炼钢—连铸生产计划与调度的优化方法研究及应用
|
摘要
随着全球化市场竞争的日益加剧,顾客的需求日益多品种小批量化而且对交货期的要求越来越严格,钢铁企业正面临着越来越严峻的挑战,因此,钢铁生产中的生产计划、性能预报、质量管理等问题引起了学术界的广泛关注。炼钢-连铸在钢铁生产的整个工序中的地位十分关键,很多文献对炼钢-连铸的生产计划问题展开了研究,然而由于钢铁生产的复杂性,很多研究结果在生产实践中只能作为参考。针对炼钢-连铸中的生产计划问题尽管已经提出了很多研究方法,但是应用基于仿真的优化的研究还未见报道。
因此,本文应用基于仿真的优化等方法对炼钢-连铸生产计划中的若干关键问题展开了研究,主要包括如下一些内容：
(1)查阅了大量的专著、期刊、会议论文集、研究报告等相关文献,对相关领域的研究现状进行了综述,包括：钢铁生产的工艺流程与主要管理模式；钢铁生产管理中的物料采购与优化、生产计划与调度、生产质量管理等问题,并着重综述了生产计划与调度方面的研究；还综述了主要的相关研究方法,并着重介绍了继数学规划和智能优化方法之后出现的基于仿真的优化(Simulation Based Optimization,简称SBO)的发展现状、主要理论工作及应用领域等。
(2)提出了炼钢组炉问题的一个多目标优化模型并开发了求解算法。该模型针对单个用户的需求量可能大于单炉容量的实际需求,以余材量和完成合同需要的总成本为目标,其中考虑了钢级之间的替代成本。对模型进行分析和化简之后,开发了一个嵌入禁忌表的求解算法。最终的仿真算例表明了模型和算法的有效性,并分析了余材量和合同需求量之间的关系,这种方法得到的非劣解分散性较好,能够为决策者提供较大的决策空间。
(3)借助图论的方法建立了组浇计划的一个数学模型并开发了基于遗传算法的求解算法。假设所有炉次都必须安排浇次、浇次数目未知、所有连铸机相同,该组浇问题被描述成为一个多城市类型且城市间距离不对称的多旅行商问题,目标函数极小化旅行商的总行程和参与的旅行商数目,单个旅行商访问的城市有最大数目限制。开发了一个带分隔符的顺序编码、单切点顺序交叉的遗传算法,并通过随机生成的算例对算法的性能进行了测试,讨论了与成本相关的参数的设定方法,分析了参数对结果的影响,以及将SBO方法引入其中的必要性与可行性。
(4)开发了作业计划的一个基于仿真的遗传算法。借助数学模型对炼钢-连铸作业计划(调度)问题进行了描述,其中以连铸机的最早可开浇时刻与实际开浇时刻的最大间隔最小化为目标。基于SBO方法开发了一个求解算法,其中优化器采用分段顺序编码的遗传算法,简洁明了易于遗传操作,仿真器不仅仿真实际系统的运行,也仿真现场人员的一些操作,仿真速度较快。源于文献的仿真案例表明这种方法便于考虑实践中的不确定因素,能使得作业计划更贴近生产实践。
(5)设计、开发了一个炼钢-连铸集成调度系统并应用于生产实践。该系统采用客户端/中间件/服务器的三层体系结构,其中中间件基于TUXEDO开发,核心功能模块包括调度的确定与调整、计划的匹配与优化、异常物流的处理等,核心算法采用“人机交互+优化算法”的模式。系统具有如下特点：可与MES系统无缝集成、人机界面友好、可维护性好。此外,本文还提出了一个基于SBO方法研究炼钢-连铸中长期计划的框架,包括计划期精细粒度的划分问题、不同计划之间的接口问题、计划的评价体系等。
The iron and steel enterprises are facing stronger and stronger challenges as the global completion in market is enhanced. Meanwhile, the customers demand small lot with different varieties and strict delivery time windows. Therefore, more and more attentions are paid to the issues such as production planning, productivity forecast and quality management in the iron and steel corporations. The steelmaking and continuous-casting (SCC) production is very important in all the processing stages of steel production. As a result, many types of production planning problems in the SCC production are researched in the literature. However, the majority of the results in the literature cannot be applied and only be referenced in actural application because the SCC production process is very complex. In spite of variety approaches that are developed for the production planning problems in the SCC process, the application of the simulation based optimization (SBO) has not been found until now.
Therefore, several key problems in the SCC process planning are researched using the approaches including the SBO approach in this thesis. The main researches are as follows:
(1) A large quantity of monographs, journal articles, proceeding papers and reports are carefully checked. The following related fields are reviewed:the whole sequence of processing stages in the iron and steel productin; the existing management modes; the management problems in the iron and steel production such as materials purchase and optimization, production quality management and particularly production planning and scheduling. Also, the related approaches, especially the development, theoretical and applied researches of the SBO approach are summarized.
(2) A multi-objective mathematical model of the charge batching problem in steclmaking is proposed with its solution algorithm developed. The model minimizes the open ordered slabs and the total costs including the substitute costs between grades respectively with the assumption that the weight of a single customer is perhaps greater than a charge. A heuristic algorithm with an embedded tabu list is developed after the analyses and simplification of the model. The final experiments indicate the validity of the algorithm. Also, the relationship between the obtained open ordered slabs and the given customer weights is analyzed. The Pareto solutions scatter well, which provides great space for the decision makers.
(3) A graph-based mathematical model for the cast batching problem is built and solved based on the genetic algorithm (GA). It is assumed that all the charges must be assigned, that number of casts is unknown, and that all the casters (tundishes) are homogeneous. The problem is formulated as a multi-city-type asymmetric multiple traveling salesmen problem, which minimizes both the total traveling distances and the number of involved traveling salesmen with the constraint of maximum number of cities that can be visited by one salesman. A GA based algorithm is developed. The algorithm uses an ordered encodeing with separators and ordered crossover with single corss point. The GA algorithm is tested based on a number of randomly generated instances with the valuing method of parameters discussed. Finally, the effects of paremeters on the results and the necessity and feasibility of introducing the SBO method are analyzed.
(4) A simulation based GA algorithm for the job scheduling problem is developed. With the help of mathematical model, the job scheduling problem in SCC process is formulated, the objective function of which minimizes the maximum gap between the earliest available time and starting casting time of casters. The problem is solved based on the SBO method. The optimizer of the SBO is a segmented ordered encoding GA, which is easy for genetic operation. The simulator of the SBO simulates not only the actual system but also the operators, which runs fast. Finally the experiments show that it is convenient to consider the uncertain factors in this method and that the obtained scheduling is much closer to the actual system.
(5) An integrated scheduling system for the SCC process is designed, developed and applied. The system is based on the three-layer architecture as client, middle ware, and server. The middle ware is realized using TUXEDO. The main modules of the system consist of scheduling and re-scheduling, match and optimization of planning, and the handling of abnormal logistics. The core algorithm is the hybrid of optimization and human-interactive method. The system has such features as friendly human-machine interface, seamlessly integration with the MES system, and easy maintenance. Furthermore, the author proposes a research frame work of SBO for the medium- or long-term planning in the SCC production, including the division of planning period, the interface between plans and the evaluation system.
