基于DNA遗传算法的协同制造资源优化配置技术研究
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摘要
在全球化和网络化的制造新环境下,网络化协同制造资源的优化配置是实现异地协同制造的关键技术,已经成为现代集成制造研究领域的热点问题之一。本文以网络化协同制造资源优化配置为背景,以制造资源为核心,深入研究制造资源对象的信息管理和建模问题,重点研究基于DNA遗传算法的制造资源的智能优化仿真问题,为实现制造资源的优化配置奠定了理论基础,并建立基于DNA遗传算法的网络化协同制造资源优化配置系统。主要研究内容如下:
(1)针对协同制造资源优化配置的理论问题进行研究。由于目前的协同制造资源优化配置尚没有完善的和普遍适用的理论基础,从概念和过程等方面对制造资源进行系统的、多层次的基础理论研究,将协同制造的资源优化配置技术分为协同制造资源信息模型、协同制造资源优化配置模型、协同制造资源优化仿真算法三类,在此基础上,建立以制造资源为核心的制造资源优化配置概念模型和过程模型,开展制造资源的管理和基于UML的建模工作。
(2)研究网络化协同制造资源的优化配置技术和评价指标。由于协同制造任务的复杂性,将制造资源优化配置技术从更为细微的资源能力信息层次来研究制造资源的选择,并建立更加科学化和精确化的定性指标评价方法,研究基于定量优化指标的面向候选制造资源集合的优化配置技术,将制造资源优化配置问题归结为传统制造模式下的多目标车间制造资源调度技术、虚拟企业或者动态联盟的伙伴选择技术以及网络化协同制造资源优化配置技术三个基本问题,并给出相应的求解方法。
(3)研究基于DNA遗传算法的协同制造资源优化配置仿真算法。针对普通遗传算法存在编码简单且局部寻优能力差等特点,重点研究采用两类基于DNA计算的改进遗传算法来求解协同制造资源优化配置问题,RNA-SIGA算法采用四进制编码方式,使用分区选择方法,设计分区动态自适应的交叉、变异等操作;DNA-DIGA算法采用双单链编码方式,使用基于Pareto排序的个体密度选择操作,设计分类渐变和突变的交叉、变异等操作,通过推理证明两类算法的全局收敛性,接着采用典型测试函数对两类算法的性能进行仿真测试,并分别利用遗传算法和设计的两类算法对比求解三类优化配置问题,优化结果证明了算法的有效性。
(4)研究网络化协同制造资源优化配置系统的设计与开发。提出协同制造资源优化配置系统的体系结构和业务流程,完成系统的功能设计与开发,实现了基于DNA遗传算法的协同制造资源优化配置技术的集成。分别以多目标车间协同资源优化、协同伙伴优选和网络化协同资源优化配置为例,给出制造资源优化配置实例的应用流程和运行结果,系统地验证本文所提出的理论、模型和方法的有效性。
In the new globalization and network manufacturing environment, network collaborative manufacturing resources optimization deployment is the key technology to realize distributed collaborative manufacture; it has become one of the hot topics in the study field of modern integrated manufacturing. This study takes network collaborative manufacturing resources optimization deployment as a background, based on the manufacturing resource as the core, made a deep research about the information management and modeling problems of the manufacturing resources objects, mainly studied the Intelligent optimization simulation problems of the manufacturing resources which based on DNA genetic algorithm, laid a theoretical foundation for the optimize configuration of the manufacturing resources, and established network collaborative manufacturing resources optimization deployment system based on DNA genetic algorithm. The main research contents are as follows:
(1) Study the theory basis of the collaborative manufacturing resources optimization deployment. Because there is no perfect and universally applicable theoretical basis for the current collaborative manufacturing resources optimization deployment, made systematic and multilevel theory basis research for the manufacturing resources from the aspects of concept and process; divided the collaborative manufacturing resources optimization deployment into three fields: collaborative manufacturing resources information model, collaborative manufacturing resources optimize configuration model and collaborative manufacturing resources optimize simulation algorithm, then established manufacturing resources optimization deployment conceptual model and process model with the manufacturing resources as the core; worked on the management of manufacturing resources and the model establishment based on UML.
(2) Study on evaluation index and optimize configuration technology of network collaborative manufacturing resources. Due to the complexity of collaborative manufacturing, study the manufacturing resource selection from the more subtle resource capacity information levels, and establish more scientific and accurate qualitative index evaluation method, studied the optimize configuration technology--candidates for manufacturing resources collection which based on quantitative optimization index, attributed the manufacturing resources optimization allocation problem into three basic problems: the multi-objective workshops manufacturing resources scheduling technique in the traditional manufacturing model; the partner selection technology of the virtual enterprise or dynamic union; the network collaborative manufacturing resources optimization deployment; and presented the corresponding solving methods.
(3) Study on simulation algorithm of collaborative manufacturing resources optimization deployment based on DNA genetic algorithm. For genetic algorithm has simple code and poor ability for local research, this paper mainly studies the method of using two kinds of improved genetic algorithm based on DNA computing to solve the problems of collaborative manufacturing resources optimization deployment. RNA-SIGA algorithm adopt four-digit-system encoding, use zoning selection method, design dynamically adaptive crossover and mutation operations; DNA-DIGA algorithm adopt sing-dual chain coding method, use the choose operation based on Pareto individual density sorting, design the crossover and mutation operations of classification gradient and mutations, proved the global convergence of these two algorithms by reasoning, using typical test function to make simulation test for these two kinds of algorithm, and by respectively using the genetic algorithm and the design algorithm to contrast solve three kinds of optimize configuration problems, the optimization results proved the effectiveness of the algorithm.
(4) Study on design and development of network collaborative manufacturing resources optimization deployment system. Put forward the system structure and business process of network collaborative manufacturing resources optimization deployment system; complete the functions design and development of the system; realize the integration of the collaborative manufacturing resources optimization deployment which based on DNA genetic algorithm. Respectively set the multi-objective workshops collaborative resources optimization, collaborative partner selection and network collaborative manufacturing resources optimization deployment as the example, given the application process and operation results for the manufacturing resources optimization allocation, systemically verify the effectiveness of the theories, models and methods which proposed in this paper.
