炼油企业生产调度研究
摘要
生产调度在流程工业企业中起着承上启下的关键作用,是生产经营的核心,它决定了生产过程是否能够顺利进行,影响到企业的生产成本和资源的合理利用。炼油生产企业是流程工业的典型代表,激烈的市场竞争促使炼油企业提高利润和减小成本的压力越来越大。由于炼油企业生产调度优化方案能减少原油采购、运输、加工和存储成本,充分利用生产加工能力,提高资源利用效率,增加生产收率,满足需求和最大化利润,因而生产调度问题成为研究的热点问题。但由于炼油企业生产调度问题具有复杂性、不确定性、非线性、多目标和多约束等特点,而成为研究的难点问题。以往的研究一般以炼油企业中某一生产环节如罐区存储、装置加工、原油和成品油调和等作为调度对象,而没有从全厂角度整体考虑调度目标,但局部生产优化不等于全厂最优,因而无法达到全局优化。本文对炼油企业调度问题的研究主要包括以下几个方面:
(1)从炼油企业整体建模角度出发,将构建炼油企业生产流程模型所需的各种模型元素抽象出来,通过对这些元素的数学描述,来剖析炼油企业流程建模的特点,通过这些抽象出的模型元素可以为灵活构建整个生产网络奠定基础。并着重描述了这些不同模型元素在多操作方案下,物料平衡计算及物性计算的特点。
(2)详细讨论了炼油企业操作约束建模以及如何构建高效数学模型的问题。论述了如何通过逻辑角度来理解和描述生产操作规则的建模,以及如何将逻辑描述转化为数学模型。同时还阐述了许多建立高效混合整数线性规划模型的技巧,如:何时将0-1变量视作0-1连续性变量、将高维离散变量分解为低维离散变量等。
(3)通过所介绍的物流框架模型和逻辑建模方法,通过实例揭示如何建立一个完整的调度模型。通过该实例展示,用一个单一的数学优化模型来刻画企业调度细节,特别是刻画与储罐相关的物流分配操作,如:同时接收物料的储罐数目、同时输送物料的储罐数目、储罐的多操作方案、储罐接收物料后的静置时间等,不仅会导致模型规模庞大,而且会导致大量的离散变量,从而极大影响了模型的运行时间。
(4)针对单一优化模型求解困难的难点,提出了一种分层建模与求解策略。上层优化模型用来决定装置宏观的调度策略:装置各加工方案的时刻和顺序、进出厂管线输送物料的时间及顺序、并决定各装置在各时间周期内生产或消耗的物料数量及种类。在上层的优化模型中只涉及企业的集合罐,即由当前储存相同物料的物理罐组合而成的逻辑罐。下层系统将应用启发式规则去调整集合罐内多用途的操作方案,以调节集合罐的存储能力。另外下层系统也通过启发式规则来管理物理罐接收和输送物料的顺序。这样的分层策略能大大减少上层优化模型离散变量的数目,加快模型的求解速度。
(5)在分层策略的基础上,提出了一种不确定条件下具有鲁棒特性的计划与调度整合的策略。由于生产过程数据测量不准确,或未预知事件的发生,可能会使模型找到的优化解不再是可行解,为了缓解这种不确定因素的影响,介绍了一种鲁棒建模策略。同时为了探索计划与调度整合问题,在分层策略的基础上,提出了基于滚动优化的计划/调度整合模型。
Scheduling is the key of Manufacturing Executing System (MES), which links process control system and business management system in process industry. It determines whether production process is running smoothly, influences production cost and reasonable utilization of resources. It is oil refinery that is one of the typical representatives of process industry. The refineries have to make great efforts to increase profits while decrease costs under market competition. The optimal solution of refinery production scheduling can reduce the costs of purchase, transportation, storage, and make the best of production capacity, improve efficiency of resource utlization and capacity ratio, meet demands and maximize profits, the refinery production scheduling problem receives significant attention in recent year. However, the refinery scheduling problem is difficult to solve for its complexity, uncertainty, nonlinearity, multi-objectives and multi-contraints. In many literatures, scheduling object was one section of production process in refinery such as tank farm storage, production unit process, crude oil or products blending, but entire refinery was not considered as the scheduling target. It is obvious that local production optimization does not achieve global refinery optimization. The main works of this dissertation are as follows:
(1) A general modeling framework and modeling approaches for scheduling in refineries are proposed. Modeling frameworks of processing units, storage tanks, and pipelines, which are introduced for operational planning (Neiro and Pinto), are extended for operational scheduling. The most important extension is that operation modes for these entities are considered. And at the same time, another framework for perimeter unit is introduced to control raw material supplies and product demands. A complex production topology can be built by these grouped elementary entities that are interconnected by intermediate streams.
(2) We reveal that many modeling problems in refineries can be understood from a logic and qualitative view. This work will shed light on the logic essential of many mathematical formulations presented in the literature. And, how to convert logic expressions to mathematical formulations is also presented. The application of the approach to some modeling problems is elaborated. These modeling problems include: modeling some important operation rules in refineries, decomposing large- index binary variables to small-index binary variables and linearization of some nonlinear formulations.
( 3 ) Based on the modeling framework and the logic modeling methods, a scheduling optimization model for refineries is built. This example shows how to build scheduling models by using the modeling framework, and how to model operation rules by using the logic inference. The computational results show that the operation rules, such as a minimum amount of time to allow the settle of tanks after receiving materials, that just one tank receives the same kind of material at the same time and a tank cannot receive and send material at the same time, that one mode of a tank can only hold one kind of material, will lead to a large number of 0-1 variables that would make the model solution unattainable within reasonable time.
( 4 ) A hierarchical approach with two decision levels for short-term scheduling problems in refineries is proposed. This approach includes two levels: the optimization model at the upper level and the heuristics and rules adopted in simulation system at the lower level. The optimization model is used to decide sequencing and timing of the operation modes of processing units and pipelines, and to determine the quantities of materials consumed/produced by each operation mode of a unit. Only aggregate tanks are used at the upper level, and the simulation system at the lower level uses a heuristic to adjust the operation modes of some actual multipurpose tanks within aggregate tanks. The iteration procedure between the upper level and the lower level is used to find optimal solution under new aggregate storage capacities. The simulation system also uses some heuristics and rules to arrange actual tanks to receive/send materials with logic operation constraints (operation rules) on tanks respected.
( 5 ) A methodology for the integration of production planning and scheduling in refineries is proposed. This method also relies on the two-level hierarchical approach. In this paper, uncertainties are also considered, and, two robust optimization approaches presented for optimization problems under uncertainty are integrated to cope with uncertain parameters with continuous and discrete probability distribution respectively.