因此,本文应用基于仿真的优化等方法对炼钢-连铸生产计划中的若干关键问题展开了研究,主要包括如下一些内容：
(1)查阅了大量的专著、期刊、会议论文集、研究报告等相关文献,对相关领域的研究现状进行了综述,包括：钢铁生产的工艺流程与主要管理模式；钢铁生产管理中的物料采购与优化、生产计划与调度、生产质量管理等问题,并着重综述了生产计划与调度方面的研究；还综述了主要的相关研究方法,并着重介绍了继数学规划和智能优化方法之后出现的基于仿真的优化(Simulation Based Optimization,简称SBO)的发展现状、主要理论工作及应用领域等。
(2)提出了炼钢组炉问题的一个多目标优化模型并开发了求解算法。该模型针对单个用户的需求量可能大于单炉容量的实际需求,以余材量和完成合同需要的总成本为目标,其中考虑了钢级之间的替代成本。对模型进行分析和化简之后,开发了一个嵌入禁忌表的求解算法。最终的仿真算例表明了模型和算法的有效性,并分析了余材量和合同需求量之间的关系,这种方法得到的非劣解分散性较好,能够为决策者提供较大的决策空间。
(3)借助图论的方法建立了组浇计划的一个数学模型并开发了基于遗传算法的求解算法。假设所有炉次都必须安排浇次、浇次数目未知、所有连铸机相同,该组浇问题被描述成为一个多城市类型且城市间距离不对称的多旅行商问题,目标函数极小化旅行商的总行程和参与的旅行商数目,单个旅行商访问的城市有最大数目限制。开发了一个带分隔符的顺序编码、单切点顺序交叉的遗传算法,并通过随机生成的算例对算法的性能进行了测试,讨论了与成本相关的参数的设定方法,分析了参数对结果的影响,以及将SBO方法引入其中的必要性与可行性。
(4)开发了作业计划的一个基于仿真的遗传算法。借助数学模型对炼钢-连铸作业计划(调度)问题进行了描述,其中以连铸机的最早可开浇时刻与实际开浇时刻的最大间隔最小化为目标。基于SBO方法开发了一个求解算法,其中优化器采用分段顺序编码的遗传算法,简洁明了易于遗传操作,仿真器不仅仿真实际系统的运行,也仿真现场人员的一些操作,仿真速度较快。源于文献的仿真案例表明这种方法便于考虑实践中的不确定因素,能使得作业计划更贴近生产实践。
(5)设计、开发了一个炼钢-连铸集成调度系统并应用于生产实践。该系统采用客户端/中间件/服务器的三层体系结构,其中中间件基于TUXEDO开发,核心功能模块包括调度的确定与调整、计划的匹配与优化、异常物流的处理等,核心算法采用“人机交互+优化算法”的模式。系统具有如下特点：可与MES系统无缝集成、人机界面友好、可维护性好。此外,本文还提出了一个基于SBO方法研究炼钢-连铸中长期计划的框架,包括计划期精细粒度的划分问题、不同计划之间的接口问题、计划的评价体系等。
The iron and steel enterprises are facing stronger and stronger challenges as the global completion in market is enhanced. Meanwhile, the customers demand small lot with different varieties and strict delivery time windows. Therefore, more and more attentions are paid to the issues such as production planning, productivity forecast and quality management in the iron and steel corporations. The steelmaking and continuous-casting (SCC) production is very important in all the processing stages of steel production. As a result, many types of production planning problems in the SCC production are researched in the literature. However, the majority of the results in the literature cannot be applied and only be referenced in actural application because the SCC production process is very complex. In spite of variety approaches that are developed for the production planning problems in the SCC process, the application of the simulation based optimization (SBO) has not been found until now.
Therefore, several key problems in the SCC process planning are researched using the approaches including the SBO approach in this thesis. The main researches are as follows:
(1) A large quantity of monographs, journal articles, proceeding papers and reports are carefully checked. The following related fields are reviewed:the whole sequence of processing stages in the iron and steel productin; the existing management modes; the management problems in the iron and steel production such as materials purchase and optimization, production quality management and particularly production planning and scheduling. Also, the related approaches, especially the development, theoretical and applied researches of the SBO approach are summarized.
(2) A multi-objective mathematical model of the charge batching problem in steclmaking is proposed with its solution algorithm developed. The model minimizes the open ordered slabs and the total costs including the substitute costs between grades respectively with the assumption that the weight of a single customer is perhaps greater than a charge. A heuristic algorithm with an embedded tabu list is developed after the analyses and simplification of the model. The final experiments indicate the validity of the algorithm. Also, the relationship between the obtained open ordered slabs and the given customer weights is analyzed. The Pareto solutions scatter well, which provides great space for the decision makers.
(3) A graph-based mathematical model for the cast batching problem is built and solved based on the genetic algorithm (GA). It is assumed that all the charges must be assigned, that number of casts is unknown, and that all the casters (tundishes) are homogeneous. The problem is formulated as a multi-city-type asymmetric multiple traveling salesmen problem, which minimizes both the total traveling distances and the number of involved traveling salesmen with the constraint of maximum number of cities that can be visited by one salesman. A GA based algorithm is developed. The algorithm uses an ordered encodeing with separators and ordered crossover with single corss point. The GA algorithm is tested based on a number of randomly generated instances with the valuing method of parameters discussed. Finally, the effects of paremeters on the results and the necessity and feasibility of introducing the SBO method are analyzed.
(4) A simulation based GA algorithm for the job scheduling problem is developed. With the help of mathematical model, the job scheduling problem in SCC process is formulated, the objective function of which minimizes the maximum gap between the earliest available time and starting casting time of casters. The problem is solved based on the SBO method. The optimizer of the SBO is a segmented ordered encoding GA, which is easy for genetic operation. The simulator of the SBO simulates not only the actual system but also the operators, which runs fast. Finally the experiments show that it is convenient to consider the uncertain factors in this method and that the obtained scheduling is much closer to the actual system.
(5) An integrated scheduling system for the SCC process is designed, developed and applied. The system is based on the three-layer architecture as client, middle ware, and server. The middle ware is realized using TUXEDO. The main modules of the system consist of scheduling and re-scheduling, match and optimization of planning, and the handling of abnormal logistics. The core algorithm is the hybrid of optimization and human-interactive method. The system has such features as friendly human-machine interface, seamlessly integration with the MES system, and easy maintenance. Furthermore, the author proposes a research frame work of SBO for the medium- or long-term planning in the SCC production, including the division of planning period, the interface between plans and the evaluation system.
引文
1.胡琨元.钢铁企业三层结构CIMS中的生产计划方法研究[D].沈阳：东北大学,2003.
2. Vessell C. Computer integrated manufacturing, and small and medium enterprises[J]. Computers and Industrial Engineering,1999,37(1):425-428.
3. Nagalingam S V, Lin G C I. Latest developments in CIM[J]. Robotics and Computer Integrated Manufacturing,1999,15(6):423-430.
4.卢和煜,朱景力,朱立平.宝钢的物资管理[M].北京：冶金工业出版社,中国人民大学出版社,1998.
5.包燕平,冯捷.钢铁冶金学教程[M].北京：冶金工业出版社,2008.
6. Fu P, Zhang Q. Investigation on steelmaking dust recycling and iron oxide red preparing[J]. Journal of University of Science and Technology Beijing, Mineral, Metallurgy, Material,2008,15(1):24-28.
7.高振,唐立新,陶炜,肖新华.大型钢铁企业原料采购计划模型[J].系统工程学报,2003,06).
8.罗治洪,唐立新,张悟移.钢铁原料物流计划问题的建模与求解[J].系统工程理论与实践,2008,05).
9. Oyama S, Kokubo T, Ishida T. Domain-specific web search with keyword spices[J]. IEEE Transactions on knowledge and data engineering,2004,16(1):17-27.
10-陈俊杰,薛云.基于Agent的元搜索引擎的研究与设计[J].计算机工程与应用,2003,39(010)：33-36.
11.张卫丰,徐宝文,周晓宇,许蕾,李东.元搜索引擎结果生成技术研究[J].小型微型计算机系统,2003,24(1)：34-37.
12.王铮,王庆,汪定伟.电子商务中基于Hopfield神经网络的货源信息搜索方法[J].东北大学学报：自然科学版,2008,29(002)：185-188.
13. Wang Z, Wang Q, Wang D W. Bayesian network based business information retrieval model[J]. Knowledge and Information Systems,2009,20(1):63-79.