(1)针对协同制造资源优化配置的理论问题进行研究。由于目前的协同制造资源优化配置尚没有完善的和普遍适用的理论基础,从概念和过程等方面对制造资源进行系统的、多层次的基础理论研究,将协同制造的资源优化配置技术分为协同制造资源信息模型、协同制造资源优化配置模型、协同制造资源优化仿真算法三类,在此基础上,建立以制造资源为核心的制造资源优化配置概念模型和过程模型,开展制造资源的管理和基于UML的建模工作。
(2)研究网络化协同制造资源的优化配置技术和评价指标。由于协同制造任务的复杂性,将制造资源优化配置技术从更为细微的资源能力信息层次来研究制造资源的选择,并建立更加科学化和精确化的定性指标评价方法,研究基于定量优化指标的面向候选制造资源集合的优化配置技术,将制造资源优化配置问题归结为传统制造模式下的多目标车间制造资源调度技术、虚拟企业或者动态联盟的伙伴选择技术以及网络化协同制造资源优化配置技术三个基本问题,并给出相应的求解方法。
(3)研究基于DNA遗传算法的协同制造资源优化配置仿真算法。针对普通遗传算法存在编码简单且局部寻优能力差等特点,重点研究采用两类基于DNA计算的改进遗传算法来求解协同制造资源优化配置问题,RNA-SIGA算法采用四进制编码方式,使用分区选择方法,设计分区动态自适应的交叉、变异等操作;DNA-DIGA算法采用双单链编码方式,使用基于Pareto排序的个体密度选择操作,设计分类渐变和突变的交叉、变异等操作,通过推理证明两类算法的全局收敛性,接着采用典型测试函数对两类算法的性能进行仿真测试,并分别利用遗传算法和设计的两类算法对比求解三类优化配置问题,优化结果证明了算法的有效性。
(4)研究网络化协同制造资源优化配置系统的设计与开发。提出协同制造资源优化配置系统的体系结构和业务流程,完成系统的功能设计与开发,实现了基于DNA遗传算法的协同制造资源优化配置技术的集成。分别以多目标车间协同资源优化、协同伙伴优选和网络化协同资源优化配置为例,给出制造资源优化配置实例的应用流程和运行结果,系统地验证本文所提出的理论、模型和方法的有效性。
In the new globalization and network manufacturing environment, network collaborative manufacturing resources optimization deployment is the key technology to realize distributed collaborative manufacture; it has become one of the hot topics in the study field of modern integrated manufacturing. This study takes network collaborative manufacturing resources optimization deployment as a background, based on the manufacturing resource as the core, made a deep research about the information management and modeling problems of the manufacturing resources objects, mainly studied the Intelligent optimization simulation problems of the manufacturing resources which based on DNA genetic algorithm, laid a theoretical foundation for the optimize configuration of the manufacturing resources, and established network collaborative manufacturing resources optimization deployment system based on DNA genetic algorithm. The main research contents are as follows:
(1) Study the theory basis of the collaborative manufacturing resources optimization deployment. Because there is no perfect and universally applicable theoretical basis for the current collaborative manufacturing resources optimization deployment, made systematic and multilevel theory basis research for the manufacturing resources from the aspects of concept and process; divided the collaborative manufacturing resources optimization deployment into three fields: collaborative manufacturing resources information model, collaborative manufacturing resources optimize configuration model and collaborative manufacturing resources optimize simulation algorithm, then established manufacturing resources optimization deployment conceptual model and process model with the manufacturing resources as the core; worked on the management of manufacturing resources and the model establishment based on UML.
(2) Study on evaluation index and optimize configuration technology of network collaborative manufacturing resources. Due to the complexity of collaborative manufacturing, study the manufacturing resource selection from the more subtle resource capacity information levels, and establish more scientific and accurate qualitative index evaluation method, studied the optimize configuration technology--candidates for manufacturing resources collection which based on quantitative optimization index, attributed the manufacturing resources optimization allocation problem into three basic problems: the multi-objective workshops manufacturing resources scheduling technique in the traditional manufacturing model; the partner selection technology of the virtual enterprise or dynamic union; the network collaborative manufacturing resources optimization deployment; and presented the corresponding solving methods.
(3) Study on simulation algorithm of collaborative manufacturing resources optimization deployment based on DNA genetic algorithm. For genetic algorithm has simple code and poor ability for local research, this paper mainly studies the method of using two kinds of improved genetic algorithm based on DNA computing to solve the problems of collaborative manufacturing resources optimization deployment. RNA-SIGA algorithm adopt four-digit-system encoding, use zoning selection method, design dynamically adaptive crossover and mutation operations; DNA-DIGA algorithm adopt sing-dual chain coding method, use the choose operation based on Pareto individual density sorting, design the crossover and mutation operations of classification gradient and mutations, proved the global convergence of these two algorithms by reasoning, using typical test function to make simulation test for these two kinds of algorithm, and by respectively using the genetic algorithm and the design algorithm to contrast solve three kinds of optimize configuration problems, the optimization results proved the effectiveness of the algorithm.
(4) Study on design and development of network collaborative manufacturing resources optimization deployment system. Put forward the system structure and business process of network collaborative manufacturing resources optimization deployment system; complete the functions design and development of the system; realize the integration of the collaborative manufacturing resources optimization deployment which based on DNA genetic algorithm. Respectively set the multi-objective workshops collaborative resources optimization, collaborative partner selection and network collaborative manufacturing resources optimization deployment as the example, given the application process and operation results for the manufacturing resources optimization allocation, systemically verify the effectiveness of the theories, models and methods which proposed in this paper.
引文
[1]杨叔子,吴波.先进制造技术及其发展趋势[J].机械工程学报,2003,39(10):73-7 8.
[2]范玉顺,刘飞,祁国宁等.网络化制造系统及其应用实践[M].北京:机械工业出版社,2003,1-28.
[3] Nagel R.N. 21stCentury Manufacturing Enterprise Strategy. Bethehem:Iacocco Institute, high University.
[4] Kuo Chin-Haw, Shih Timothy K., Wang His-Tsung, et al. System architecture of CSCW[C]. Proceedings of the IEEE International Conference on Intelligent Pr ocessing Systems, ICIPS, 1998(2):1612-1615.
[5] Kamel Nabil N., Davison Robert M. Applying CSCW technology to overcome traditional barriers in group interactions[J].Source: Information& Management,199 8,34(11):209-219.
[6] Benoit Montreuil, Jean-Marc Frayret, Sophie D. Amours. A Strategic Framework for Networked Manufacturing. Computers for Industry, Special Issue on Computer Aided Production,2000,vol.42:299-317.
[7] Radharamanan R. The Present and the future of virtual manufacturing. IIE Annual Conference and Exhibition 2004:2691-2730.
[8] Shuka C, NaZquez M, ChenF F. Virtual Manufacturing: an Overview. Computers & Industrial Engineering,1996,31(l/2):79-82.
[9] MonPlaisir Leslie. Integrated CSCW architecture for distributed Project Planning[C].Industrial Engineering Research-Conference Proceeding,1997:364-368.
[10] Gutein C., Roseman M., Greenberg S. A usability study of awareness widgets in a shared workspace groupware system[C].Proceedings of the 1996 ACM Conference on Computer Supported Cooperative Work. Cambridge,MA,USA,1996:258-267.
[11] Sanderson A.C., Grave R.J., Millard D.L. Multipath agility in electronics manufacturing, in IEEE International Conference on Systems, Man, and Cybernetics,1994:501-505.
[12] Rudberg M., Olhager J. Manufacturing Networks and Supply Chains: an OperationsStrategy Perspective[J].Omega,2003,31(1):29-39.
[13] Mezgar I. and Kovacs G.L. Co-ordination of SME Production through a co- operation network[J]. J. of intelligent Manufacturing,1998,9(2):167-172.