(1)从炼油企业整体建模角度出发,将构建炼油企业生产流程模型所需的各种模型元素抽象出来,通过对这些元素的数学描述,来剖析炼油企业流程建模的特点,通过这些抽象出的模型元素可以为灵活构建整个生产网络奠定基础。并着重描述了这些不同模型元素在多操作方案下,物料平衡计算及物性计算的特点。
(2)详细讨论了炼油企业操作约束建模以及如何构建高效数学模型的问题。论述了如何通过逻辑角度来理解和描述生产操作规则的建模,以及如何将逻辑描述转化为数学模型。同时还阐述了许多建立高效混合整数线性规划模型的技巧,如:何时将0-1变量视作0-1连续性变量、将高维离散变量分解为低维离散变量等。
(3)通过所介绍的物流框架模型和逻辑建模方法,通过实例揭示如何建立一个完整的调度模型。通过该实例展示,用一个单一的数学优化模型来刻画企业调度细节,特别是刻画与储罐相关的物流分配操作,如:同时接收物料的储罐数目、同时输送物料的储罐数目、储罐的多操作方案、储罐接收物料后的静置时间等,不仅会导致模型规模庞大,而且会导致大量的离散变量,从而极大影响了模型的运行时间。
(4)针对单一优化模型求解困难的难点,提出了一种分层建模与求解策略。上层优化模型用来决定装置宏观的调度策略:装置各加工方案的时刻和顺序、进出厂管线输送物料的时间及顺序、并决定各装置在各时间周期内生产或消耗的物料数量及种类。在上层的优化模型中只涉及企业的集合罐,即由当前储存相同物料的物理罐组合而成的逻辑罐。下层系统将应用启发式规则去调整集合罐内多用途的操作方案,以调节集合罐的存储能力。另外下层系统也通过启发式规则来管理物理罐接收和输送物料的顺序。这样的分层策略能大大减少上层优化模型离散变量的数目,加快模型的求解速度。
(5)在分层策略的基础上,提出了一种不确定条件下具有鲁棒特性的计划与调度整合的策略。由于生产过程数据测量不准确,或未预知事件的发生,可能会使模型找到的优化解不再是可行解,为了缓解这种不确定因素的影响,介绍了一种鲁棒建模策略。同时为了探索计划与调度整合问题,在分层策略的基础上,提出了基于滚动优化的计划/调度整合模型。
Scheduling is the key of Manufacturing Executing System (MES), which links process control system and business management system in process industry. It determines whether production process is running smoothly, influences production cost and reasonable utilization of resources. It is oil refinery that is one of the typical representatives of process industry. The refineries have to make great efforts to increase profits while decrease costs under market competition. The optimal solution of refinery production scheduling can reduce the costs of purchase, transportation, storage, and make the best of production capacity, improve efficiency of resource utlization and capacity ratio, meet demands and maximize profits, the refinery production scheduling problem receives significant attention in recent year. However, the refinery scheduling problem is difficult to solve for its complexity, uncertainty, nonlinearity, multi-objectives and multi-contraints. In many literatures, scheduling object was one section of production process in refinery such as tank farm storage, production unit process, crude oil or products blending, but entire refinery was not considered as the scheduling target. It is obvious that local production optimization does not achieve global refinery optimization. The main works of this dissertation are as follows:
(1) A general modeling framework and modeling approaches for scheduling in refineries are proposed. Modeling frameworks of processing units, storage tanks, and pipelines, which are introduced for operational planning (Neiro and Pinto), are extended for operational scheduling. The most important extension is that operation modes for these entities are considered. And at the same time, another framework for perimeter unit is introduced to control raw material supplies and product demands. A complex production topology can be built by these grouped elementary entities that are interconnected by intermediate streams.
(2) We reveal that many modeling problems in refineries can be understood from a logic and qualitative view. This work will shed light on the logic essential of many mathematical formulations presented in the literature. And, how to convert logic expressions to mathematical formulations is also presented. The application of the approach to some modeling problems is elaborated. These modeling problems include: modeling some important operation rules in refineries, decomposing large- index binary variables to small-index binary variables and linearization of some nonlinear formulations.
( 3 ) Based on the modeling framework and the logic modeling methods, a scheduling optimization model for refineries is built. This example shows how to build scheduling models by using the modeling framework, and how to model operation rules by using the logic inference. The computational results show that the operation rules, such as a minimum amount of time to allow the settle of tanks after receiving materials, that just one tank receives the same kind of material at the same time and a tank cannot receive and send material at the same time, that one mode of a tank can only hold one kind of material, will lead to a large number of 0-1 variables that would make the model solution unattainable within reasonable time.
( 4 ) A hierarchical approach with two decision levels for short-term scheduling problems in refineries is proposed. This approach includes two levels: the optimization model at the upper level and the heuristics and rules adopted in simulation system at the lower level. The optimization model is used to decide sequencing and timing of the operation modes of processing units and pipelines, and to determine the quantities of materials consumed/produced by each operation mode of a unit. Only aggregate tanks are used at the upper level, and the simulation system at the lower level uses a heuristic to adjust the operation modes of some actual multipurpose tanks within aggregate tanks. The iteration procedure between the upper level and the lower level is used to find optimal solution under new aggregate storage capacities. The simulation system also uses some heuristics and rules to arrange actual tanks to receive/send materials with logic operation constraints (operation rules) on tanks respected.
( 5 ) A methodology for the integration of production planning and scheduling in refineries is proposed. This method also relies on the two-level hierarchical approach. In this paper, uncertainties are also considered, and, two robust optimization approaches presented for optimization problems under uncertainty are integrated to cope with uncertain parameters with continuous and discrete probability distribution respectively.
引文
[1]王凌,王雄,金以慧.MES-流程工业CIMS发展的关键[J].化工自动化及仪表,2001,28(4):1-5.
[2]Energy information administration,http://www.eia.doe.gov.
[3]姚建初.面向CIPS的智能集成优化设计系统研究[J].计算机集成制造系统.2000,6(5):39-42.
[4]徐用懋.流程工业的CIMS[J].化工自动化及仪表,1997,24(3):58-62.
[5]WENKAI L,CHI-WAI H.Scheduling crude oil unloading,storage,and processing[J].Industrial and Engineering Chemistry Research,2002,41:6723-6734.
[6]PINTO J M,JOLY M,MORO L F L.Planning and scheduling models for refinery operations[J].Computers and Chemical Engineering,2000,24:2259-2276.
[7]FLOUDAS,C A,LIN X.Continuous-time versus discrete-time approaches for scheduling of chemical processes:a review[J].Computers and Chemical Engineering,2004,28:2109-2119.
[8]PINTO J M,GROSSMANN I E.Optimal cyclic scheduling of multistage continuous multiproduct plants[J].Computers and Chemical Engineering,1994,18:797-816.
[9]PINTO J M,GROSSMANN I E.A continuous time mixed integer linear programming model for short term scheduling of multistage batch plants[J].Industrial and Engineering Chemistry Research,1995,34:3037-3051.
[10]PINTO J M,GROSSMANN I E.An alternate MILP model for short-term scheduling of batch plants with preordering constraints[J].Industrial and Engineering Chemistry Research,(1996),35:338-342.
[11]KARIMI L A,MCDONALD C M.Planning and scheduling of parallel semicontinuous processes.Part 2.Short-term scheduling[J].Industrial and Engineering Chemistry Research,1997,36:2701-2714.
[12]LAMBA N,KARIMI L A.Scheduling parallel production lines with resource constraints.Part 1.Model formulation[J].Industrial and Engineering Chemistry Research,2002,41:779-789.
[13]LAMBA N,KARIMI I A.Scheduling parallel production lines with resource constraints. Part 2. Decomposition algorithm[J]. Industrial and Engineering Chemistry Research, 2002, .41: 790-800.
[14] Mendez C A, Grossmann I E. Harjunkoski I, Kabore P, A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations[J]. Computers and Chemical Engineering, 2006, 30: 614-634.
[15] CASTRO P, BARBOSA-POVOA A P F D, Matos H. An improved RTN continuous-time formulation for the short-term scheduling of multipurpose batch plants[J]. Industrial and Engineering Chemistry Research, 2001, 40:2059-2068.