14.汪定伟,王庆,宫俊,万福才.双边多轮价格谈判过程的建模与分析[J].管理科学学报,2007,10(001)：94-98.
15. Dingwei Wang, Kaku I. Modelling and Analysis of Bargaining Process for E-Procurement of Large Enterprise Group[C]. Proceedings of the 17th World Congress The International Federation of Automatic Control,2008:1639-1644.
16.杜斌.宝钢炼钢,连铸自动化技术研发的回顾与展望[J].冶金自动化,2006,S2(1)：10-17.
17.卢克斌,黄可为,汪定伟,张瑞友.钢铁企业合同计划与余材匹配的集成优化方法[J].控制与决策,2009,24(01)：71-75.
18.胡琨元,高政威,汪定伟.钢铁企业合同匹配多目标优化模型与算法[J].东北大学学报(自然科学版),2004,25(06)：527-530.
19. Salustowicz R, Schmidhuber J. Probabilistic incremental program evolution[J]. Evolutionary Computation,1997,5(2):123-141.
20.王高鹏,窦丽华,陈杰,张娟,陈晨.一种基于PBIL算法和变焦算法的混合算法[J].模式识别与人工智能,2009,22(001)：1-7.
21.胡琨元,常春光,郑秉霖,汪定伟.钢铁企业中库存匹配与生产计划联合优化模型与算法[J].信息与控制,2004,33(02)：177-180,196.
22.胡琨元,陈文明,汪定伟,郑秉霖.考虑批量因素的成品匹配与生产计划联合优化[J].系统工程理论方法应用,2004,13(003)：199-202.
23. Iosif A-M, Hanrot F, Ablitzer D. Process integrated modelling for steelmaking Life Cycle Inventory analysis[J]. Environmental Impact Assessment Review,2008,28(7):429-438.
24.黄可为,卢克斌,汪定伟.炼钢组炉问题优化模型及其动态规划算法[J].东北大学学报(自然科学版),2006,27(02)：138-141.
25.唐立新,杨自厚,王梦光.炼钢—连铸最优炉次计划模型与算法[J].东北大学学报(自然科学版),1996,17(04)：440-445.
26.卢克斌,黄可为,张瑞友,汪定伟.炼钢组炉问题的多目标优化模型与算法[J].中南大学学报(自然科学版),2009,增刊1(315-318.
27.王秀英,赵明,任志考.炼钢-连铸组浇计划的两阶段求解算法[J].青岛化工学院学报,2001.
28.李岩,刘建荣,李铁克.基于约束规划的最优浇次计划模型研究[J].冶金自动化,2007,31(003)：28-30.
29.唐立新,杨自厚.炼钢—连铸最优浇次(CAST)计划[J].东北大学学报：自然科学版,1996,17(005)：554-558.
30.唐立新,王梦光.炼钢—连铸对于浇次数未知的最优浇次计划模型与算法[J]·钢铁,1997,32(010)：19-21.
31.彭志刚,吴光宇.用两阶段启发式算法编制连铸生产计划[J].控制与决策,1999,14(A11)：585-588.
32.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)：1109-1112,1116.
33.陈军鹏,陈文明,罗首章,苏冬平,周仁义,庞新富,郑秉霖.CBR在炼钢连铸动态 调度系统中的应用研究[J].冶金自动化,2008,32(002)：29-33.
34.王秀英,柴天佑,郑秉霖.炼钢-连铸智能调度软件的开发及应用[J].计算机集成制造系统,2006,12(008)：1220-1226.
35.郑忠,朱道飞,高小强.钢厂炼钢连铸生产调度及重计划方法[J].重庆大学学报,2008,07)：114-118.
36.郑忠,朱道飞,高小强.基于蚁群算法的炼钢-连铸作业计划编制方法[J].北京科技大学学报,2009,04)：102-108.
37.朱道飞.钢厂生产作业计划与调度的自组织优化方法研究[D].重庆大学,2008.
38.朱道飞,郑忠,高小强.炼钢-连铸作业计划的遗传优化模型[J].钢铁,2008,07)：29-34.
39.俞胜平,王秀英,郑秉霖,柴天佑.基于变约束规划模型的炼钢连铸动态调度[J].控制理论与应用,2009,07)：65-70.
40余丽霞,唐秋华.炼钢-连铸生产作业计划编制及仿真检验方法研究[J].工业控制计算机,2008,11)：42-44.
41. Tang L, Liu J, Rong A, Yang Z. A mathematical programming model for scheduling steelmaking-continuous casting production[J]. European Journal of Operational Research, 2000,120(2):423-435.
42. Tang L, Wang G. Decision support system for the batching problems of steelmaking and continuous-casting production[J]. Omega,2008,36(6):976-991.
43郑秉霖,胡琨元.一体化钢铁生产计划系统的研究现状与展望[J].控制工程,2003,10(001)：6-10.
44.陈雄,徐心和.基于模拟退火轧制批量计划问题的两阶段算法[J].控制理论与应用,1999,16(002)：209-212.
45.黄可为,汪定伟.热轧计划中的多旅行商问题及其计算方法[J].计算机应用研究,2007,24(007)：43-45.
46.覃一宁.冷轧薄板生产计划与调度系统的研究与应用[D].大连理工大学,2007.
47.李建祥,唐立新,庞哈利,王梦光.热轧无缝钢管生产作业计划研究[J].系统仿真学报,2003,09).
48.李建祥,唐立新,庞哈利,王梦光.热轧无缝钢管排产计划研究[J].控制与决策,2004,02).
49.李建祥,唐立新,庞哈利,吴会江.热轧无缝钢管生产计划系统[J].冶金自动化,2003,05).
50.李建祥,唐立新,吴会江.热轧钢管主生产计划模型与算法研究[J].系统工程学报,2005,05).
51.刘彩燕.面向过程集成的钢铁企业质量管理研究[D].大连理工大学,2006.
52.张浩.钢铁制造业集成成本管理方法及应用研究[D].大连理工大学,2007.
53.范晓慧,曾垂喜,姜涛,陈许玲,龙红明,胡友明,张克诚.铁矿石烧结性能预报模型[J].中南大学学报：自然科学版,2005,36(006)：949-954.
54.《运筹学》教材编写组.运筹学[M].清华大学出版社,1990.
55.常春光,胡琨元,汪定伟,郑秉霖,李慧莹.钢铁生产动态调度理论研究与工程应用综述[J].信息与控制,2003,32(6)：531-537.
56.常春光,汪定伟,胡琨元,郑秉霖.嵌入调整模型的CBR技术的炼钢调度系统[J].东北工学院学报,2004,25(006)：531-534.
57.牛秦洲,叶恒舟,吴一峰.烧结厂烧结配料专家系统设计[J].桂林工学院学报,2002,22(004)：391-395.
58. Azadeh A, Ghaderi S F, Tarverdian S, Saberi M. Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption[J]. Applied Mathematics and Computation,2007,186(2):1731-1741.
59. Chau K W. A split-step particle swarm optimization algorithm in river stage forecasting[J]. Journal of Hydrology,2007,346(3-4):131-135.
60. Garcia-Fernandez I, Martin-Guerrero J D, Pla-Castells M, Soria-Olivas E, Martinez-Dura R J, Munoz-Mari J. Crane collision modelling using a neural network approach[J]. Expert Systems with Applications,2004,27(3):341-348.
61. Ghiassi M, Zimbra D K, Saidane H. Medium term system load forecasting with a dynamic artificial neural network model[J]. Electric Power Systems Research,2006, 76(5):302-316.
62. Bistline Sr W G, Banerjee S, Banerjee A. RTSS:an interactive decision support system for solving real time scheduling problems considering customer and job priorities with schedule interruptions[J]. Computers & Operations Research,1998,25(11):981-995.
63. Wang D, Tang Z, Ip W H, Fung R Y K. A human-computer interactive approach based on activity-section analysis for BPR[J]. Production Planning & Control,2000,11(8): 789-796.