[14] Shen W.M. Virtual Organizations in Collaborative Design and Manufacturing Systems[J], Journal of Organizational Virtualness.2000,2(2),43-58.
[15] Paul Wright, Carlo Sequin. CyberCut: A Networked Manufacturing service- the First International Conference on "Managing Enterprises-Stakeholders, Engineering, Logistics and Achievement" at Loughborough University, UK, July 22-24,1997.
[16] Olivier perrin, Claude Godart. A model to support collaborative work in virtual enterprises. Data & Knowledge Engineering,50(2004):63-86.
[17]于海斌,朱云龙.协同制造[M].北京:清华大学出版社,2004:10-52.
[18]伍晓宇,王志勇.模具网络协同制造[M].北京:机械工业出版社,2008:1-20.
[19]史美林.计算机支持的协同工作:概念、技术、应用[M].北京:电子工业出版社,1998:20-72.
[20]许竹君.协同制造系统中若干关键技术的研究与应用[D],硕士论文,浙江:浙江工商大学,2009.
[21]冯蔚东,陈剑,赵纯均.基于遗传算法的动态联盟伙伴选择过程及优化模型[J].清华大学学报(自然科学版).2000,40(10):120-124.
[22]曹洪医,汪定伟.用GA求解动态联盟中伙伴选择的多目标优化模型[J].控制与决策. 2002,5:274-277.
[23] NIIIP. The NIIIP reference architecture[EB/OL]. http://www.niiiP.org, 2001.
[24] NIIIP INC. Introduction to NIIIP concepts[EB/OL]. httP://www.niiiP.org, 2002.
[25]吴澄.基于CIMSNET的敏捷化工程[J].高技术通讯.2000,10(1):3-6.
[26] Hu J M,Paul G. Conceptual Framework and Architecture for Service Mediating Workfolw Management[J]. Information and Software Technology, 2003,45(13): 929-939.
[27] Mehrabi M G, Ulsoy A G, Koren Y. Reconfigurable manufacturing systems: Key to future manufaeturing. Journal of Intelligent Manufacturing,2000(11):403-419.
[28] Gou H M, Huang B Q, Liu W H, et al. A Framework for Virtual Enterprise OperationManagement[J]. Computers in Industry.2003,50(3):333-352.
[29]史元春,徐光佑.计算机支持的协同设计研究[J].计算机研究与发展,1998,35(7): 648-651.
[30]寿黎但,陈文智,石教英.一个基于多媒体X终端的协作CAD系统[J].计算机应用, 1998(7):8-11.
[31]黄业清,乌兰木其,邓家褆.并行工程环境下基于黑板的多自治体协同设计[J].计算机集成制造系统,1999,5(3):49-54.
[32]冯晨华等.CSCW系统中共享对象的协作支持模型[J].计算机研究与发展,1999,36(3):304-308.
[33]乌兰木其,黄业清,段海波,邓家提.支持虚拟产品设计的分布式协同开发环境[J].中国机械工程,2001,12(增刊):61-63.
[34] Alonso G. Advanced Transaction Models in Workflow Context[C]. Proceedings of International Conference on Data Engineering.1996,574-581.
[35] Dogac, Kalinichenko, Ozsu, Sheth. Workflow Management Systems and Interoperability[C].1998.
[36] S. Li, D. Jia, G Zhuang, A. Kittcl. Security Considerations for Workflow Systems[C]. Hawaii: Network Operations and Management Symposium,2000,34-49.
[37] Rohit Va1ia, Yaliya Y., AI-Salqan. Secure workflow Environment[C]. IEEE Computer Society DL WETICE,1997,269-276.
[38] Douglas W. Clark. Measurements of Dynamic List Structure[J]. IEEE Transactions on Software Engineering,1979,5(l):809-822.
[39]江平宇,陈献国.基于Web的同步远程协同产品设计的实现[J].机械工程学报.2002,38(3):34-38.
[40]范玉顺.全面集成的数字化企业与整体解决方案[J].计算机集成制造系统-CIMS,2002,8(12):925-930.
[41]董建华,肖田元,乔桂秀等.面向大规模定制的制造系统关键技术[J].计算机集成制造系统-CIMS,2002,8(8):589-592.
[42] D. Xue, J. Sun, D.H. Norrie. An intelligent optimal Production scheduling approach using constraint-based search and agent- based collaboration. Computers in Industry 46(2001):209-231.
[43] J. Sun, D. Xue. A dynamic reactive scheduling mechanism for responding to changesof production orders and manufacturing resources. Computer in Industry 46(2001):189 -207.
[44] G. Berio, F.B. Vernadat. New developments in enterprise modeling using CIMOSA. Computers in Industry,40(1999):99-114.
[45] M.K. Lim, Z. Zhang. A multi-agent based manufacturing control strategy for responsive manufacturing. Journal of Materials Processing Technology,2003.
[46]张玉云,吴瑞荣,田文生.制造系统资源建模与适应性工艺过程设计[J].计算机集成制造系统,1997(5):33-39.
[47]马鹏举,陈剑虹,卢秉恒等.支持动态联盟的制造资源信息建模[J].中国机械工程,2000(7),11(7):780-783.
[48]宋玉银,褚秀萍,蔡复之.基于STEP的制造资源能力建模及其应用研究[J].计算机集成制造系统,1999,5(4):46-50.
[49]吉锋.基于语义Web的协同制造链快速构建研究[D].博士论文,西安:西北工业大学,2006.
[50]姚倡锋.复杂零件异地协同制造资源优化配置技术研究[D].博士论文,西安:西北工业大学,2006.
[51]胡庆夕.并行环境下基于层次与特征技术的设备资源模型研究[J].中国机械工程,1999,10(11):1231-1235.
[52]戴毅茹,严隽薇,张晓棠.面向对象技术的资源建模方法[J].计算机集成制造系统,2001,7(10):26-30.
[53]戴毅茹,严隽薇,董志斌.支持BPR的面向对象资源建模技术[J].计算机工程,2001,27(9):184-186.
[54]倪中华,易红,汤文成等.基于XML面向工艺设计的制造资源模型[J].计算机集成制造系统,2002,8(6):429-432.
[55] Kaslingam R, Lee C. Selection of vendors-a mixed integer Programming approach[J]. Computers and Industrial Engineering,1996,31:347-50.
[56] Ossadnik W. AHP-based synergy allocation to Partners in a merger[J]. European Journal of Operational Research,1996,88:42-49.
[57] L. Mikhailov. Fuzzy analytical approach to Partnership selection information of virtual enterprises[J]. International Journal of Management Science,2002,30:393-401.
[58] Wang D, Ip W.H., Yung K.L. A heuristic genetic algorithm for subcontractor selection in a global manufacturing environment[C]. IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Reviews 2001,31(2):189-198.
[59] W.H. Ip, Min Huang, K.L. Yung,Dingwei Wang. Genetic algorithm solution for a risk-based partner selection problem in a virtual enterprise[J]. Computers and Operations Research,2003,30(2):213-231.