[16] MAJOZI T, ZHU X X. A novel continuous-time milp formulation for multipurpose batch plants, part 1: short-term scheduling[J]. Industrial and Engineering Chemistry Research, 2001, 40: 5935-5949.
[17] LEE K, PARK H I, LEE I. A novel nonuniform discretetime formulation for short-term scheduling of batch and continuous processes[J]. Industrial and Engineering Chemistry Research, 2001,40: 4902-4911.
[18] BURKARD R E, FORTUNA T, HURKENS C A J. Makespan minimization for chemical batch processes using non-uniform timegrids[J]. Computers and Chemical Engineering, 2002, 26: 1321-1332.
[19] WANG S, GUIGNARD M. Redefining event variables for efficient modeling of continuous-time batch processing[J]. Annals of Operations Research, 2002,116: 113-126.
[20] IERAPETRITOU M G, FLOUDAS C A. Effective continuous-time formulation for short-term scheduling. Part 1. Multipurpose batch processes.Industrial and Engineering Chemistry Research, 1998a, 37: 4341-4359.
[21] IERAPETRITOU M G, FLOUDAS C A. Effective continuous-time formulation for short-term scheduling. Part 2. Continuous and semi-continuous processes[J]. Industrial and Engineering Chemistry Research, 1998b, 37: 4360-4374.
[22] IERAPETRITOU M G, HENE T S, FLOUDAS C A. Effective continuous-time formulation for short-term scheduling. Part 3. Multiple intermediate due dates[J]. Industrial and Engineering Chemistry Research, 1999, 38: 3446-3461.
[23] LIN X, AND FLOUDAS C A. Design, synthesis and scheduling of multipurpose batch plants via an effective continuous-time formulation[J].Computers and Chemical Engineering, 2001, 25: 665-670.
[24] IERAPETRITOU M G, FLOUDAS C A. Comments on "an improved RTN continuous-time formulation for the short-term scheduling of multipurpose batch plants" [J]. Industrial and Engineering Chemistry Research, 2001, 40:5040-5041.
[25] SAHINIDIS N V. Optimization under uncertainty: state-of-the-art and opportunities[J]. Computers and Chemical Engineering, 2004, 28: 971-983.
[26] SHAPIRO A, HOMEM-DE-MELLO T. A simulation-based approach to two-stage stochastic programming with recourse[J]. Mathematical Programming,1998,81:301-325.
[27] LINDEROTH J, SHAPIRO A, WRIGHT S. (2002). The empirical behavior of sampling methods for stochastic programming. Technical report, University of Wisconsin, Madison, WI.
[28] BASSETT M H, PEKNY J F, REKLAITIS G.V. Using detailed scheduling to obtain realistic operating policies for a batch processing facility[J]. Industrial and Engineering Chemistry Research, 1997,36: 1717-1726.
[29] VIN J P, IERAPETRITOU M G. A new approach for efficient rescheduling of multiproduct batch plants[J]. Industrial and Engineering Chemistry Research,2000, 39: 4228-4238.
[30] ACEVEDO J, PISTIKOPOULOS E N. Stochastic optimization based algorithms for process synthesis under uncertainty[J]. Computers and Chemical Engineering, 1998, 22: 647-671.
[31] CLAY R L, GROSSMANN I E. A disaggregation algorithm for the optimization of stochastic planning models[J]. Computers and Chemical Engineering, 1997, 21: 751-774.
[32] GUPTA A, MARANAS C D. A two-stage modeling and solution framework for multisite midterm planning under demand uncertainty[J]. Industrial and Engineering Chemistry Research, 2000, 39: 3799-3813.
[33] LIU M L, SAHINIDIS N V. Optimization in process planning under uncertainty[J]. Industrial and Engineering Chemistry Research, 1996, 35: 4154-4165.
[34] LI L S, LAI K K. A fuzzy approach to the multiobjective transportation problem[J]. Computers and Operations Research, 2000, 27: 43-57.
[35] SAKAWA M, NISHIZAKI I, UEMURA Y. A decentralized two-level transportation problem in a housing material manufacturer: Interactive fuzzy programming[J]. European Journal of Operational Research, 2002, 141:167-185.
[36J Balasubramanian J, Grossmann 1 E. Scheduling optimization under uncertaintys- An alternative approach[J]. Computers and Chemical Engineering, 2003, 27:469-490.
[37] LIN 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: 1069-1085.
[38] FLOUDAS C A, LIN X. Mixed integer linear programming in process scheduling: modeling, algorithms, and applications[J]. Annuals of Operation Research, 2005, 139, 131-162.
[39] HONKOMP S J, MOCKUS L, REKLAIT1S G V. A framework for schedule evaluation with processing uncertainty[J]. Computers and Chemical Engineering, 1999,23: 595-609.
[40] ROSLOF J, HARJUNKOSKI I, BJORKQVIST J, KARLSSON S. An MILP-based reordering algorithm for complex industrial scheduling and rescheduling[J]. Computers and Chemical Engineering, 2001, 25: 821-828.
[41] JANAK S L, AND FLOUDAS C A. Production scheduling of a large-scale industrial batch plant. ii. reactive scheduling[J]. Industrial and Engineering Chemistry Research, 2006, 45: 8253-8269.
[42] MENDEZ C A, CERDA J. Dynamic scheduling in multiproduct batch plants[J].Computers and Chemical Engineering, 2003, 27: 1247-1250.
[43] Mendez C A, Cerda J. An MILP framework for batch reactive scheduling with limited discrete resources[J]. Computers and Chemical Engineering, 2004, 28:1059-1067.
[44] AYTUG H, LAWLEY M A, MCKAY K, MOHAN S, UZSOY R. Executing production scheduling in the face of uncertainties: a review and some future directions[J]. European Journal of Operational Research, 2005, 161: 86-110.
[45] RYN J H, DUA V, PISTIKOPOULOS E N. A bilevel programming framework for enterprise-wide process networks under uncertainty[J]. Computers and Chemical Engineering, 2004, 28: 1121-1129.
[46] ADHITYA A, SRINIVASAN R, KARIMI I A. Heuristic rescheduling of crude oil operationsto manage abnormal supply chain events[J]. AICHE Journal,2007,53: 397-422.
[47] TILL J, SAND G, URSELMANN M, ENGELL S A. hybrid evolutionary algorithm for solving two-stage stochastic integer programs in chemical batch scheduling[J]. Computers and Chemical Engineering, 2007, 31: 630-647.
[48]DEPAOLO C A,RADER D J.A heuristic algorithm for a chance constrained stochastic program[J].European Journal of Operational Research,2007,176:27-45.
[49]SAKAWA M,NISHIZAKI I,UEMURA Y.A decentralized two-level transportation problem in a housing material manufacturer:Interactive fuzzy programming[J].European Journal of Operational Research,2002,141:167-185.
[50]ORCUN S,ALTINEL I K.HORTACSU(O|¨).Scheduling of batch processes with operational uncertainties[J].Computers and Chemical Engineering,1996,20:1191-1196.
[51]JOHNSON E L,NEMHAUSER G L.Recent developments and future directions in mathematical Programming[J].IBM Systems Journal,31:79-93.
[52]LAND A H,DOIG A G.An automatic method for solving discrete programming problems[J].Econometrica,1960,28:497-520.
[53]JOHNSON E L,NEMHAUSER G L,SAVELSBERGH M P.Progress in linear programming-based algorithms for integer programming:an exposition[J].INFORMS Journal on Computing,2000,12,1201-1222.