64. Shapiro A. Simulation based optimization[C]. Proceedings of the 1996 Winter Simulation Conference,1996:332-336.
65.李东,汪定伟.基于仿真的优化方法综述[J].控制工程,2008,15(06)：672-677,702.
66.李浩渊,汪定伟.集装箱码头箱区规划的并行仿真优化方法[J].东北大学学报(自然科学版),2008,29(12)：1685-1688.
67. Jacobs P H M, Verbraeck A, Mulder J B P. Flight scheduling at KLM[C]. Proceedings of the 2005 Winter Simulation Conference,2005:299-306.
68. Alkhamis T M, Ahmed M A. Simulation-based optimization using simulated annealing with confidence interval[C]. Proceedings of the 2004 Winter Simulation Conference, 2004:514-519.
69. Alkhamis T M, Ahmed M A. Simulation-based optimization for repairable systems using particle swarm algorithm[C]. Proceedings of the 2005 Winter Simulation Conference, 2005:857-861.
70. Ho Y C, Sreenivas R, Vakili P. Ordinal optimization of discrete event dynamic systems[J]. Journal of Discrete Event Dynamic Systems,1992,2(2):61-88.
71. Kim S H, Nelson B L. Selecting the best system:Theory and methods[C]. Proceedings of the 2003 Winter Simulation Conference,2003,1(101-112.
72. Swisher J R, Jacobson S H, Yiicesan E. Discrete-event simulation optimization using ranking, selection, and multiple comparison procedures:A survey[J]. ACM Transactions on Modeling and Computer Simulation (TOMACS),2003,13(2):134-154.
73. Nelson B L, Swann J, Goldsman D, Song W. Simple procedures for selecting the best simulated system when the number of alternatives is large[J]. Operations Research,2001, 49(6):950-963.
74. Lee L H, Chew E P. A simulation study on sampling and selecting under fixed computing budget[C]. Proceedings of the 1998 Winter Simulation Conference,2003,1(535-542
75. Bachelet B, Yon L. Model enhancement:Improving theoretical optimization with simulation[J]. Simulation Modelling Practice and Theory,2007,15(6):703-715.
76. Bettonvil B, del Castillo E, Kleijnen J P C. Statistical testing of optimality conditions in multiresponse simulation-based optimization[J]. European Journal of Operational Research,2009,199(2):448-458.
77.刘慧超,伊山,梅珊,王维平.一种新的近似优化方法——变复杂度优化[J].军事运筹与系统工程,2007,21(002)：15-18.
78.李东.铁路集装箱调运的基于仿真的优化方法[D].东北大学,2009.
79. Paul R J, Chanev T S. Simulation optimisation using a genetic algorithm[J]. Simulation Practice and Theory,1998,6(6):601-611.
80.李东,汪定伟.基于仿真的铁路集装箱装卸策略优化[J].系统仿真学报,2009,21(11)：3188-3191,3198.
81.李浩渊,汪定伟.集装箱码头动态集卡配置的并行仿真优化方法[J].系统仿真学报,2009,21(14)：4243-4247.
82. Ahmed M A, Alkhamis T M. Simulation-based optimization using simulated annealing with ranking and selection[J]. Computers & Operations Research,2002,29(4):387-402.
83. Al-Aomar R. Designing machine operating strategy with simulated annealing and Monte Carlo simulation[J]. Journal of the Franknn Institute,2006,343(4-5):372-388.
84. Takahama H, Nishi T, Konishi M, Imai J. A determination method of product allocation schedule for warehouse management[C]. Proceedings of the 41st SICE Annual Conference,2002,2(1004-1007.
85. Ueno N, Nakagawa Y, Okuno Y, Morito S. Steel product transportation and storage simulation:a combined simulation/optimization approach[C]. Proceedings of the 2001 Winter Simulation Conference,1988:678-683.
86. Klemmt A, Horn S, Weigert G, Wolter K-J. Simulation-based optimization vs. mathematical programming:A hybrid approach for optimizing scheduling problems[J]. Robotics and Computer-Integrated Manufacturing,2009,25(6):917-925.
87. Tamaki H, Kitamura S, Murao H. Simulation-based optimization model and metaheuristic solution of multiple crane scheduling problems[C].2004 IEEE International Conference on Systems, Man and Cybernetics,2004,2(1469-1474.
88.伍乃骐,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(001)：90-96.
89.王国新,宁汝新,王爱民.基于仿真的生产调度优化技术研究[J].计算机集成制造系统,2007,13(007)：1419-1427.
90. Krishnamoorthy S, Bedekar A S, Feng J J, Sundaram S. Need for Simulation-Based Design Analysis and Optimization[J]. Journal of the Association for Laboratory Automation,2006,11(3):118-127.
91. Truong T H, Azadivar F. Simulation based optimization for supply chain configuration design[C]. Proceedings of the 2003 Winter Simulation Conference,2003,2(
92.于越,金淳,霍琳.基于仿真优化的集装箱堆场大门系统规划研究[J].系统仿真学报,2007,19(013)：3080-3084.
93.张涛,苗明,金淳.基于仿真优化的集装箱堆场资源配置研究[J].系统仿真学报,2007,19(024)：5631-5634.
94.真虹.集装箱码头生产过程动态图形仿真优化的研究[J].中国图象图形学报：A辑,1999,4(006)：502-506.
95.蔡芸,王少梅,严武元.集装箱码头前沿布局的仿真优化[J].武汉理工大学学报：交通科学与工程版,2006,30(002)：326-328.
96.曹振新,朱云龙,李富明.混流轿车总装配线的动态规划与仿真优化研究[J].计算机集成制造系统,2006,12(004)：526-532.
97. Ekren B Y, Ekren O. Simulation based size optimization of a PV/wind hybrid energy conversion system with battery storage under various load and auxiliary energy conditions[J]. Applied Energy,2009,86(9):1387-1394.
98.钟登华,刘建民,熊开智.复杂长距离输水系统仿真优化研究[J].中国工程科学, 2005,7(010)：60-63.
99.汪定伟,王俊伟,王洪峰,张瑞友,郭哲.智能优化方法[M].北京：高等教育出版社,2007.
100. Bortfeldt A, Gehring H. A hybrid genetic algorithm for the container loading problem[J]. European Journal of Operational Research,2001,131(1):143-161.
101. Ng W C, Mak K L, Zhang Y X. Scheduling trucks in container terminals using a genetic algorithm[J]. Engineering Optimization,2007,39(1):33-47.
102. Yasmina H, Hicham C, Lionel A, Farouk Yalaoui A F Y. Simulation based optimization of a train maintenance facility model using genetic algorithms[C]. Service Systems and Service Management,2006 International Conference on,2006,1(513-518.
103. Zhang R, Han S, Zhang J, Wang D. Zone division models and algorithms in zonal pricing power market[J]. European Transactions on Electrical Power,2009,19(1):140-149.
104. Zhang R, Yun W Y, Moon I. A reactive tabu search for the multi-depot container truck transportation problem [J]. Transprot. Res. Part E 2009,45E(6):904-914.
105. Battiti R, Tecchiolli G. The reactive tabu search[J]. ORSA Journal on Computing,1994, 6(2):126-140.
106. Chen L, Bostel N, Dejax P, Cai J, Xi L. 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(1):40-58.
107. Glover F. Tabu search:part I [J]. ORSA Journal on Computing,1989,1(3):190-206.
108. Lee D-H, Cao Z, Meng Q. Scheduling of two-transtainer systems for loading outbound containers in port container terminals with simulated annealing algorithm[J]. International Journal of Production Economics,2007,107(1):115-124.
109. Lim A, Zhu W B. A fast and effective insertion algorithm for multi-depot vehicle routing problem with fixed distribution of vehicles and a new simulated annealing approach[J]. Lecture Notes in Computer Science,2006,4031(282-291.
110.王俊伟.粒子群优化算法的改进及应用[D].东北大学,2006.