[60] W.H. Ip, K.L. Yung, Dingwei Wang. A branch and bound algorithm for sub-contractorselection in agile manufacturing environment[J]. International journal of Production economics,2004,l:195-205.
[61] Talluri S, Baker RC. Quantitative framework for designing efficient business process alliances[C]. In: Proceedings of 1996 International Conference on Engineering and Technology Management, Piscataway,1996:656-661.
[62] Talluri S, Baker R,Sarkis J. A framework for designing efficient value chain networks[J]. International Journal on production Eeonomics,1999,62(1-2):133-144.
[63]赵乃岩,范玉顺.基于产品结构的动态联盟盟员选择算法[J].计算机集成制造系统,2002,8(2):99-104.
[64]冯蔚东,陈剑,赵纯均.基于遗传算法的动态联盟伙伴选择过程及优化模型[J].清华大学学报(自然科学版),2000,40(10):120-124.
[65]沙磊,徐晓飞,李全龙等.基于遗传算法的动态联盟性能标准优化方法[J].计算机集成制造系统,2002,8(6):462-466.
[66]王丹,杨晓春,王国仁等.基于模糊层次分析法实现虚拟企业中的伙伴选择[J].东北大学学报,2002,21(6):607-609.
[67]马鹏举,陈剑虹,卢秉恒.敏捷制造的盟员选择策略研究[J].中国机械工程,1999,10(10):1176-1179.
[68]董朝阳,孙树栋.基于混合遗传算法的制造伙伴类型选择[J].机械制造,2006,(2):46-48.
[69]董朝阳,孙树栋,彭文利,姚倡锋.基于成组技术与遗传算法的两阶段制造伙伴选择[J].制造技术与机床,2005,(3):95-96.
[70]戴毅茹,严隽薇.基于市场驱动的虚拟企业伙伴选择方法[J].计算机集成制造系统,2002,8(9):710-714.
[71] Bellman R. Dynamic programming[M]. Princeton University Press,USA,1957.
[72] Bellman R, Esogbue A.O, Nabeshima I. Mathematical Aspects of Scheduling and Application[M],New York: Pergamon Press,1982.
[73] Maccarthy B.L, Liu J, Addressing the gap in scheduling research: a review of optimization and heuristic methods in production scheduling[J]. International Journal of Production Research,1993,31(1):59-79.
[74] Fisher M.L. The Lagraangian relaxation method for solving integer programming problems[J]. Management Science,1981,27,1:1-18.
[75] Fisher M.L. An application oriented guide to Lagraangian relaxation[J]. Interfaces,1985,15,2:10-21.
[76] Davis L. Job shop scheduling with genetic algorithm[C], In: Grefenstrette J, editor, Proceeding of the First International Conference on Genetic Algorithms, Lawrence Erlbaum Associates Hillsdale, NJ, Pittsburgh, PA,1985,136-140.
[77] Jones A, Rabelo K.Y, et al. A hybrid approach for real-time sequencing and scheduling[J]. International Journal of Computer Integrated Manufacturing,1995,8(2): 145-154.
[78]贺文锐.面向网络协同制造的资源优化配置技术[D].博士论文,西安:西北工业大学,2007.
[79]吕琳.基于Multi-agent的协同制造资源共享的相关理论与技术研究[D].博士论文,武汉:武汉理工大学,2007.
[80]屈喜龙.区域网络化制造中的资源资源管理及其优化配置技术研究[D].博士论文,成都:西南交通大学,2006.
[81]田建锋.支持ASP服务的制造资源描述与评价技术研究及实现[D].硕士论文,西安:西安理工大学,2004.
[82]王丙占.支持ASP服务的制造业资源管理与优化配置技术研究[D].硕士论文,西安:西安理工大学,2003.
[83]李惠林,谢庆生,殷国富等.面向ASP平台的网络化制造资源应用服务研究[J].贵州工业大学学报(自然科学版).2004,33(5):67-70.
[84] Liu Chengying, Wang Xiangkui, He Yuchen.Research on manufacturing resourcemodeling based on the O-O method[J]. Journal of Materials Processing Technol-ogy,2003,(139):40-43.
[85]董朝阳.网络化制造资源优化配置研究[D].博士论文,西安:西北工业大学,2005.
[86] Jim Arlow, Ila Neustadt. UML 2 and the unified process: practical object-oriented analysis and design[M]. Addison Wesley,2005.
[87] Martin Fowler. UML distilled: a brief guide to the standard object modeling language[M]. Addison Wesley,2006.
[88] Craig Larman. Applying UML and Patterns: an Introduction to Object-Oriented Analysis and Design and the Unified Process[M]. Pearson Education,2001.
[89] Holland J.H. Adaptation in Natural and Artificial System[M], An Arbor: The University of Michigan Press,1975.
[90]玄光男,程润伟.遗传算法与工程设计[M].北京:科学出版社,2000:30-98.
[91]陈国良等.遗传算法及其应用[M].北京:人民邮电出版社,1996:12-53.
[92]周明,孙树栋.遗传算法原理及应用[M].北京:国防工业出版社,1999:21-36.
[93] Adleman A.L, Molecular computation of solutions to combinatorial problems [J],Science,1994,266(1):1021-1023.
[94] G. Paun, G. Rozenberg, A. Salomaa. DNA Computing: New Computing Paradigms [M].Springer VerlagGmbH,1998.
[95] Begley J.D. The behavior of adaptive system which employ genetic and correlation algorithm[J]. Dissertation Abstracts Internatioanl,1967.
[96] Goldberg D.E, Lingle R. Alleles, Loci and the traveling salesman problem[C]. Proceedings of an International Conference on Genetic Algorithms and Their Application,1985.
[97] Goldberg D.E. Genetic Algorithms in Search[J], Optimization and Machine Learning. Addison-Wesley,1989.
[98] Gen M, Cheng R.W, Genetic Algorithms and Engineering Design[M]. New York: John Wiley & Sons,1996.
[99] Oliver I, Smith D, Holland J.R. A study of permutation crossover operators on the traveling salesman problem[C]. Proceedings of the 2nd International Conference on Genetic Algorithms, NJ: Lawrence Erlbaum,1987,224-230.
[100] Croce F, Tadei R, Volta G. A genetic algorithm for the job shop problem[J]. Computers and Operations Research,1995,22:15-24.
[101] Davis L. Handbook of Genetic Algorithms[M]. NY: Van Nostrand Reinhold,1991.
[102] Grefenstette J. J. Optimization of control parameters for genetic algorithms[C].IEEE Transactions on System, Man & Cybernetics,1986,16(1):122-128.
[103] Srinivas M, Patnaik L.M. Adaptive probabilities of crossover and mutation in genetic algorithms[C]. IEEE Transactions on System, Man and Cybernetic,1994,24(4):656- 667.
[104] Courant R, Variational methods for the solution of problems of equilibrium and vibrations[J]. Bulletin of the American Mathematical Society,1943,49(1):1-23.