[54]NEMHAUSER G L,WOLSEY L A.integer and combinatorial optimization[M].John Wiley,New York,1988.
[55]马振华,刘坤林,.陆璇.运筹学与最优化理论[M].清华大学出版社,1998.
[56]甘应爱,田丰,李维铮.运筹学[M].清华大学出版社,2005.
[57]KALLRATH J.Mixed integer optimization in the chemical process industry experience,potential and future perspectives[J].Trans IChemE,2000,78,809-822.
[58]GUPTA O K.RAVINDRAN V.Branch and bound experiments in convex nonlinear integer programming[J].Management Science,1985,31:1533-1546
[59]GEOFFRION A M.Generalized Benders Decomposition[J].Journal of Optimization Theory and Applications,1972,10,237-260.
[60]DURAN M A,GROSSMANN I E.An outer-approximation algorithm for a class of mixed-integer nonlinear programs[J],mathematical programming,1986,36,307-312.
[61]GROSSMANN I E,HEEVER S A,HARJUNKOSKI I.discrete optimization methods and their role in the integration of planning and scheduling[J].AIChE Symposium 2002,98:150-168.
[62] IYER R R, GROSSMANN 1. E. A bilevel decomposition algorithm for longe-range planning of process networks[J]. Industrial and Engineering Chemistry Research, 1998,37:474-492.
[63] IYER R R, GROSSMANN I. E. Synthesis and operational planning of utility systems for multiperiod operation[J]. Computers and Chemical Engineering,1998,22:979-993.
[64] PAPAGEORGIOU L G, PANTELIDES C C. Optimal campaign planning/scheduling of multipurpose batch/semi-continuous plants, 2 A mathematical decomposition approach[J]. Industrial and Engineering Chemistry Research,1996,35:510-529.
[65] DOGAN M E, GROSSMANN I E. A decomposition method for the simultaneous planning and scheduling of single stage continuous multiproduct plants[J]. Industrial and Engineering Chemistry Research, 45: 299-315.
[66] FISHER M L. The Lagrangean relaxation method for solving integer programming problems[J]. Management Science, 1981, 27(1): 1-17.
[67] GUIGNARD M, KIM S. Lagrangean decomposition: A model yielding stronger lagrangean bounds[J]. Mathematic Programming, 1987, 39: 215-228.
[68] DAN WU, IERAPETRITOU M G. Decomposition approaches for the efficient solution of short-term scheduling problems[J]. Computers and Chemical Engineering, 2003,27: 1261-1276.
[69] DAN WU, IERAPETRITOU M G. Lagrangean decomposition using an improved Nelder-Mead approach for Lagrangean multiplier update[J].Computers and Chemical Engineering, 2006, 30: 778-789.
[70] HEEVER S A, GROSSMANN I E.A strategy for the integration of production planning and reactive scheduling in the optimization of a hydrogen supply network[J]. Computers and Chemical Engineering, 2003, 27: 1813-1839.
[71] ROGERS D F, PLANTE R D, WONG R T, EVANS J R. Aggregation and disaggregation techniques and methodology in optimization[J]. Operation Research, 1991,39:553-574.
[72] ERMOLIEV Y M, KRYAZHIMSKII A V, Ruszcynski A. Constraint aggregation principle in convex optimization[J]. Mathematic Programming,1997, 76: 353-372.
[73] WILKINSON S J, CORTIER A, SHAH N, PANTELIDES C C. Integrated production and distribution scheduling on a Europe-wide basis[J]. Computers and Chemical Engineering, 1996,20: 1275-1280.
[74]HEEVER,S A,GROSSMANN I E.an iterative aggregation/disaggregation approach for the solution of a mixed-integer nonlinear oilfield infrastructure planning model[J].Industrial and Engineering Chemistry Research,2000,39:1955-1971.
[75]BLOMER F,GUNTHER H O.Scheduling of a multi-product batch process in the chemical industry[J].Computers in Industry,1998,36:245-259.
[76]BYLKA S,REMPA R.Heuristics for impulse replenishment with continuous periodic demand[J].International Journal of Production Economics,2004.88:183-190.
[77]CHERN C C,HSIEH J S.A heuristic algorithm for master planning that satisfies multiple objectives[J].Computers and Operations Research,2007,34:3491-3513.
[78]WITT A,STEFAN V.Simple heuristics for scheduling with limited intermediate storage[J].Computers and Operations Research,2007,34:2293-2309.
[79]钱晓龙,唐立靳,刘文新.动态调度的研究方法综述[J].控制与决策,2001,16(2):141-145.
[80]KIMMS A.Competitive methods for multi-level lot sizing and scheduling:Tabu search and randomized regrets[J].International Journal of Production Research,1996,34:2279-2298.
[81]ZHANG C Y,LIP G,GUAN Z L,RAO Y Q.A tabu search algorithm with a new neighborhood structure for the job shop scheduling problem[J].Computers and Operations Research,2007,34:3229-3242.
[82]CHEN L,BOSTEL N,DEJAX P,CAI J G,XI L F.A tabu search algorithm for the integrated scheduling problem of container handling systems in a maritime terminal[J].European Journal of Operational Research,2007,181:40-58.
[83]ARULDOSS T A V,EBENEZER J A.A tabu search based hybrid optimization approach for a fuzzy modelled unit commitment problem[J].Electric Power Systems Research,2006,76:413-425.
[84]王凌.车间调度及其遗传算法[M].北京:清华大学出版社,2003.
[85]ZHANG C Y,LIP G,RAO Y Q,GUAN Z L.A very fast TS/SA algorithm for the job shop scheduling problem[J].Computers and Operations Research,2008,35:282-294.
[86]DAHAL K P,AND CHAKPITAK N.Generator maintenance scheduling in power systems using metaheuristic-based hybrid approaches[J].Electric Power Systems Research,2007,77:771-779.
[87]SADEGHEIH A.Sequence optimization and design of allocation using GA and SA[J].Applied Mathematics and Computation,2007,186:1723-1730.
[88]CHEN J S,PAN J C H,LIN C M.A hybrid genetic algorithm for the re-entrant flow-shop scheduling problem[J].Expert Systems with Applications,2008,34:570-577.
[89]CHATFIELD D C.The economic lot scheduling problem:A pure genetic search approach[J].Computers and Operations Research,2007.34:2865-2881.
[90]HU X B,CHEN W H,Genetic algorithm based on receding horizon control for arrival sequencing and scheduling[J].Engineering Applications of Artificial Intelligence,2005,18:633-642.
[91]WANG K,LOHL T,STOBBE M,ENGELS.A genetic algorithm for onlinescheduling of a multiproduct polymer batch plant[J].Computers and Chemical Engineering,2000,24(2-7):393-400.
[92]HU X H,EBERHART R.Solving constrained nonlinear optimization problems with particle swarm optimization[J].Proceedings of the Sixth World Multiconference on Systemics,Cybernetics and Informatics(SCI 2002),Orlando,USA.
[93]REKLAITIS G V.Scheduling approaches for the batch process industries[J].ISA Transactions,1995,34(4):349-358.
[94]ARTIBA A,RIANE F.An application of a planning and scheduling multimodel approach in the chemical industry[J].Computer in Industry,1998,36:209-229.
[95]王伟达,王伟,刘文剑.基于仿真的生产计划与调度系统集成[J].计算机工程与设计,2007,28,55-72.