111.谈超,李小平.双目标无等待流水线调度的加权混合算法[J].计算机科学,2008,11).
112.潘全科,王文宏,朱剑英.求解无等待流水车间问题的若干算法[J].计算机集成制造系统,2007,05).
113.宁正元,林大辉,李丽珊,钟一文.置换流水车间调度问题的离散粒子群优化算法[J].集美大学学报(自然科学版),2008,02).
114.黄辉,屠乃威,马天牧,郑秉霖,柴天佑.基于VRP的模铸组炉问题模型及其蚁群算法[C],2009.
115. Linhares A. Synthesizing a predatory search strategy for VLSI layouts[J]. IEEE Transactions on Evolutionary Computation,1999,3(2):147-152.
116.张顶学,关治洪,刘新芝.基于捕食搜索策略的遗传算法研究[J].计算机应用研究,2008,25(004)：1006-1007.
117.黄海燕,顾幸生,刘漫丹.求解约束优化问题的文化算法研究[J].自动化学报,2007,33(010)：1115-1120.
118.齐仲纪,刘漫丹.文化算法研究[J].计算机技术与发展,2008,18(005)：126-130.
119. Wang D. Colony location algorithm for assignment problems[J]. Journal of Control Theory and Applications,2004,2(2):111-116.
120. Xiao J, Xu J, Chen Z, Zhang K, Pan L. A hybrid quantum chaotic swarm evolutionary algorithm for DNA encoding[J]. Computers & Mathematics with Applications,2009, 57(11-12):1949-1958.
121.殷志祥,张风月,许进.基于分子信标的DNA计算[J].生物数学学报,2003,18(004)：497-501.
122.李威武,王慧,邹志君,钱积新.基于细菌群体趋药性的函数优化方法[J].电路与系统学报,2005,10(001)：58-63.
123. Goldrian G, Huth T, Krill B, Lauritsen J, Schick H, Ouda I, Heybrock S, Hierl D, Maurer T, Meyer N, Schafer A, Solbrig S, Streuer T, Wettig T, Pleiter D, Sulanke K H, Winter F, Simma H, Schifano S F, Tripiccione R, Nobile A, Drochner M, Lippert T, Fodor Z. QPACE:Quantum Chromodynamics Parallel Computing on the Cell Broadband Engine[J]. Computing in Science & Engineering,2008,10(6):46-54.
124. Li Z, Rudolph G, Li K. Convergence performance comparison of quantum-inspired multi-objective evolutionary algorithms[J]. Computers & Mathematics with Applications, 2009,57(11-12):1843-1854.
125. Xing H, Ji Y, Bai L, Liu X, Qu Z, Wang X. An adaptive-evolution-based quantum-inspired evolutionary algorithm for QoS multicasting in IP/DWDM networks [J]. Computer Communications,2009,32(6):1086-1094.
126.王洪峰,汪定伟,杨圣祥.动态环境中的进化算法[J].控制与决策,2007,22(002)：127-131.
127.石嘉川,刘玉田.计及分布式发电的配电网多目标电压优化控制[J].电力系统自动化,2007：13.
128.闫海峰,董守清.铁路集装箱结点站间空箱调配的优化[J].中国铁道科学,2009,30(01)：131-135.
129. Willis K O, Jones D F. Multi-objective simulation optimization through search heuristics and relational database analysis[J]. Decision Support Systems,2008,46(1):277-286.
130. Lee L H, Chew E P, Teng S, Chen Y. Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem[J]. European Journal of Operational Research,2008,189(2):476-491.
131. Filipic B, Tu ar T, Laitinen E. Preliminary numerical experiments in multiobjective optimization of a metallurgical production process[J]. Informatica,2007,31(2):233-240.
132. Xu L, Reinikainen T, Ren W, Wang B P, Han Z, Agonafer D. A simulation-based multi-objective design optimization of electronic packages under thermal cycling and bending[J]. Microelectronics Reliability,2004,44(12):1977-1983.
133.彭频,李铁克.基于准时制的炼钢连铸组炉问题模型和算法[J].计算机工程与应用,2007,43(031)：222-224.
134. Blochliger I, Zufferey N. A graph coloring heuristic using partial solutions and a reactive tabu scheme[J]. Computers & Operations Research,2008,35(3):960-975.
135. Braysy O, Gendreau M. Vehicle Routing Problem with Time Windows, Part II: Metaheuristics[J]. Transportation Science,2005,39(1):119-139.
136. Castellani U, Fusiello A, Gherardi R, Murino V. Automatic selection of MRF control parameters by reactive tabu search[J]. Image and Vision Computing,2007,25(11): 1824-1832.
137.王正国,刘振元,王红卫.适应性禁忌搜索算法求解带回程的时变速度车辆路径问题[J].计算机集成制造系统,2006,12(009)：1453-1458.
138.杨银国,张伏生,贺春光,王春娟,李宁.配电网无功电压优化控制求解的一种新方法[J].电力系统自动化,2005,29(009)：45-49.
139.孙福权,郑秉霖,汪定伟,杨晶伟,胡琨元,洪世辉.连铸区最优组批模型研究及应用[J].钢铁,1999,34(增刊)：72-75.
140. Harjunkoski I, Grossmann I E. A decomposition approach for the scheduling of a steel plant production[J]. Computers & Chemical Engineering,2001,25(11-12):1647-1660.
141.崔建双,李铁克,张文新.混合流水车间调度模型及其遗传算法[J].北京科技大学学报,2005,05)：113-116.
142.李大卫,王莉,王梦光.基于遗传算法的钢管生产批量计划[J].系统工程理论方法应用,1998,03).
143.王红卫,孙长银,沈轶,余明晖.系统科学与系统工程学科发展战略研究[J].中国科学基金,2009,23(2).
144.管超军,白丽平.基于eM—plant的炼油一次加工过程建模与仿真[J].机电工程技术,2009,004)：20-23.
145. Zhang Y, Gallipoli D, Augarde C E. Simulation-based calibration of geotechnical parameters using parallel hybrid moving boundary particle swarm optimization[J]. Computers and Geotechnics,2009,36(4):604-615.
146. Sriver T A, Chrissis J W, Abramson M A. Pattern search ranking and selection algorithms for mixed variable simulation-based optimization[J]. European Journal of Operational Research,2009,198(3):878-890.
147. Li Y. A simulation-based evolutionary approach to LNA circuit design optimization[J]. Applied Mathematics and Computation,2009,209(1):57-67.
148. Jaluria Y. Simulation-based optimization of thermal systems[J]. Applied Thermal Engineering,2009,29(7):1346-1355.
149. Altiok T, Xiong W, Gunduc M. A capacity planning tool for the tuxedo middleware used in transaction processing systems[C]. Proceedings of the 33nd conference on Winter simulation 2001:502-507.
150.徐春金Tuxedo中间件开发与配置[M].北京：中国电力出版社,2003.
2. Vessell C. Computer integrated manufacturing, and small and medium enterprises[J]. Computers and Industrial Engineering,1999,37(1):425-428.
3. Nagalingam S V, Lin G C I. Latest developments in CIM[J]. Robotics and Computer Integrated Manufacturing,1999,15(6):423-430.
4.卢和煜,朱景力,朱立平.宝钢的物资管理[M].北京：冶金工业出版社,中国人民大学出版社,1998.
5.包燕平,冯捷.钢铁冶金学教程[M].北京：冶金工业出版社,2008.
6. Fu P, Zhang Q. Investigation on steelmaking dust recycling and iron oxide red preparing[J]. Journal of University of Science and Technology Beijing, Mineral, Metallurgy, Material,2008,15(1):24-28.
7.高振,唐立新,陶炜,肖新华.大型钢铁企业原料采购计划模型[J].系统工程学报,2003,06).
8.罗治洪,唐立新,张悟移.钢铁原料物流计划问题的建模与求解[J].系统工程理论与实践,2008,05).
