Congress of Chemical and Process Engineering. (2004): 5019-5025.
[132]Li M J. Increasing propene yield in the propene rectification tower by using PRO/II software [J]. Petroleum Refinery Engineering,1996,26(1): 61-66.
[133]Kadma J V, Schlegel M, Marquardt W, et al. A Two-level strategy of intergrated dynamic optimization and control of industrial Poreesses-a case study[C]. European Symposium on Computer Aided Process Engineering (ESCAPE-12), Hague, Netherlands,2002:511-516.
[134]赵晓光,何小荣,陈丙珍.神经元网络用于建立油品质量模型的研究.石油炼制,1993,24(9)：9-14.
[135]Sneesby M G. Two-point Control of a Reactive Distillation Column for Composition and Conversion[J]. Journal of Process Control,1999,9(1): 19～31.
[136]任杰,翁惠新,刘馥英.渣油催化裂化反应集总动力学模型的研究[J],石油学报(石油加工),1994,10(4)：1-8.
[137]张结喜,齐艳华,邱建章.催化裂化关联模型的研究[J].计算机与应用化学,2007,24(11)：1519-1522.
[138]Abrahan J N, Rao K S. Integration of generative and evaluative models for production scheduling of lube oil plants in a petroleum refinery[J]. Applied Mathematical Modelling,2009,33:1213-1227.
[139]Rola A, Abou J, Yash P G.Constrained multivariable control of fluidized catalytic cracking process using linear programming[J]. Institution of Chemical Engineers,2001,79(4):274-282.
[140]Monfared M A S, Yang J B. Design of integrated manufacturing planning, scheduling and control systems:A new framework for automation[J]. International Journal of Advanced Manufacturing Technology,2007,33(5):545-559.
[141]Blanding F H. Reaction rates in catalytic cracking of petroleum. Industry Engineering Chemical.1953,45(6):1186-1197.
[142]Weekman J V W. Model of catalytic cracking conversion in fixed, moving, and fluid bed reactor [J]. Industry Engineering Chemical,1968, 7(1):90-95.
[143]王建平,许先琨,翁慧新等.加氢渣油催化裂化七集总动力学模型的建立[J].石油学报(石油加工),2006,22(4)：44-48.
[144]郭文忠,陈国龙.求解TSP问题的模糊自适应粒子群算法[J].计算机科学,2006,33(6)：161-185.
[145]邹桂娟,熊玉萍,马庆生.应用ASPEN PLUS建立常减压装置的模拟系统[J].炼油与化工,2002,15(2)：32-34.
[146]Johnson S M. Optimal two-and three-stage production schedules with setup times included[J]. Naval Research Logistics Quarterly,2006, 1(1):61-68.
[147]Panwalker S S, Iskander Wafik. A survey of scheduling rules[J]. Operations Research,1977,25(1):45-61.
[148]胡培祥.面向问题的知识表示支持系统研究[J].哈尔滨工业大学学报. 1999,31(1)：1-5.
[149]Quinlan J R, C4.5:Programs for Machine Learning[M]. Morgan Kaufmann Publishers Inc. San Francisco, CA USA,1992.
[150]Grzymala J W, Chien P, Wang B. Classificationand rule induction based on rough sets[J]. IEEE International Conference on Fuzzy Systems, 1996,2:744-747.
[151]Grzymala J W. LERS-A system for learning from examples based on rough sets[J]. Intelligent Decision Support,1992:3-18.
[152]Khoo L P. A Rough-Set-Based Approach for Classification and Rule Induction[J]. International Journal of Advanced Manufacturing Technology,1999,15(6):438-444.
[153]Pawlak Z.Rough sets[J]. International Journal of Computer and Information Sciences,1982,11:341-356.
[154]张文修,吴伟志.粗糙集理论介绍和研究综述[J].模糊系统与数学,2000,14(4)：2-12.
[155]Ziarko W. Variable precision rough set model [J]. Journal of Computer and System Sciences,1993,46:39-59.
[156]王国胤.Rough集理论与知识获取[M].西安：西安交通大学出版社,2003.
[157]Kryszkiewicz M, Rybinski H. Reducing information systems with uncertain attributes[C]. The 9th International Symposium on Foundations of Intelligent Systems, Zakopane, Poland, June (9-13),1996.285-294.
[158]于景元.系统科学与系统工程的发展[J].复杂系统与复杂性科学,2004.7,1(3)：4-9.
[159]Komorowski O J. ROSETTA-A rough set toolkit for analysis of data[C]. Fifth International Workshop on Rough Sets and Soft Computing, Durham,1997.403-407.
[160]韩祯祥,张琦,文福拴.粗糙集理论及其应用[J].信息与控制,1998,27(1)：37-45.
[161]赵小强,李战明,刘军.炼油生产装置调度模型与算度研究[C].Proceedings of the 26th Chinese Control Conference, Zhangjiajie, China, July 26-31,2007:748-752.
[162]陈昌龄,宗学军,孙鹏,邵惠鹤.单阶段多产品批处理过程的短期调度一模型的简化[J].信息与控制,2002,31(3)：202-210.
[163]周卫东,杨加敏,贾磊,李歧强.一种Petri网结合遗传算法的优化方法及应用[J].山东大学学报(工学版),2005,30(4)：59-63.