[96]PFEIFFER A,KADAR B,MONOSTORI L.Stability-oriented evaluation of rescheduling strategies,by using simulation[J].Computers in Industry,2007,58:630-643.
[97]伍乃骐,白丽平.炼油生产计划和调度优化的研究[J].计算机集成制造系统2005,11(1):90-96.
[98]廖良才.成品油调和调度优化模型及其应用研究[D].长沙:国防科学技术大学,2003.
[99]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.
[100]SHAH N.Mathematical programming techniques for crude oil scheduling[J].Computers and Chemical Engineering,1996,20:1227-1232.
[101]LEE H.,PINTO J M,GROSSMANN I E,PARK S.Mixed-Integer programming model for re(?)nery short term scheduling of crude oil unloading with inventory management[J].Industrial and Engineering Chemistry Research,1996,35:1630-1641.
[102]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.
[103]JIA Z,IEARAPETRITOU M.Refinery Short-Term Scheduling Using Continuous Time Formulation:Crude-Oil Operations[J].Industrial Engineering and Chemistry Research,2003,42:3085-3097.
[104]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.
[105]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.
[106]DAHAL K P,ALDRIDGE C J,MCDONALD J R,BURT G M.A GA-based technique for the scheduling of storage tanks.Proceeding of Congress of Evolutionary Computation,1999:2199-2206.
[107]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.
[108]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.
[109]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.
[110]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.
[111]G(O|¨)THE-LUNDGREN M,LUNDGREN J T.An optimization model for refinery production scheduling[J].International Journal of Production Economics,2002,78:255-270.
[112]PERSSONY J A,GOTHE-LUNDGREN M,LUNDGREN J T,GENDRON B.A tabu search heuristic for scheduling the production processes at an oil refinery[J].International Journal of Production Economics,2004,42:445-471.
[113]丁锋,杨家本,兰鸿森.连续过程生产调度5日滚动作业计划的研究[J].系统工程理论与实践,1998,18(11):11-19.
[114]杜江,王波兴,周煌.基于优化技术的生产调度专家系统研究[J].数学杂志,1998,18:121-124.
[115]杜江 周济,王波兴等.CIPS环境下的生产调度专家系统研究[J].计算机工程与设计,1999,20(4):29-33.
[116]姚建初,周济,刘伯龙.炼油生产调度专家系统的应用研究[J].计算机工程与应用,2000,10:174-176.
[117]姚建初,刘伯龙,李卫国.流程工业管控一体化系统的研究与开发[J].自动化博览 管控一体化技术,2000,(S1):58-60.
[118]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.
[119]GLISMANN K,GRUHN G.Short-term scheduling and recipe optimization of blending processes[J].Computers and Chemical Engineering,2001,25:627-634.
[120]JIA Z,IEARAPETRITOU,M.Mixed-integer linear programming for gasoline blending and distribution scheduling[J].Industrial Engineering and Chemistry Research,2003,42:825-835.
[121]NEIRO S M S,AND PINTO J M.A general modeling framework for the operational planning of petroleum supply chains[J].Computers and Chemical Engineering,2004,28:871-896.
[122]KELLY J D,Next-generation refinery scheduling technology,http://www.dashop timiz ation com/home/downloads/pdf/npra2003.pdf.
[123]RAMAN R,GROSSMANN I E.Relation between MILP modeling and logical inference for chemical process synthesis[J].Computers and Chemical Engineering,1991,15:73-84.
[124]GROSSMANN I E.Logic-based modeling and solution for discrete/continuous problems in process systems engineering,http://cepac,cheme,cmu,edu/pasilect ures,htm.
[125]FLOUDAS C A, LIN X. Continuous-time versus discrete-time approaches for scheduling of chemical processes: a review[J]. Computers and Chemical Engineering, 2004,28: 2109-2129.
[126]MORO L F L, PINTO J M. Mixed-Integer programming ppproach for short-term crude oil scheduling[J]. Industrial and Engineering Chemistry Research,2004,43:85-94.
[127]CHRYSSOLOURIS G, PAPAKOSTAS N. Refinery short-term scheduling with tank farm, inventory and distillation management: An integrated simulation-based approach[J]. European Journal of Operational Research, 2005,166: 812-827.
[128]HENNING G P, JAIME C. Knowledge-based predictive and reactive schedule-ing in industrial environments[J]. Computers and Chemical Engineering,2000,24:2315-2338.
[129]BASSETT M H, DAVE P, DOYLE F J. Perspectives on model based integration of process operations[J]. Computers and Chemical Engineering, 1996,20:821-844.
[130]BIREWAR D B, GROSSMANN I E. Simultaneous production planning and scheduling of multiproduct batch plants[J]. Industrial and Engineering Chemistry Research, 1990, 29: 570-593.
[131] GUILLEN G, BADELL M, ESPUNA A, PUIGJANER L. Simultaneous optimization of process operations and financial decisions to enhance the integrated planning/scheduling of chemical supply chains[J]. Computers and Chemical Engineering,.2006, 30: 421-436.
[132]CHENG L, SUBRAHMANIAN E, WESTERBERG A W. A comparison of optimal control and stochastic programming from a formulation and computation perspective[J]. Computers and Chemical Engineering, 2004, 29:149-164.
[133] JANAK S L, LIN X, FLOUDAS C A. A new robust optimization approach for scheduling under uncertainty II. Uncertainty with known probability distribution[J]. Computers and Chemical Engineering, 2007, 31:171-195.
[134] LEUNG S C H, TSANG S O S, NG W L, WU Y. A robust optimization model for multi-site production planning problem in an uncertain environment[J].European Journal of Operational Research, 2007, 181: 224-238.
[135]徐用懋,张猛.流程工业的综合自动化技术[J].工业控制计算机, 2002, 15:5-10.
[136]李慧莹,柴天佑.钢铁企业扁半化管理模式下的CIMS体系结构[J].东北大学学报(自然科学版),2002.23:746-749.
[137]柴天佑,金以慧.基于三层结构的流程工业现代集成制造系统[J].控制工程,2002,9:1-6.
[138]赵小强.炼油企业生产调度问题研究[D].浙江:浙江大学信息科学与工程学院,2005:77-89.
[2]Energy information administration,http://www.eia.doe.gov.
[3]姚建初.面向CIPS的智能集成优化设计系统研究[J].计算机集成制造系统.2000,6(5):39-42.
[4]徐用懋.流程工业的CIMS[J].化工自动化及仪表,1997,24(3):58-62.
[5]WENKAI L,CHI-WAI H.Scheduling crude oil unloading,storage,and processing[J].Industrial and Engineering Chemistry Research,2002,41:6723-6734.
[6]PINTO J M,JOLY M,MORO L F L.Planning and scheduling models for refinery operations[J].Computers and Chemical Engineering,2000,24:2259-2276.
[7]FLOUDAS,C A,LIN X.Continuous-time versus discrete-time approaches for scheduling of chemical processes:a review[J].Computers and Chemical Engineering,2004,28:2109-2119.
[8]PINTO J M,GROSSMANN I E.Optimal cyclic scheduling of multistage continuous multiproduct plants[J].Computers and Chemical Engineering,1994,18:797-816.
[9]PINTO J M,GROSSMANN I E.A continuous time mixed integer linear programming model for short term scheduling of multistage batch plants[J].Industrial and Engineering Chemistry Research,1995,34:3037-3051.