9. Oyama S, Kokubo T, Ishida T. Domain-specific web search with keyword spices[J]. IEEE Transactions on knowledge and data engineering,2004,16(1):17-27.
10-陈俊杰,薛云.基于Agent的元搜索引擎的研究与设计[J].计算机工程与应用,2003,39(010)：33-36.
11.张卫丰,徐宝文,周晓宇,许蕾,李东.元搜索引擎结果生成技术研究[J].小型微型计算机系统,2003,24(1)：34-37.
12.王铮,王庆,汪定伟.电子商务中基于Hopfield神经网络的货源信息搜索方法[J].东北大学学报：自然科学版,2008,29(002)：185-188.
13. Wang Z, Wang Q, Wang D W. Bayesian network based business information retrieval model[J]. Knowledge and Information Systems,2009,20(1):63-79.
14.汪定伟,王庆,宫俊,万福才.双边多轮价格谈判过程的建模与分析[J].管理科学学报,2007,10(001)：94-98.
15. Dingwei Wang, Kaku I. Modelling and Analysis of Bargaining Process for E-Procurement of Large Enterprise Group[C]. Proceedings of the 17th World Congress The International Federation of Automatic Control,2008:1639-1644.
16.杜斌.宝钢炼钢,连铸自动化技术研发的回顾与展望[J].冶金自动化,2006,S2(1)：10-17.
17.卢克斌,黄可为,汪定伟,张瑞友.钢铁企业合同计划与余材匹配的集成优化方法[J].控制与决策,2009,24(01)：71-75.
18.胡琨元,高政威,汪定伟.钢铁企业合同匹配多目标优化模型与算法[J].东北大学学报(自然科学版),2004,25(06)：527-530.
19. Salustowicz R, Schmidhuber J. Probabilistic incremental program evolution[J]. Evolutionary Computation,1997,5(2):123-141.
20.王高鹏,窦丽华,陈杰,张娟,陈晨.一种基于PBIL算法和变焦算法的混合算法[J].模式识别与人工智能,2009,22(001)：1-7.
21.胡琨元,常春光,郑秉霖,汪定伟.钢铁企业中库存匹配与生产计划联合优化模型与算法[J].信息与控制,2004,33(02)：177-180,196.
22.胡琨元,陈文明,汪定伟,郑秉霖.考虑批量因素的成品匹配与生产计划联合优化[J].系统工程理论方法应用,2004,13(003)：199-202.
23. Iosif A-M, Hanrot F, Ablitzer D. Process integrated modelling for steelmaking Life Cycle Inventory analysis[J]. Environmental Impact Assessment Review,2008,28(7):429-438.
24.黄可为,卢克斌,汪定伟.炼钢组炉问题优化模型及其动态规划算法[J].东北大学学报(自然科学版),2006,27(02)：138-141.
25.唐立新,杨自厚,王梦光.炼钢—连铸最优炉次计划模型与算法[J].东北大学学报(自然科学版),1996,17(04)：440-445.
26.卢克斌,黄可为,张瑞友,汪定伟.炼钢组炉问题的多目标优化模型与算法[J].中南大学学报(自然科学版),2009,增刊1(315-318.
27.王秀英,赵明,任志考.炼钢-连铸组浇计划的两阶段求解算法[J].青岛化工学院学报,2001.
28.李岩,刘建荣,李铁克.基于约束规划的最优浇次计划模型研究[J].冶金自动化,2007,31(003)：28-30.
29.唐立新,杨自厚.炼钢—连铸最优浇次(CAST)计划[J].东北大学学报：自然科学版,1996,17(005)：554-558.
30.唐立新,王梦光.炼钢—连铸对于浇次数未知的最优浇次计划模型与算法[J]·钢铁,1997,32(010)：19-21.
31.彭志刚,吴光宇.用两阶段启发式算法编制连铸生产计划[J].控制与决策,1999,14(A11)：585-588.
32.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)：1109-1112,1116.
33.陈军鹏,陈文明,罗首章,苏冬平,周仁义,庞新富,郑秉霖.CBR在炼钢连铸动态 调度系统中的应用研究[J].冶金自动化,2008,32(002)：29-33.
34.王秀英,柴天佑,郑秉霖.炼钢-连铸智能调度软件的开发及应用[J].计算机集成制造系统,2006,12(008)：1220-1226.
35.郑忠,朱道飞,高小强.钢厂炼钢连铸生产调度及重计划方法[J].重庆大学学报,2008,07)：114-118.
36.郑忠,朱道飞,高小强.基于蚁群算法的炼钢-连铸作业计划编制方法[J].北京科技大学学报,2009,04)：102-108.
37.朱道飞.钢厂生产作业计划与调度的自组织优化方法研究[D].重庆大学,2008.
38.朱道飞,郑忠,高小强.炼钢-连铸作业计划的遗传优化模型[J].钢铁,2008,07)：29-34.
39.俞胜平,王秀英,郑秉霖,柴天佑.基于变约束规划模型的炼钢连铸动态调度[J].控制理论与应用,2009,07)：65-70.
40余丽霞,唐秋华.炼钢-连铸生产作业计划编制及仿真检验方法研究[J].工业控制计算机,2008,11)：42-44.
41. Tang L, Liu J, Rong A, Yang Z. A mathematical programming model for scheduling steelmaking-continuous casting production[J]. European Journal of Operational Research, 2000,120(2):423-435.
42. Tang L, Wang G. Decision support system for the batching problems of steelmaking and continuous-casting production[J]. Omega,2008,36(6):976-991.
43郑秉霖,胡琨元.一体化钢铁生产计划系统的研究现状与展望[J].控制工程,2003,10(001)：6-10.
44.陈雄,徐心和.基于模拟退火轧制批量计划问题的两阶段算法[J].控制理论与应用,1999,16(002)：209-212.
45.黄可为,汪定伟.热轧计划中的多旅行商问题及其计算方法[J].计算机应用研究,2007,24(007)：43-45.
46.覃一宁.冷轧薄板生产计划与调度系统的研究与应用[D].大连理工大学,2007.
47.李建祥,唐立新,庞哈利,王梦光.热轧无缝钢管生产作业计划研究[J].系统仿真学报,2003,09).
48.李建祥,唐立新,庞哈利,王梦光.热轧无缝钢管排产计划研究[J].控制与决策,2004,02).
49.李建祥,唐立新,庞哈利,吴会江.热轧无缝钢管生产计划系统[J].冶金自动化,2003,05).
50.李建祥,唐立新,吴会江.热轧钢管主生产计划模型与算法研究[J].系统工程学报,2005,05).
51.刘彩燕.面向过程集成的钢铁企业质量管理研究[D].大连理工大学,2006.
52.张浩.钢铁制造业集成成本管理方法及应用研究[D].大连理工大学,2007.
53.范晓慧,曾垂喜,姜涛,陈许玲,龙红明,胡友明,张克诚.铁矿石烧结性能预报模型[J].中南大学学报：自然科学版,2005,36(006)：949-954.
54.《运筹学》教材编写组.运筹学[M].清华大学出版社,1990.
55.常春光,胡琨元,汪定伟,郑秉霖,李慧莹.钢铁生产动态调度理论研究与工程应用综述[J].信息与控制,2003,32(6)：531-537.
56.常春光,汪定伟,胡琨元,郑秉霖.嵌入调整模型的CBR技术的炼钢调度系统[J].东北工学院学报,2004,25(006)：531-534.
57.牛秦洲,叶恒舟,吴一峰.烧结厂烧结配料专家系统设计[J].桂林工学院学报,2002,22(004)：391-395.
58. Azadeh A, Ghaderi S F, Tarverdian S, Saberi M. Integration of artificial neural networks and genetic algorithm to predict electrical energy consumption[J]. Applied Mathematics and Computation,2007,186(2):1731-1741.
59. Chau K W. A split-step particle swarm optimization algorithm in river stage forecasting[J]. Journal of Hydrology,2007,346(3-4):131-135.