[105] Richardson J.T, M.R. Palmer, G. Liepins, M. Hilliard. Some guidelines for genetic algorithms with penalty functions[C]. In: J.D. Schaffer(Ed.),Proceeding of the 3rd International Conference on Genetic Algorithms, George Mason University, Morgan Kaufmann, Reading, MA,1989:191-197.
[106] Kazarlis S, V. Petridis. Varying fitness functions in genetic algorithms: Studying the rate of increase of the dynamic penalty terms[C]. In: A.E. Eiben, T. Back, M. Schoenauer, H.P. Schwefel(Eds.), Parallel Problem Solving from Nature V-PPSN V, Springer, Amsterdam, Netherlands,1998,1498:211-220.
[107] Smith A. E, D.M. Tate, Genetic optimization using a penalty function[C]. In: S. Forrest(Ed.), Proceedings of the Fifth International Conference on Genetic Algorithms, University of Illinois at Urbana-Champaign, Morgan Kaufmann, San Mateo, CA, July 1993,499-503.
[108] Davis L.(Ed.). Handbook of Genetic Algorithms[M]. Van Nostrand Reinhold, New York,1991.
[109] Michalewicz Z, C.Z. Janikow. Handling constraints in genetic algorithms[M]. In: R.K. Belew, L.B. Booker(Eds.), Proceeding of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA,1991,151-157.
[110] Gen M, Cheng R.W, Genetic algorithms and engineering optimization[M]. Wiley Interscience, New York,2000.
[111] Deb K. and D. Kalyanmoy. Multi-Objective Optimization Using Evolutionary Algorithms[M]. John Wiley & Sons Ltd, London,2001.
[112] Goldberg D.E. and J. Richardson. Genetic algorithm with sharing for multimodal function optimization[C]. Proc. Of 2nd Int. Conf. Genetic Algorithms, Lawrence Erlbaum Associates,1987:41-49.
[113] Deb K. and D.E. Goldberg. An investigation of niche and species formation in genetic function optimization[C]. Proceeding of the 3rd International Conference on Genetic algorithms,1989:42-50.
[114] Deb K, et al. A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ[J]. IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[115] Jones A, Rabelo K.Y, et al. A hybrid approach for real-time sequencing and scheduling[J]. International Journal of Computer Integrated Manufacturing,1995,8(2): 145-154
[116] Garetti M, Taisch M. Neural networks in production planning and control[J]. Production Planning and Control,1999,10:324-339.
[117] Garetti M, Taisch M. Guest editorial: Advanced computing techniques in production planning and control[J].Production Planning and Control,1999, 10(4):301-304.
[118]丁永生.计算智能——理论、技术与应用[M].北京:科学出版社,2004:36-107.
[119]丁永生,邵世煌,任立红.DNA计算与软计算[M].北京:科学出版社,2002:12-116.
[120]陶吉利.基于DNA计算的遗传算法及应用研究[D].杭州:浙江大学博士论文,2007.
[121]王凌.智能优化算法及其应用[M].北京:清华大学出版社,2001:1-52.
[122]陈宝林.最优化理论与算法[M].北京:清华大学出版社,1989:1-39.
[123] Davis L. Job shop scheduling with genetic algorithm[C], In: Grefenstrette J, editor, Proceeding of the First International Conference on Genetic Algorithms, Lawrence Erlbaum Associates Hillsdale, NJ, Pittsburgh, PA,1985,136-140.
[124]刘西奎.DNA计算和遗传算法的编码与几个优化模型的研究[D].博士论文,武汉:华中科技大学,2003.
[125]黄布毅.DNA计算中若干理论问题的研究[D].博士论文,武汉:华中科技大学,2005.
[126]董亚非.若干DNA计算粘贴模型的研究[D].博士论文,武汉:华中科技大学,2004.
[127]王淑栋.四类DNA计算模型中一些理论与应用的研究[D].博士论文,武汉:华中科技大学,2004.
[128]曲惠琴.DNA计算若干问题研究[D].博士论文,上海:复旦大学,2005.
[129] Martyn Amos. Theoretical and Experimental DNA Computation[M].Springer,2005.
[130] Beave D. Computing with DNA[J]. Journal of Computational Biology. 1995,2(1):1-8.
[131] Neuhauser C, Krones M. The Genealogy of Samples in Models with Selection[J].Genetic s,1997,145(2):519-534.
[132] Smith W.D, Schweitzer A. DNA computers in vitro and vivo[J]. DIMACS Workshop on DNA-based Computing. Princeton,1995.
[133] Winfree E., Liu F., Wenzler L.A. and Seeman N.C. Design and self-assembly of two-dimensional DNA crystals. Nature.1998,394:539-544.
[134] Winfree E. On the computational power of DNA annealing and ligation. In DNA-based computers(Eds., Baum E.B. and Lipton R.J.). American Mathematical Society.1996,199-221.
[135] Kari L., Paun G. Salomaa A., Yu S. and Rozenberg G. DNA computing: Sticker systems and universality[J]. Acta Information.1998,35:401-420.
[136] Lioton R., DNA Solution of Hard Computation Problems[J].Science,1995,268(4):42- 54.
[137] Wu H.Y. An improved surface-based method for DNA computation[J].Biosystems, 2001,59(1):1-5.
[138] Cukras A.R., D. Faulhammer and R.J. Lipton et al. Chess games: a model for RNA based computation[J], BioSystems,1999,52(1-3):35-45.
[139] Li S.C., J. Xu. Operational Rules for Digital Coding of RNA Sequences Based on DNA Computing in high Dimensional Space[J],Bulletin of Science and Technology,2003,18(6):388-398.
[140] Dio H. and M. Furusawa. Evolution is promoted by asymmetrical mutations in DNA replication genetic algorithm with double-stranded DNA[J]. Fujitsu Scientific and Technical Journal(Japan),1996,32(2):248-255.
[141] M. Garzon, P. Neathery, R. Deaton et al. A new metric for DNA computing, in Proceedings of Genetic Programming 1997.1997,472-478.
[142] A.J. Hartemink, D.K. Giord and J. Khodor. Automated constraint-based nucleotide sequence selection for DNA computation, in Proceedings of 4th DIMACS Workshop on DNA Based Computers,1998,227-235.
[143] Chen L, V. Vempati and N. Aljaber. An application of genetic algorithms for flow-shop problems[J], European Journal of Operational Research.1995,80:389-396.
[144] T.F. Smith, M.S. Watemlan. Identification of common molecular subsequences[J]. J.Mol.Biol,1981(147):195-197.
[145] W.R. Pearson, D.J. Lipman. Improved tools for biological sequence comparison[J]. Proc.Natl.Acad.Sci,1988,85:2444-2448.
[146] Ouyang Q. et al. DNA Solution of the Maximal Clique Problem[J]. Science,1997,278 (17):446-449.
[147] Yoshikawa T., T. Furuhashi and Y. Uchikawa. The effects of combination of DNA coding method with pseudo-bacterial GA[C]. Proceedings of 1997 IEEE International Conference on Evolutionary Computation,1997,285-290.