[164]Zhuang T, LI Q Q, Guo Q Q, et al. A Two-Stage Particle Swarm Optimizer. Proceedings of the IEEE Congress on Evolutionary Computation (OEC 2008). Hong Kong, China, June 1-6,2008:557-563.
[1]Pawlak Z.Rough sets[J].International Journal of Computer and Information Sciences,1982,11:341-356.
[2]Ning Zhong,Ju-Zhen Dong,Setsuo Ohsuga.Meningitis data mining by cooperatively using GDT-RS and RSBR. Pattern Recognition Letters 24 (2003) 887-894.
[3]Salvatore Greco, Benedetto Matarazzo, Roman Slowinski.Rough sets theory for multicriteria decision analysis. European Journal of Operational Research 129 (2001)1-47.
[4]Pawlak Z, Wong S K M, Ziarko W. Rough Sets:Probabilistic versus Deterministic Approach. Int J Man-Machine Studies,1998,29:81-95.
[5]Richard Jensen and Qiang Shen, Fuzzy-rough attributesreduction with application to web categorization, Fuzzy Sets and Systems 141(2004)469-485.
[6]Tsang, G.C.Y, Chen Degang,et al. On attributes reduction with fuzzy rough sets. Systems, Man and Cybernetics, IEEE International Conference (2005):2775-2780.
[7]Jing-kai Liang, Yang Zhang, Yan-bin Qu. A heuristic algorithm of attribute reduction in rough set. Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, (2005):3139-3142.
[8]Ziarko W. Variable Precision Rough Set Model. Journal of Computer and System Sciences,1993,46:39-59.
[9]Komorowski O J,Showron A,Synak P. The ROSETTA System. In:Plokowki L, Skowron A, ed.Rough Sets in Kowlege Discovery, Physica-Verlag,19:572-576.
[10]Torres G L,Quintana VH,da Silva A P A,et al. Classification of Power System Operation Point using Rough Set Techniques. IEEE International Conference on Systems, Man and Cybernetics, Information Intelligence and Systems, 1996:1898-1903.
[1]Moro L.F.L., Process technology in the petroleum refinery industry-current situation and future trends, Computers and Chemical Engineering.27(2003) 1303-1305.
[2]Lee, H., Pinto, J. M., Grossmann, I. E.,& Park, S. (1996). Mixed-integer linear programming model for refinery short-term scheduling of crude oil unloading with inventory management. Industrial and Engineering Chemistry Research,35, 1630-1641.
[3]Pinto J.M., Joly M., Moro L.F.L., Planning and scheduling models for refinery operations, Computers and Chemical Engineering.24 (2000) 2259-2276.
[4]Jia, Z.; Ierapetritou, M. Efficient Short-Term Scheduling of Refinery Operations Based on a Continuous Time Formulation. Comput. Chem. Eng.2004,28, 1001-1019.
[5]Jia, Z.; Ierapetritou, M. Mixed-Integer Linear Programming Model for Gasoline Blending and Distribution Scheduling. Ind. Eng. Chem. Res.2003,42,825-835.
[6]Xiong G., Nyberg T.R., Push/pull production plan and schedule used in modern refinery CIMS, Robotics Comput. Integrat.Manufact.16 (2000) 397-410.
[7]Alle A., Pinto J.M., Mixed-integer programming models for the scheduling and operational optimization of multi-product continuous plants, Industrial and Engineering Chemistry Research,41 (2002) 2689-2704.
[8]Chryssolouris G., Papakostas N., Mourtzis D., Refinery short-term scheduling with tank farm, inventory and distillation management:an integrated simulation-based approach, Eur. J. Oper. Res.166 (2005) 812-827.
[9]Moro L.F.L., Pinto J.M., Mixed-integer programming approach for short-term crude oil scheduling, Ind. Eng. Chem. Res.43 (2004) 85-94.
[10]Susana Relvas, Henrique A. Matos, Ana Paula F. D. Barbosa-Povoa, Joao Fialho, Antonio S. Pinheiro. Pipeline Scheduling and Inventory Management of a Multiproduct Distribution Oil System. Industrial and Engineering Chemistry Research,2006,45,7841-7855.
[11]Carlos A. Mendez, Ignacio E. Grossmann, Iiro Harjunkoski, Pousga Kabor. A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations. Computers and Chemical Engineering.2006,30,614-665.
[12]Magatao, L., Arruda, L. V. R., Neves, F. J. Using CLP and MILP for scheduling commodities in a pipeline. In Proceedings of the 15th European Symposium on Computer-Aided Process Engineering (ESCAPE15), Barcelona, Spain, May 29-June 1,2005; Computer Aided Chemical Engineering, Vol.20; Puigjaner, L., Espuna, A., Eds.; Elsevier:Amsterdam, New York,2005; pp 1027-1032.
[13]Furman, K. C., Jia, Z.,& Ierapetritou, M. G. (2007). A robust event-based continuous time formulation for tank transfer scheduling. Industrial and Engineering Chemistry Research,46, (2007),9126-9136.
[14]Guo Q. Q, Li Q.Q., Ding R. Zhang J.S. Wang X.H.. Dynamic Scheduling of Oil Refinery based on Model-optimization and Process-configuration. ICAL 2007, Jinan, China.2007.8:1117-1121.