[10]PINTO J M,GROSSMANN I E.An alternate MILP model for short-term scheduling of batch plants with preordering constraints[J].Industrial and Engineering Chemistry Research,(1996),35:338-342.
[11]KARIMI L A,MCDONALD C M.Planning and scheduling of parallel semicontinuous processes.Part 2.Short-term scheduling[J].Industrial and Engineering Chemistry Research,1997,36:2701-2714.
[12]LAMBA N,KARIMI L A.Scheduling parallel production lines with resource constraints.Part 1.Model formulation[J].Industrial and Engineering Chemistry Research,2002,41:779-789.
[13]LAMBA N,KARIMI I A.Scheduling parallel production lines with resource constraints. Part 2. Decomposition algorithm[J]. Industrial and Engineering Chemistry Research, 2002, .41: 790-800.
[14] Mendez C A, Grossmann I E. Harjunkoski I, Kabore P, A simultaneous optimization approach for off-line blending and scheduling of oil-refinery operations[J]. Computers and Chemical Engineering, 2006, 30: 614-634.
[15] CASTRO P, BARBOSA-POVOA A P F D, Matos H. An improved RTN continuous-time formulation for the short-term scheduling of multipurpose batch plants[J]. Industrial and Engineering Chemistry Research, 2001, 40:2059-2068.
[16] MAJOZI T, ZHU X X. A novel continuous-time milp formulation for multipurpose batch plants, part 1: short-term scheduling[J]. Industrial and Engineering Chemistry Research, 2001, 40: 5935-5949.
[17] LEE K, PARK H I, LEE I. A novel nonuniform discretetime formulation for short-term scheduling of batch and continuous processes[J]. Industrial and Engineering Chemistry Research, 2001,40: 4902-4911.
[18] BURKARD R E, FORTUNA T, HURKENS C A J. Makespan minimization for chemical batch processes using non-uniform timegrids[J]. Computers and Chemical Engineering, 2002, 26: 1321-1332.
[19] WANG S, GUIGNARD M. Redefining event variables for efficient modeling of continuous-time batch processing[J]. Annals of Operations Research, 2002,116: 113-126.
[20] IERAPETRITOU M G, FLOUDAS C A. Effective continuous-time formulation for short-term scheduling. Part 1. Multipurpose batch processes.Industrial and Engineering Chemistry Research, 1998a, 37: 4341-4359.
[21] IERAPETRITOU M G, FLOUDAS C A. Effective continuous-time formulation for short-term scheduling. Part 2. Continuous and semi-continuous processes[J]. Industrial and Engineering Chemistry Research, 1998b, 37: 4360-4374.
[22] IERAPETRITOU M G, HENE T S, FLOUDAS C A. Effective continuous-time formulation for short-term scheduling. Part 3. Multiple intermediate due dates[J]. Industrial and Engineering Chemistry Research, 1999, 38: 3446-3461.
[23] LIN X, AND FLOUDAS C A. Design, synthesis and scheduling of multipurpose batch plants via an effective continuous-time formulation[J].Computers and Chemical Engineering, 2001, 25: 665-670.
[24] IERAPETRITOU M G, FLOUDAS C A. Comments on "an improved RTN continuous-time formulation for the short-term scheduling of multipurpose batch plants" [J]. Industrial and Engineering Chemistry Research, 2001, 40:5040-5041.
[25] SAHINIDIS N V. Optimization under uncertainty: state-of-the-art and opportunities[J]. Computers and Chemical Engineering, 2004, 28: 971-983.
[26] SHAPIRO A, HOMEM-DE-MELLO T. A simulation-based approach to two-stage stochastic programming with recourse[J]. Mathematical Programming,1998,81:301-325.
[27] LINDEROTH J, SHAPIRO A, WRIGHT S. (2002). The empirical behavior of sampling methods for stochastic programming. Technical report, University of Wisconsin, Madison, WI.
[28] BASSETT M H, PEKNY J F, REKLAITIS G.V. Using detailed scheduling to obtain realistic operating policies for a batch processing facility[J]. Industrial and Engineering Chemistry Research, 1997,36: 1717-1726.
[29] VIN J P, IERAPETRITOU M G. A new approach for efficient rescheduling of multiproduct batch plants[J]. Industrial and Engineering Chemistry Research,2000, 39: 4228-4238.
[30] ACEVEDO J, PISTIKOPOULOS E N. Stochastic optimization based algorithms for process synthesis under uncertainty[J]. Computers and Chemical Engineering, 1998, 22: 647-671.
[31] CLAY R L, GROSSMANN I E. A disaggregation algorithm for the optimization of stochastic planning models[J]. Computers and Chemical Engineering, 1997, 21: 751-774.
[32] GUPTA A, MARANAS C D. A two-stage modeling and solution framework for multisite midterm planning under demand uncertainty[J]. Industrial and Engineering Chemistry Research, 2000, 39: 3799-3813.
[33] LIU M L, SAHINIDIS N V. Optimization in process planning under uncertainty[J]. Industrial and Engineering Chemistry Research, 1996, 35: 4154-4165.
[34] LI L S, LAI K K. A fuzzy approach to the multiobjective transportation problem[J]. Computers and Operations Research, 2000, 27: 43-57.
[35] SAKAWA M, NISHIZAKI I, UEMURA Y. A decentralized two-level transportation problem in a housing material manufacturer: Interactive fuzzy programming[J]. European Journal of Operational Research, 2002, 141:167-185.
[36J Balasubramanian J, Grossmann 1 E. Scheduling optimization under uncertaintys- An alternative approach[J]. Computers and Chemical Engineering, 2003, 27:469-490.
[37] LIN 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: 1069-1085.
[38] FLOUDAS C A, LIN X. Mixed integer linear programming in process scheduling: modeling, algorithms, and applications[J]. Annuals of Operation Research, 2005, 139, 131-162.
[39] HONKOMP S J, MOCKUS L, REKLAIT1S G V. A framework for schedule evaluation with processing uncertainty[J]. Computers and Chemical Engineering, 1999,23: 595-609.
[40] ROSLOF J, HARJUNKOSKI I, BJORKQVIST J, KARLSSON S. An MILP-based reordering algorithm for complex industrial scheduling and rescheduling[J]. Computers and Chemical Engineering, 2001, 25: 821-828.
[41] JANAK S L, AND FLOUDAS C A. Production scheduling of a large-scale industrial batch plant. ii. reactive scheduling[J]. Industrial and Engineering Chemistry Research, 2006, 45: 8253-8269.
[42] MENDEZ C A, CERDA J. Dynamic scheduling in multiproduct batch plants[J].Computers and Chemical Engineering, 2003, 27: 1247-1250.
[43] Mendez C A, Cerda J. An MILP framework for batch reactive scheduling with limited discrete resources[J]. Computers and Chemical Engineering, 2004, 28:1059-1067.
[44] AYTUG H, LAWLEY M A, MCKAY K, MOHAN S, UZSOY R. Executing production scheduling in the face of uncertainties: a review and some future directions[J]. European Journal of Operational Research, 2005, 161: 86-110.
[45] RYN J H, DUA V, PISTIKOPOULOS E N. A bilevel programming framework for enterprise-wide process networks under uncertainty[J]. Computers and Chemical Engineering, 2004, 28: 1121-1129.
[46] ADHITYA A, SRINIVASAN R, KARIMI I A. Heuristic rescheduling of crude oil operationsto manage abnormal supply chain events[J]. AICHE Journal,2007,53: 397-422.