[148]王万良,吴启迪.生产调度智能算法及其应用[M].北京:科学出版社,2007:36-62.
[149]刘民,吴澄.制造过程智能优化调度算法及其应用[M].北京:国防工业出版社,2008:53-101.
[150]黄席樾,张著洪,何传江等.现代智能算法理论及应用[M],科学出版社,2005:11-62.
[151]刘晓霞,谢里阳等.柔性作业车间多目标调度优化研究[J].沈阳:东北大学学报(自然科学版),2008,3:362-365.
[152]耿兆强,邹益仁.基于遗传算法的作业车间模糊调度问题的研究[J].计算机集成制造系统,2007,8(8):616-620.
[153] De Jong. Analysis of the Behavior of a Class of Genetic Adaptive Systems.PhD Thesis, Depart. Of Computer and Communication Sciences, University of Michigan, Ann Arbor,1975.
[154]雷英杰,张善文等.Matlab遗传算法工具箱及应用[M].西安:西安电子科技大学出版社,2005:62-140.
[155]刘维.精通Matlab与C/C++混合程序设计[M].北京:北京航空航天大学出版社,2008:48-131.
[156] Arita M., Hagiya M. and Suyama A. Joining and rotating data with molecules[C]. Proc. 1997 IEEE Int. Conf. on Evolutionary Computation. Indianapolis. IN USA. April 13~16.1997,243-248.
[157] ESPRIT Consortium AMICE. CIMOSA: Open System Architecture for CIM[C].(2nd Revised and Extended Edition)Berlin: Spring Verlag,1993.
[158] Didic M M. CIMOSA Model Creation and Execution for a Casting Process and Manufacturing Cell[J]. Computers in Industry,1994,(24):237-247.
[159] Kosanke K, Zelm M. CIMOSA modeling Processes[J]. Computers in Industry,1999,40(2-3):141-153.
[160] Larman, Craig. Applying UML and patterns: an introduction to object-oriented analysis and design and iterative development[M]. China Machine Press, 2006:121-163.
[161] Simon Robinson. Professional C# 3rd Edition[M]. Wrox Press,2004:238-265.
[162] Jeffrey Richter. Applied Microsoft .NET Framework Programming[M]. Microsoft Press,2005:57-75.
[163] John Kauffman,Thiru Thangarathinam. Beginning ASP.NET 2.0 Databases[M]. Wrox Press, 2002:83-109.
[2]范玉顺,刘飞,祁国宁等.网络化制造系统及其应用实践[M].北京:机械工业出版社,2003,1-28.
[3] Nagel R.N. 21stCentury Manufacturing Enterprise Strategy. Bethehem:Iacocco Institute, high University.
[4] Kuo Chin-Haw, Shih Timothy K., Wang His-Tsung, et al. System architecture of CSCW[C]. Proceedings of the IEEE International Conference on Intelligent Pr ocessing Systems, ICIPS, 1998(2):1612-1615.
[5] Kamel Nabil N., Davison Robert M. Applying CSCW technology to overcome traditional barriers in group interactions[J].Source: Information& Management,199 8,34(11):209-219.
[6] Benoit Montreuil, Jean-Marc Frayret, Sophie D. Amours. A Strategic Framework for Networked Manufacturing. Computers for Industry, Special Issue on Computer Aided Production,2000,vol.42:299-317.
[7] Radharamanan R. The Present and the future of virtual manufacturing. IIE Annual Conference and Exhibition 2004:2691-2730.
[8] Shuka C, NaZquez M, ChenF F. Virtual Manufacturing: an Overview. Computers & Industrial Engineering,1996,31(l/2):79-82.
[9] MonPlaisir Leslie. Integrated CSCW architecture for distributed Project Planning[C].Industrial Engineering Research-Conference Proceeding,1997:364-368.
[10] Gutein C., Roseman M., Greenberg S. A usability study of awareness widgets in a shared workspace groupware system[C].Proceedings of the 1996 ACM Conference on Computer Supported Cooperative Work. Cambridge,MA,USA,1996:258-267.
[11] Sanderson A.C., Grave R.J., Millard D.L. Multipath agility in electronics manufacturing, in IEEE International Conference on Systems, Man, and Cybernetics,1994:501-505.
[12] Rudberg M., Olhager J. Manufacturing Networks and Supply Chains: an OperationsStrategy Perspective[J].Omega,2003,31(1):29-39.
[13] Mezgar I. and Kovacs G.L. Co-ordination of SME Production through a co- operation network[J]. J. of intelligent Manufacturing,1998,9(2):167-172.
[14] Shen W.M. Virtual Organizations in Collaborative Design and Manufacturing Systems[J], Journal of Organizational Virtualness.2000,2(2),43-58.
[15] Paul Wright, Carlo Sequin. CyberCut: A Networked Manufacturing service- the First International Conference on "Managing Enterprises-Stakeholders, Engineering, Logistics and Achievement" at Loughborough University, UK, July 22-24,1997.
[16] Olivier perrin, Claude Godart. A model to support collaborative work in virtual enterprises. Data & Knowledge Engineering,50(2004):63-86.
[17]于海斌,朱云龙.协同制造[M].北京:清华大学出版社,2004:10-52.
[18]伍晓宇,王志勇.模具网络协同制造[M].北京:机械工业出版社,2008:1-20.
[19]史美林.计算机支持的协同工作:概念、技术、应用[M].北京:电子工业出版社,1998:20-72.
[20]许竹君.协同制造系统中若干关键技术的研究与应用[D],硕士论文,浙江:浙江工商大学,2009.
[21]冯蔚东,陈剑,赵纯均.基于遗传算法的动态联盟伙伴选择过程及优化模型[J].清华大学学报(自然科学版).2000,40(10):120-124.
[22]曹洪医,汪定伟.用GA求解动态联盟中伙伴选择的多目标优化模型[J].控制与决策. 2002,5:274-277.
[23] NIIIP. The NIIIP reference architecture[EB/OL]. http://www.niiiP.org, 2001.
[24] NIIIP INC. Introduction to NIIIP concepts[EB/OL]. httP://www.niiiP.org, 2002.
[25]吴澄.基于CIMSNET的敏捷化工程[J].高技术通讯.2000,10(1):3-6.
[26] Hu J M,Paul G. Conceptual Framework and Architecture for Service Mediating Workfolw Management[J]. Information and Software Technology, 2003,45(13): 929-939.
[27] Mehrabi M G, Ulsoy A G, Koren Y. Reconfigurable manufacturing systems: Key to future manufaeturing. Journal of Intelligent Manufacturing,2000(11):403-419.
[28] Gou H M, Huang B Q, Liu W H, et al. A Framework for Virtual Enterprise OperationManagement[J]. Computers in Industry.2003,50(3):333-352.
[29]史元春,徐光佑.计算机支持的协同设计研究[J].计算机研究与发展,1998,35(7): 648-651.
[30]寿黎但,陈文智,石教英.一个基于多媒体X终端的协作CAD系统[J].计算机应用, 1998(7):8-11.