60. Garcia-Fernandez I, Martin-Guerrero J D, Pla-Castells M, Soria-Olivas E, Martinez-Dura R J, Munoz-Mari J. Crane collision modelling using a neural network approach[J]. Expert Systems with Applications,2004,27(3):341-348.
61. Ghiassi M, Zimbra D K, Saidane H. Medium term system load forecasting with a dynamic artificial neural network model[J]. Electric Power Systems Research,2006, 76(5):302-316.
62. Bistline Sr W G, Banerjee S, Banerjee A. RTSS:an interactive decision support system for solving real time scheduling problems considering customer and job priorities with schedule interruptions[J]. Computers & Operations Research,1998,25(11):981-995.
63. Wang D, Tang Z, Ip W H, Fung R Y K. A human-computer interactive approach based on activity-section analysis for BPR[J]. Production Planning & Control,2000,11(8): 789-796.
64. Shapiro A. Simulation based optimization[C]. Proceedings of the 1996 Winter Simulation Conference,1996:332-336.
65.李东,汪定伟.基于仿真的优化方法综述[J].控制工程,2008,15(06)：672-677,702.
66.李浩渊,汪定伟.集装箱码头箱区规划的并行仿真优化方法[J].东北大学学报(自然科学版),2008,29(12)：1685-1688.
67. Jacobs P H M, Verbraeck A, Mulder J B P. Flight scheduling at KLM[C]. Proceedings of the 2005 Winter Simulation Conference,2005:299-306.
68. Alkhamis T M, Ahmed M A. Simulation-based optimization using simulated annealing with confidence interval[C]. Proceedings of the 2004 Winter Simulation Conference, 2004:514-519.
69. Alkhamis T M, Ahmed M A. Simulation-based optimization for repairable systems using particle swarm algorithm[C]. Proceedings of the 2005 Winter Simulation Conference, 2005:857-861.
70. Ho Y C, Sreenivas R, Vakili P. Ordinal optimization of discrete event dynamic systems[J]. Journal of Discrete Event Dynamic Systems,1992,2(2):61-88.
71. Kim S H, Nelson B L. Selecting the best system:Theory and methods[C]. Proceedings of the 2003 Winter Simulation Conference,2003,1(101-112.
72. Swisher J R, Jacobson S H, Yiicesan E. Discrete-event simulation optimization using ranking, selection, and multiple comparison procedures:A survey[J]. ACM Transactions on Modeling and Computer Simulation (TOMACS),2003,13(2):134-154.
73. Nelson B L, Swann J, Goldsman D, Song W. Simple procedures for selecting the best simulated system when the number of alternatives is large[J]. Operations Research,2001, 49(6):950-963.
74. Lee L H, Chew E P. A simulation study on sampling and selecting under fixed computing budget[C]. Proceedings of the 1998 Winter Simulation Conference,2003,1(535-542
75. Bachelet B, Yon L. Model enhancement:Improving theoretical optimization with simulation[J]. Simulation Modelling Practice and Theory,2007,15(6):703-715.
76. Bettonvil B, del Castillo E, Kleijnen J P C. Statistical testing of optimality conditions in multiresponse simulation-based optimization[J]. European Journal of Operational Research,2009,199(2):448-458.
77.刘慧超,伊山,梅珊,王维平.一种新的近似优化方法——变复杂度优化[J].军事运筹与系统工程,2007,21(002)：15-18.
78.李东.铁路集装箱调运的基于仿真的优化方法[D].东北大学,2009.
79. Paul R J, Chanev T S. Simulation optimisation using a genetic algorithm[J]. Simulation Practice and Theory,1998,6(6):601-611.
80.李东,汪定伟.基于仿真的铁路集装箱装卸策略优化[J].系统仿真学报,2009,21(11)：3188-3191,3198.
81.李浩渊,汪定伟.集装箱码头动态集卡配置的并行仿真优化方法[J].系统仿真学报,2009,21(14)：4243-4247.
82. Ahmed M A, Alkhamis T M. Simulation-based optimization using simulated annealing with ranking and selection[J]. Computers & Operations Research,2002,29(4):387-402.
83. Al-Aomar R. Designing machine operating strategy with simulated annealing and Monte Carlo simulation[J]. Journal of the Franknn Institute,2006,343(4-5):372-388.
84. Takahama H, Nishi T, Konishi M, Imai J. A determination method of product allocation schedule for warehouse management[C]. Proceedings of the 41st SICE Annual Conference,2002,2(1004-1007.
85. Ueno N, Nakagawa Y, Okuno Y, Morito S. Steel product transportation and storage simulation:a combined simulation/optimization approach[C]. Proceedings of the 2001 Winter Simulation Conference,1988:678-683.
86. Klemmt A, Horn S, Weigert G, Wolter K-J. Simulation-based optimization vs. mathematical programming:A hybrid approach for optimizing scheduling problems[J]. Robotics and Computer-Integrated Manufacturing,2009,25(6):917-925.
87. Tamaki H, Kitamura S, Murao H. Simulation-based optimization model and metaheuristic solution of multiple crane scheduling problems[C].2004 IEEE International Conference on Systems, Man and Cybernetics,2004,2(1469-1474.
88.伍乃骐,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(001)：90-96.
89.王国新,宁汝新,王爱民.基于仿真的生产调度优化技术研究[J].计算机集成制造系统,2007,13(007)：1419-1427.
90. Krishnamoorthy S, Bedekar A S, Feng J J, Sundaram S. Need for Simulation-Based Design Analysis and Optimization[J]. Journal of the Association for Laboratory Automation,2006,11(3):118-127.
91. Truong T H, Azadivar F. Simulation based optimization for supply chain configuration design[C]. Proceedings of the 2003 Winter Simulation Conference,2003,2(
92.于越,金淳,霍琳.基于仿真优化的集装箱堆场大门系统规划研究[J].系统仿真学报,2007,19(013)：3080-3084.
93.张涛,苗明,金淳.基于仿真优化的集装箱堆场资源配置研究[J].系统仿真学报,2007,19(024)：5631-5634.
94.真虹.集装箱码头生产过程动态图形仿真优化的研究[J].中国图象图形学报：A辑,1999,4(006)：502-506.
95.蔡芸,王少梅,严武元.集装箱码头前沿布局的仿真优化[J].武汉理工大学学报：交通科学与工程版,2006,30(002)：326-328.
96.曹振新,朱云龙,李富明.混流轿车总装配线的动态规划与仿真优化研究[J].计算机集成制造系统,2006,12(004)：526-532.
97. Ekren B Y, Ekren O. Simulation based size optimization of a PV/wind hybrid energy conversion system with battery storage under various load and auxiliary energy conditions[J]. Applied Energy,2009,86(9):1387-1394.
98.钟登华,刘建民,熊开智.复杂长距离输水系统仿真优化研究[J].中国工程科学, 2005,7(010)：60-63.
99.汪定伟,王俊伟,王洪峰,张瑞友,郭哲.智能优化方法[M].北京：高等教育出版社,2007.
100. Bortfeldt A, Gehring H. A hybrid genetic algorithm for the container loading problem[J]. European Journal of Operational Research,2001,131(1):143-161.
101. Ng W C, Mak K L, Zhang Y X. Scheduling trucks in container terminals using a genetic algorithm[J]. Engineering Optimization,2007,39(1):33-47.
102. Yasmina H, Hicham C, Lionel A, Farouk Yalaoui A F Y. Simulation based optimization of a train maintenance facility model using genetic algorithms[C]. Service Systems and Service Management,2006 International Conference on,2006,1(513-518.
103. Zhang R, Han S, Zhang J, Wang D. Zone division models and algorithms in zonal pricing power market[J]. European Transactions on Electrical Power,2009,19(1):140-149.
104. Zhang R, Yun W Y, Moon I. A reactive tabu search for the multi-depot container truck transportation problem [J]. Transprot. Res. Part E 2009,45E(6):904-914.