[47] TILL J, SAND G, URSELMANN M, ENGELL S A. hybrid evolutionary algorithm for solving two-stage stochastic integer programs in chemical batch scheduling[J]. Computers and Chemical Engineering, 2007, 31: 630-647.
[48]DEPAOLO C A,RADER D J.A heuristic algorithm for a chance constrained stochastic program[J].European Journal of Operational Research,2007,176:27-45.
[49]SAKAWA M,NISHIZAKI I,UEMURA Y.A decentralized two-level transportation problem in a housing material manufacturer:Interactive fuzzy programming[J].European Journal of Operational Research,2002,141:167-185.
[50]ORCUN S,ALTINEL I K.HORTACSU(O|¨).Scheduling of batch processes with operational uncertainties[J].Computers and Chemical Engineering,1996,20:1191-1196.
[51]JOHNSON E L,NEMHAUSER G L.Recent developments and future directions in mathematical Programming[J].IBM Systems Journal,31:79-93.
[52]LAND A H,DOIG A G.An automatic method for solving discrete programming problems[J].Econometrica,1960,28:497-520.
[53]JOHNSON E L,NEMHAUSER G L,SAVELSBERGH M P.Progress in linear programming-based algorithms for integer programming:an exposition[J].INFORMS Journal on Computing,2000,12,1201-1222.
[54]NEMHAUSER G L,WOLSEY L A.integer and combinatorial optimization[M].John Wiley,New York,1988.
[55]马振华,刘坤林,.陆璇.运筹学与最优化理论[M].清华大学出版社,1998.
[56]甘应爱,田丰,李维铮.运筹学[M].清华大学出版社,2005.
[57]KALLRATH J.Mixed integer optimization in the chemical process industry experience,potential and future perspectives[J].Trans IChemE,2000,78,809-822.
[58]GUPTA O K.RAVINDRAN V.Branch and bound experiments in convex nonlinear integer programming[J].Management Science,1985,31:1533-1546
[59]GEOFFRION A M.Generalized Benders Decomposition[J].Journal of Optimization Theory and Applications,1972,10,237-260.
[60]DURAN M A,GROSSMANN I E.An outer-approximation algorithm for a class of mixed-integer nonlinear programs[J],mathematical programming,1986,36,307-312.
[61]GROSSMANN I E,HEEVER S A,HARJUNKOSKI I.discrete optimization methods and their role in the integration of planning and scheduling[J].AIChE Symposium 2002,98:150-168.
[62] IYER R R, GROSSMANN 1. E. A bilevel decomposition algorithm for longe-range planning of process networks[J]. Industrial and Engineering Chemistry Research, 1998,37:474-492.
[63] IYER R R, GROSSMANN I. E. Synthesis and operational planning of utility systems for multiperiod operation[J]. Computers and Chemical Engineering,1998,22:979-993.
[64] PAPAGEORGIOU L G, PANTELIDES C C. Optimal campaign planning/scheduling of multipurpose batch/semi-continuous plants, 2 A mathematical decomposition approach[J]. Industrial and Engineering Chemistry Research,1996,35:510-529.
[65] DOGAN M E, GROSSMANN I E. A decomposition method for the simultaneous planning and scheduling of single stage continuous multiproduct plants[J]. Industrial and Engineering Chemistry Research, 45: 299-315.
[66] FISHER M L. The Lagrangean relaxation method for solving integer programming problems[J]. Management Science, 1981, 27(1): 1-17.
[67] GUIGNARD M, KIM S. Lagrangean decomposition: A model yielding stronger lagrangean bounds[J]. Mathematic Programming, 1987, 39: 215-228.
[68] DAN WU, IERAPETRITOU M G. Decomposition approaches for the efficient solution of short-term scheduling problems[J]. Computers and Chemical Engineering, 2003,27: 1261-1276.
[69] DAN WU, IERAPETRITOU M G. Lagrangean decomposition using an improved Nelder-Mead approach for Lagrangean multiplier update[J].Computers and Chemical Engineering, 2006, 30: 778-789.
[70] HEEVER S A, GROSSMANN I E.A strategy for the integration of production planning and reactive scheduling in the optimization of a hydrogen supply network[J]. Computers and Chemical Engineering, 2003, 27: 1813-1839.
[71] ROGERS D F, PLANTE R D, WONG R T, EVANS J R. Aggregation and disaggregation techniques and methodology in optimization[J]. Operation Research, 1991,39:553-574.
[72] ERMOLIEV Y M, KRYAZHIMSKII A V, Ruszcynski A. Constraint aggregation principle in convex optimization[J]. Mathematic Programming,1997, 76: 353-372.
[73] WILKINSON S J, CORTIER A, SHAH N, PANTELIDES C C. Integrated production and distribution scheduling on a Europe-wide basis[J]. Computers and Chemical Engineering, 1996,20: 1275-1280.
[74]HEEVER,S A,GROSSMANN I E.an iterative aggregation/disaggregation approach for the solution of a mixed-integer nonlinear oilfield infrastructure planning model[J].Industrial and Engineering Chemistry Research,2000,39:1955-1971.
[75]BLOMER F,GUNTHER H O.Scheduling of a multi-product batch process in the chemical industry[J].Computers in Industry,1998,36:245-259.
[76]BYLKA S,REMPA R.Heuristics for impulse replenishment with continuous periodic demand[J].International Journal of Production Economics,2004.88:183-190.
[77]CHERN C C,HSIEH J S.A heuristic algorithm for master planning that satisfies multiple objectives[J].Computers and Operations Research,2007,34:3491-3513.
[78]WITT A,STEFAN V.Simple heuristics for scheduling with limited intermediate storage[J].Computers and Operations Research,2007,34:2293-2309.
[79]钱晓龙,唐立靳,刘文新.动态调度的研究方法综述[J].控制与决策,2001,16(2):141-145.
[80]KIMMS A.Competitive methods for multi-level lot sizing and scheduling:Tabu search and randomized regrets[J].International Journal of Production Research,1996,34:2279-2298.
[81]ZHANG C Y,LIP G,GUAN Z L,RAO Y Q.A tabu search algorithm with a new neighborhood structure for the job shop scheduling problem[J].Computers and Operations Research,2007,34:3229-3242.
[82]CHEN L,BOSTEL N,DEJAX P,CAI J G,XI L F.A tabu search algorithm for the integrated scheduling problem of container handling systems in a maritime terminal[J].European Journal of Operational Research,2007,181:40-58.
[83]ARULDOSS T A V,EBENEZER J A.A tabu search based hybrid optimization approach for a fuzzy modelled unit commitment problem[J].Electric Power Systems Research,2006,76:413-425.
[84]王凌.车间调度及其遗传算法[M].北京:清华大学出版社,2003.
[85]ZHANG C Y,LIP G,RAO Y Q,GUAN Z L.A very fast TS/SA algorithm for the job shop scheduling problem[J].Computers and Operations Research,2008,35:282-294.
[86]DAHAL K P,AND CHAKPITAK N.Generator maintenance scheduling in power systems using metaheuristic-based hybrid approaches[J].Electric Power Systems Research,2007,77:771-779.
[87]SADEGHEIH A.Sequence optimization and design of allocation using GA and SA[J].Applied Mathematics and Computation,2007,186:1723-1730.
[88]CHEN J S,PAN J C H,LIN C M.A hybrid genetic algorithm for the re-entrant flow-shop scheduling problem[J].Expert Systems with Applications,2008,34:570-577.