[31]黄业清,乌兰木其,邓家褆.并行工程环境下基于黑板的多自治体协同设计[J].计算机集成制造系统,1999,5(3):49-54.
[32]冯晨华等.CSCW系统中共享对象的协作支持模型[J].计算机研究与发展,1999,36(3):304-308.
[33]乌兰木其,黄业清,段海波,邓家提.支持虚拟产品设计的分布式协同开发环境[J].中国机械工程,2001,12(增刊):61-63.
[34] Alonso G. Advanced Transaction Models in Workflow Context[C]. Proceedings of International Conference on Data Engineering.1996,574-581.
[35] Dogac, Kalinichenko, Ozsu, Sheth. Workflow Management Systems and Interoperability[C].1998.
[36] S. Li, D. Jia, G Zhuang, A. Kittcl. Security Considerations for Workflow Systems[C]. Hawaii: Network Operations and Management Symposium,2000,34-49.
[37] Rohit Va1ia, Yaliya Y., AI-Salqan. Secure workflow Environment[C]. IEEE Computer Society DL WETICE,1997,269-276.
[38] Douglas W. Clark. Measurements of Dynamic List Structure[J]. IEEE Transactions on Software Engineering,1979,5(l):809-822.
[39]江平宇,陈献国.基于Web的同步远程协同产品设计的实现[J].机械工程学报.2002,38(3):34-38.
[40]范玉顺.全面集成的数字化企业与整体解决方案[J].计算机集成制造系统-CIMS,2002,8(12):925-930.
[41]董建华,肖田元,乔桂秀等.面向大规模定制的制造系统关键技术[J].计算机集成制造系统-CIMS,2002,8(8):589-592.
[42] D. Xue, J. Sun, D.H. Norrie. An intelligent optimal Production scheduling approach using constraint-based search and agent- based collaboration. Computers in Industry 46(2001):209-231.
[43] J. Sun, D. Xue. A dynamic reactive scheduling mechanism for responding to changesof production orders and manufacturing resources. Computer in Industry 46(2001):189 -207.
[44] G. Berio, F.B. Vernadat. New developments in enterprise modeling using CIMOSA. Computers in Industry,40(1999):99-114.
[45] M.K. Lim, Z. Zhang. A multi-agent based manufacturing control strategy for responsive manufacturing. Journal of Materials Processing Technology,2003.
[46]张玉云,吴瑞荣,田文生.制造系统资源建模与适应性工艺过程设计[J].计算机集成制造系统,1997(5):33-39.
[47]马鹏举,陈剑虹,卢秉恒等.支持动态联盟的制造资源信息建模[J].中国机械工程,2000(7),11(7):780-783.
[48]宋玉银,褚秀萍,蔡复之.基于STEP的制造资源能力建模及其应用研究[J].计算机集成制造系统,1999,5(4):46-50.
[49]吉锋.基于语义Web的协同制造链快速构建研究[D].博士论文,西安:西北工业大学,2006.
[50]姚倡锋.复杂零件异地协同制造资源优化配置技术研究[D].博士论文,西安:西北工业大学,2006.
[51]胡庆夕.并行环境下基于层次与特征技术的设备资源模型研究[J].中国机械工程,1999,10(11):1231-1235.
[52]戴毅茹,严隽薇,张晓棠.面向对象技术的资源建模方法[J].计算机集成制造系统,2001,7(10):26-30.
[53]戴毅茹,严隽薇,董志斌.支持BPR的面向对象资源建模技术[J].计算机工程,2001,27(9):184-186.
[54]倪中华,易红,汤文成等.基于XML面向工艺设计的制造资源模型[J].计算机集成制造系统,2002,8(6):429-432.
[55] Kaslingam R, Lee C. Selection of vendors-a mixed integer Programming approach[J]. Computers and Industrial Engineering,1996,31:347-50.
[56] Ossadnik W. AHP-based synergy allocation to Partners in a merger[J]. European Journal of Operational Research,1996,88:42-49.
[57] L. Mikhailov. Fuzzy analytical approach to Partnership selection information of virtual enterprises[J]. International Journal of Management Science,2002,30:393-401.
[58] Wang D, Ip W.H., Yung K.L. A heuristic genetic algorithm for subcontractor selection in a global manufacturing environment[C]. IEEE Transactions on Systems Man and Cybernetics Part C-Applications and Reviews 2001,31(2):189-198.
[59] W.H. Ip, Min Huang, K.L. Yung,Dingwei Wang. Genetic algorithm solution for a risk-based partner selection problem in a virtual enterprise[J]. Computers and Operations Research,2003,30(2):213-231.
[60] W.H. Ip, K.L. Yung, Dingwei Wang. A branch and bound algorithm for sub-contractorselection in agile manufacturing environment[J]. International journal of Production economics,2004,l:195-205.
[61] Talluri S, Baker RC. Quantitative framework for designing efficient business process alliances[C]. In: Proceedings of 1996 International Conference on Engineering and Technology Management, Piscataway,1996:656-661.
[62] Talluri S, Baker R,Sarkis J. A framework for designing efficient value chain networks[J]. International Journal on production Eeonomics,1999,62(1-2):133-144.
[63]赵乃岩,范玉顺.基于产品结构的动态联盟盟员选择算法[J].计算机集成制造系统,2002,8(2):99-104.
[64]冯蔚东,陈剑,赵纯均.基于遗传算法的动态联盟伙伴选择过程及优化模型[J].清华大学学报(自然科学版),2000,40(10):120-124.
[65]沙磊,徐晓飞,李全龙等.基于遗传算法的动态联盟性能标准优化方法[J].计算机集成制造系统,2002,8(6):462-466.
[66]王丹,杨晓春,王国仁等.基于模糊层次分析法实现虚拟企业中的伙伴选择[J].东北大学学报,2002,21(6):607-609.
[67]马鹏举,陈剑虹,卢秉恒.敏捷制造的盟员选择策略研究[J].中国机械工程,1999,10(10):1176-1179.
[68]董朝阳,孙树栋.基于混合遗传算法的制造伙伴类型选择[J].机械制造,2006,(2):46-48.
[69]董朝阳,孙树栋,彭文利,姚倡锋.基于成组技术与遗传算法的两阶段制造伙伴选择[J].制造技术与机床,2005,(3):95-96.
[70]戴毅茹,严隽薇.基于市场驱动的虚拟企业伙伴选择方法[J].计算机集成制造系统,2002,8(9):710-714.
[71] Bellman R. Dynamic programming[M]. Princeton University Press,USA,1957.
[72] Bellman R, Esogbue A.O, Nabeshima I. Mathematical Aspects of Scheduling and Application[M],New York: Pergamon Press,1982.
[73] Maccarthy B.L, Liu J, Addressing the gap in scheduling research: a review of optimization and heuristic methods in production scheduling[J]. International Journal of Production Research,1993,31(1):59-79.
[74] Fisher M.L. The Lagraangian relaxation method for solving integer programming problems[J]. Management Science,1981,27,1:1-18.
[75] Fisher M.L. An application oriented guide to Lagraangian relaxation[J]. Interfaces,1985,15,2:10-21.