105. Battiti R, Tecchiolli G. The reactive tabu search[J]. ORSA Journal on Computing,1994, 6(2):126-140.
106. Chen L, Bostel N, Dejax P, Cai J, Xi L. 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(1):40-58.
107. Glover F. Tabu search:part I [J]. ORSA Journal on Computing,1989,1(3):190-206.
108. Lee D-H, Cao Z, Meng Q. Scheduling of two-transtainer systems for loading outbound containers in port container terminals with simulated annealing algorithm[J]. International Journal of Production Economics,2007,107(1):115-124.
109. Lim A, Zhu W B. A fast and effective insertion algorithm for multi-depot vehicle routing problem with fixed distribution of vehicles and a new simulated annealing approach[J]. Lecture Notes in Computer Science,2006,4031(282-291.
110.王俊伟.粒子群优化算法的改进及应用[D].东北大学,2006.
111.谈超,李小平.双目标无等待流水线调度的加权混合算法[J].计算机科学,2008,11).
112.潘全科,王文宏,朱剑英.求解无等待流水车间问题的若干算法[J].计算机集成制造系统,2007,05).
113.宁正元,林大辉,李丽珊,钟一文.置换流水车间调度问题的离散粒子群优化算法[J].集美大学学报(自然科学版),2008,02).
114.黄辉,屠乃威,马天牧,郑秉霖,柴天佑.基于VRP的模铸组炉问题模型及其蚁群算法[C],2009.
115. Linhares A. Synthesizing a predatory search strategy for VLSI layouts[J]. IEEE Transactions on Evolutionary Computation,1999,3(2):147-152.
116.张顶学,关治洪,刘新芝.基于捕食搜索策略的遗传算法研究[J].计算机应用研究,2008,25(004)：1006-1007.
117.黄海燕,顾幸生,刘漫丹.求解约束优化问题的文化算法研究[J].自动化学报,2007,33(010)：1115-1120.
118.齐仲纪,刘漫丹.文化算法研究[J].计算机技术与发展,2008,18(005)：126-130.
119. Wang D. Colony location algorithm for assignment problems[J]. Journal of Control Theory and Applications,2004,2(2):111-116.
120. Xiao J, Xu J, Chen Z, Zhang K, Pan L. A hybrid quantum chaotic swarm evolutionary algorithm for DNA encoding[J]. Computers & Mathematics with Applications,2009, 57(11-12):1949-1958.
121.殷志祥,张风月,许进.基于分子信标的DNA计算[J].生物数学学报,2003,18(004)：497-501.
122.李威武,王慧,邹志君,钱积新.基于细菌群体趋药性的函数优化方法[J].电路与系统学报,2005,10(001)：58-63.
123. Goldrian G, Huth T, Krill B, Lauritsen J, Schick H, Ouda I, Heybrock S, Hierl D, Maurer T, Meyer N, Schafer A, Solbrig S, Streuer T, Wettig T, Pleiter D, Sulanke K H, Winter F, Simma H, Schifano S F, Tripiccione R, Nobile A, Drochner M, Lippert T, Fodor Z. QPACE:Quantum Chromodynamics Parallel Computing on the Cell Broadband Engine[J]. Computing in Science & Engineering,2008,10(6):46-54.
124. Li Z, Rudolph G, Li K. Convergence performance comparison of quantum-inspired multi-objective evolutionary algorithms[J]. Computers & Mathematics with Applications, 2009,57(11-12):1843-1854.
125. Xing H, Ji Y, Bai L, Liu X, Qu Z, Wang X. An adaptive-evolution-based quantum-inspired evolutionary algorithm for QoS multicasting in IP/DWDM networks [J]. Computer Communications,2009,32(6):1086-1094.
126.王洪峰,汪定伟,杨圣祥.动态环境中的进化算法[J].控制与决策,2007,22(002)：127-131.
127.石嘉川,刘玉田.计及分布式发电的配电网多目标电压优化控制[J].电力系统自动化,2007：13.
128.闫海峰,董守清.铁路集装箱结点站间空箱调配的优化[J].中国铁道科学,2009,30(01)：131-135.
129. Willis K O, Jones D F. Multi-objective simulation optimization through search heuristics and relational database analysis[J]. Decision Support Systems,2008,46(1):277-286.
130. Lee L H, Chew E P, Teng S, Chen Y. Multi-objective simulation-based evolutionary algorithm for an aircraft spare parts allocation problem[J]. European Journal of Operational Research,2008,189(2):476-491.
131. Filipic B, Tu ar T, Laitinen E. Preliminary numerical experiments in multiobjective optimization of a metallurgical production process[J]. Informatica,2007,31(2):233-240.
132. Xu L, Reinikainen T, Ren W, Wang B P, Han Z, Agonafer D. A simulation-based multi-objective design optimization of electronic packages under thermal cycling and bending[J]. Microelectronics Reliability,2004,44(12):1977-1983.
133.彭频,李铁克.基于准时制的炼钢连铸组炉问题模型和算法[J].计算机工程与应用,2007,43(031)：222-224.
134. Blochliger I, Zufferey N. A graph coloring heuristic using partial solutions and a reactive tabu scheme[J]. Computers & Operations Research,2008,35(3):960-975.
135. Braysy O, Gendreau M. Vehicle Routing Problem with Time Windows, Part II: Metaheuristics[J]. Transportation Science,2005,39(1):119-139.
136. Castellani U, Fusiello A, Gherardi R, Murino V. Automatic selection of MRF control parameters by reactive tabu search[J]. Image and Vision Computing,2007,25(11): 1824-1832.
137.王正国,刘振元,王红卫.适应性禁忌搜索算法求解带回程的时变速度车辆路径问题[J].计算机集成制造系统,2006,12(009)：1453-1458.
138.杨银国,张伏生,贺春光,王春娟,李宁.配电网无功电压优化控制求解的一种新方法[J].电力系统自动化,2005,29(009)：45-49.
139.孙福权,郑秉霖,汪定伟,杨晶伟,胡琨元,洪世辉.连铸区最优组批模型研究及应用[J].钢铁,1999,34(增刊)：72-75.
140. Harjunkoski I, Grossmann I E. A decomposition approach for the scheduling of a steel plant production[J]. Computers & Chemical Engineering,2001,25(11-12):1647-1660.
141.崔建双,李铁克,张文新.混合流水车间调度模型及其遗传算法[J].北京科技大学学报,2005,05)：113-116.
142.李大卫,王莉,王梦光.基于遗传算法的钢管生产批量计划[J].系统工程理论方法应用,1998,03).
143.王红卫,孙长银,沈轶,余明晖.系统科学与系统工程学科发展战略研究[J].中国科学基金,2009,23(2).
144.管超军,白丽平.基于eM—plant的炼油一次加工过程建模与仿真[J].机电工程技术,2009,004)：20-23.
145. Zhang Y, Gallipoli D, Augarde C E. Simulation-based calibration of geotechnical parameters using parallel hybrid moving boundary particle swarm optimization[J]. Computers and Geotechnics,2009,36(4):604-615.
146. Sriver T A, Chrissis J W, Abramson M A. Pattern search ranking and selection algorithms for mixed variable simulation-based optimization[J]. European Journal of Operational Research,2009,198(3):878-890.
147. Li Y. A simulation-based evolutionary approach to LNA circuit design optimization[J]. Applied Mathematics and Computation,2009,209(1):57-67.
148. Jaluria Y. Simulation-based optimization of thermal systems[J]. Applied Thermal Engineering,2009,29(7):1346-1355.
149. Altiok T, Xiong W, Gunduc M. A capacity planning tool for the tuxedo middleware used in transaction processing systems[C]. Proceedings of the 33nd conference on Winter simulation 2001:502-507.
150.徐春金Tuxedo中间件开发与配置[M].北京：中国电力出版社,2003.