[89]CHATFIELD D C.The economic lot scheduling problem:A pure genetic search approach[J].Computers and Operations Research,2007.34:2865-2881.
[90]HU X B,CHEN W H,Genetic algorithm based on receding horizon control for arrival sequencing and scheduling[J].Engineering Applications of Artificial Intelligence,2005,18:633-642.
[91]WANG K,LOHL T,STOBBE M,ENGELS.A genetic algorithm for onlinescheduling of a multiproduct polymer batch plant[J].Computers and Chemical Engineering,2000,24(2-7):393-400.
[92]HU X H,EBERHART R.Solving constrained nonlinear optimization problems with particle swarm optimization[J].Proceedings of the Sixth World Multiconference on Systemics,Cybernetics and Informatics(SCI 2002),Orlando,USA.
[93]REKLAITIS G V.Scheduling approaches for the batch process industries[J].ISA Transactions,1995,34(4):349-358.
[94]ARTIBA A,RIANE F.An application of a planning and scheduling multimodel approach in the chemical industry[J].Computer in Industry,1998,36:209-229.
[95]王伟达,王伟,刘文剑.基于仿真的生产计划与调度系统集成[J].计算机工程与设计,2007,28,55-72.
[96]PFEIFFER A,KADAR B,MONOSTORI L.Stability-oriented evaluation of rescheduling strategies,by using simulation[J].Computers in Industry,2007,58:630-643.
[97]伍乃骐,白丽平.炼油生产计划和调度优化的研究[J].计算机集成制造系统2005,11(1):90-96.
[98]廖良才.成品油调和调度优化模型及其应用研究[D].长沙:国防科学技术大学,2003.
[99]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.
[100]SHAH N.Mathematical programming techniques for crude oil scheduling[J].Computers and Chemical Engineering,1996,20:1227-1232.
[101]LEE H.,PINTO J M,GROSSMANN I E,PARK S.Mixed-Integer programming model for re(?)nery short term scheduling of crude oil unloading with inventory management[J].Industrial and Engineering Chemistry Research,1996,35:1630-1641.
[102]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.
[103]JIA Z,IEARAPETRITOU M.Refinery Short-Term Scheduling Using Continuous Time Formulation:Crude-Oil Operations[J].Industrial Engineering and Chemistry Research,2003,42:3085-3097.
[104]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.
[105]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.
[106]DAHAL K P,ALDRIDGE C J,MCDONALD J R,BURT G M.A GA-based technique for the scheduling of storage tanks.Proceeding of Congress of Evolutionary Computation,1999:2199-2206.
[107]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.
[108]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.
[109]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.
[110]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.
[111]G(O|¨)THE-LUNDGREN M,LUNDGREN J T.An optimization model for refinery production scheduling[J].International Journal of Production Economics,2002,78:255-270.
[112]PERSSONY J A,GOTHE-LUNDGREN M,LUNDGREN J T,GENDRON B.A tabu search heuristic for scheduling the production processes at an oil refinery[J].International Journal of Production Economics,2004,42:445-471.
[113]丁锋,杨家本,兰鸿森.连续过程生产调度5日滚动作业计划的研究[J].系统工程理论与实践,1998,18(11):11-19.
[114]杜江,王波兴,周煌.基于优化技术的生产调度专家系统研究[J].数学杂志,1998,18:121-124.
[115]杜江 周济,王波兴等.CIPS环境下的生产调度专家系统研究[J].计算机工程与设计,1999,20(4):29-33.
[116]姚建初,周济,刘伯龙.炼油生产调度专家系统的应用研究[J].计算机工程与应用,2000,10:174-176.
[117]姚建初,刘伯龙,李卫国.流程工业管控一体化系统的研究与开发[J].自动化博览 管控一体化技术,2000,(S1):58-60.
[118]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.
[119]GLISMANN K,GRUHN G.Short-term scheduling and recipe optimization of blending processes[J].Computers and Chemical Engineering,2001,25:627-634.
[120]JIA Z,IEARAPETRITOU,M.Mixed-integer linear programming for gasoline blending and distribution scheduling[J].Industrial Engineering and Chemistry Research,2003,42:825-835.
[121]NEIRO S M S,AND PINTO J M.A general modeling framework for the operational planning of petroleum supply chains[J].Computers and Chemical Engineering,2004,28:871-896.
[122]KELLY J D,Next-generation refinery scheduling technology,http://www.dashop timiz ation com/home/downloads/pdf/npra2003.pdf.
[123]RAMAN R,GROSSMANN I E.Relation between MILP modeling and logical inference for chemical process synthesis[J].Computers and Chemical Engineering,1991,15:73-84.
[124]GROSSMANN I E.Logic-based modeling and solution for discrete/continuous problems in process systems engineering,http://cepac,cheme,cmu,edu/pasilect ures,htm.
[125]FLOUDAS C A, LIN X. Continuous-time versus discrete-time approaches for scheduling of chemical processes: a review[J]. Computers and Chemical Engineering, 2004,28: 2109-2129.
[126]MORO L F L, PINTO J M. Mixed-Integer programming ppproach for short-term crude oil scheduling[J]. Industrial and Engineering Chemistry Research,2004,43:85-94.
[127]CHRYSSOLOURIS G, PAPAKOSTAS N. Refinery short-term scheduling with tank farm, inventory and distillation management: An integrated simulation-based approach[J]. European Journal of Operational Research, 2005,166: 812-827.
[128]HENNING G P, JAIME C. Knowledge-based predictive and reactive schedule-ing in industrial environments[J]. Computers and Chemical Engineering,2000,24:2315-2338.
[129]BASSETT M H, DAVE P, DOYLE F J. Perspectives on model based integration of process operations[J]. Computers and Chemical Engineering, 1996,20:821-844.
[130]BIREWAR D B, GROSSMANN I E. Simultaneous production planning and scheduling of multiproduct batch plants[J]. Industrial and Engineering Chemistry Research, 1990, 29: 570-593.
[131] GUILLEN G, BADELL M, ESPUNA A, PUIGJANER L. Simultaneous optimization of process operations and financial decisions to enhance the integrated planning/scheduling of chemical supply chains[J]. Computers and Chemical Engineering,.2006, 30: 421-436.
[132]CHENG L, SUBRAHMANIAN E, WESTERBERG A W. A comparison of optimal control and stochastic programming from a formulation and computation perspective[J]. Computers and Chemical Engineering, 2004, 29:149-164.
[133] JANAK S L, LIN X, FLOUDAS C A. A new robust optimization approach for scheduling under uncertainty II. Uncertainty with known probability distribution[J]. Computers and Chemical Engineering, 2007, 31:171-195.
[134] LEUNG S C H, TSANG S O S, NG W L, WU Y. A robust optimization model for multi-site production planning problem in an uncertain environment[J].European Journal of Operational Research, 2007, 181: 224-238.
[135]徐用懋,张猛.流程工业的综合自动化技术[J].工业控制计算机, 2002, 15:5-10.
[136]李慧莹,柴天佑.钢铁企业扁半化管理模式下的CIMS体系结构[J].东北大学学报(自然科学版),2002.23:746-749.
[137]柴天佑,金以慧.基于三层结构的流程工业现代集成制造系统[J].控制工程,2002,9:1-6.
[138]赵小强.炼油企业生产调度问题研究[D].浙江:浙江大学信息科学与工程学院,2005:77-89.