[76] Davis L. Job shop scheduling with genetic algorithm[C], In: Grefenstrette J, editor, Proceeding of the First International Conference on Genetic Algorithms, Lawrence Erlbaum Associates Hillsdale, NJ, Pittsburgh, PA,1985,136-140.
[77] Jones A, Rabelo K.Y, et al. A hybrid approach for real-time sequencing and scheduling[J]. International Journal of Computer Integrated Manufacturing,1995,8(2): 145-154.
[78]贺文锐.面向网络协同制造的资源优化配置技术[D].博士论文,西安:西北工业大学,2007.
[79]吕琳.基于Multi-agent的协同制造资源共享的相关理论与技术研究[D].博士论文,武汉:武汉理工大学,2007.
[80]屈喜龙.区域网络化制造中的资源资源管理及其优化配置技术研究[D].博士论文,成都:西南交通大学,2006.
[81]田建锋.支持ASP服务的制造资源描述与评价技术研究及实现[D].硕士论文,西安:西安理工大学,2004.
[82]王丙占.支持ASP服务的制造业资源管理与优化配置技术研究[D].硕士论文,西安:西安理工大学,2003.
[83]李惠林,谢庆生,殷国富等.面向ASP平台的网络化制造资源应用服务研究[J].贵州工业大学学报(自然科学版).2004,33(5):67-70.
[84] Liu Chengying, Wang Xiangkui, He Yuchen.Research on manufacturing resourcemodeling based on the O-O method[J]. Journal of Materials Processing Technol-ogy,2003,(139):40-43.
[85]董朝阳.网络化制造资源优化配置研究[D].博士论文,西安:西北工业大学,2005.
[86] Jim Arlow, Ila Neustadt. UML 2 and the unified process: practical object-oriented analysis and design[M]. Addison Wesley,2005.
[87] Martin Fowler. UML distilled: a brief guide to the standard object modeling language[M]. Addison Wesley,2006.
[88] Craig Larman. Applying UML and Patterns: an Introduction to Object-Oriented Analysis and Design and the Unified Process[M]. Pearson Education,2001.
[89] Holland J.H. Adaptation in Natural and Artificial System[M], An Arbor: The University of Michigan Press,1975.
[90]玄光男,程润伟.遗传算法与工程设计[M].北京:科学出版社,2000:30-98.
[91]陈国良等.遗传算法及其应用[M].北京:人民邮电出版社,1996:12-53.
[92]周明,孙树栋.遗传算法原理及应用[M].北京:国防工业出版社,1999:21-36.
[93] Adleman A.L, Molecular computation of solutions to combinatorial problems [J],Science,1994,266(1):1021-1023.
[94] G. Paun, G. Rozenberg, A. Salomaa. DNA Computing: New Computing Paradigms [M].Springer VerlagGmbH,1998.
[95] Begley J.D. The behavior of adaptive system which employ genetic and correlation algorithm[J]. Dissertation Abstracts Internatioanl,1967.
[96] Goldberg D.E, Lingle R. Alleles, Loci and the traveling salesman problem[C]. Proceedings of an International Conference on Genetic Algorithms and Their Application,1985.
[97] Goldberg D.E. Genetic Algorithms in Search[J], Optimization and Machine Learning. Addison-Wesley,1989.
[98] Gen M, Cheng R.W, Genetic Algorithms and Engineering Design[M]. New York: John Wiley & Sons,1996.
[99] Oliver I, Smith D, Holland J.R. A study of permutation crossover operators on the traveling salesman problem[C]. Proceedings of the 2nd International Conference on Genetic Algorithms, NJ: Lawrence Erlbaum,1987,224-230.
[100] Croce F, Tadei R, Volta G. A genetic algorithm for the job shop problem[J]. Computers and Operations Research,1995,22:15-24.
[101] Davis L. Handbook of Genetic Algorithms[M]. NY: Van Nostrand Reinhold,1991.
[102] Grefenstette J. J. Optimization of control parameters for genetic algorithms[C].IEEE Transactions on System, Man & Cybernetics,1986,16(1):122-128.
[103] Srinivas M, Patnaik L.M. Adaptive probabilities of crossover and mutation in genetic algorithms[C]. IEEE Transactions on System, Man and Cybernetic,1994,24(4):656- 667.
[104] Courant R, Variational methods for the solution of problems of equilibrium and vibrations[J]. Bulletin of the American Mathematical Society,1943,49(1):1-23.
[105] Richardson J.T, M.R. Palmer, G. Liepins, M. Hilliard. Some guidelines for genetic algorithms with penalty functions[C]. In: J.D. Schaffer(Ed.),Proceeding of the 3rd International Conference on Genetic Algorithms, George Mason University, Morgan Kaufmann, Reading, MA,1989:191-197.
[106] Kazarlis S, V. Petridis. Varying fitness functions in genetic algorithms: Studying the rate of increase of the dynamic penalty terms[C]. In: A.E. Eiben, T. Back, M. Schoenauer, H.P. Schwefel(Eds.), Parallel Problem Solving from Nature V-PPSN V, Springer, Amsterdam, Netherlands,1998,1498:211-220.
[107] Smith A. E, D.M. Tate, Genetic optimization using a penalty function[C]. In: S. Forrest(Ed.), Proceedings of the Fifth International Conference on Genetic Algorithms, University of Illinois at Urbana-Champaign, Morgan Kaufmann, San Mateo, CA, July 1993,499-503.
[108] Davis L.(Ed.). Handbook of Genetic Algorithms[M]. Van Nostrand Reinhold, New York,1991.
[109] Michalewicz Z, C.Z. Janikow. Handling constraints in genetic algorithms[M]. In: R.K. Belew, L.B. Booker(Eds.), Proceeding of the Fourth International Conference on Genetic Algorithms, Morgan Kaufmann, San Mateo, CA,1991,151-157.
[110] Gen M, Cheng R.W, Genetic algorithms and engineering optimization[M]. Wiley Interscience, New York,2000.
[111] Deb K. and D. Kalyanmoy. Multi-Objective Optimization Using Evolutionary Algorithms[M]. John Wiley & Sons Ltd, London,2001.
[112] Goldberg D.E. and J. Richardson. Genetic algorithm with sharing for multimodal function optimization[C]. Proc. Of 2nd Int. Conf. Genetic Algorithms, Lawrence Erlbaum Associates,1987:41-49.
[113] Deb K. and D.E. Goldberg. An investigation of niche and species formation in genetic function optimization[C]. Proceeding of the 3rd International Conference on Genetic algorithms,1989:42-50.
[114] Deb K, et al. A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ[J]. IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[115] Jones A, Rabelo K.Y, et al. A hybrid approach for real-time sequencing and scheduling[J]. International Journal of Computer Integrated Manufacturing,1995,8(2): 145-154
[116] Garetti M, Taisch M. Neural networks in production planning and control[J]. Production Planning and Control,1999,10:324-339.
[117] Garetti M, Taisch M. Guest editorial: Advanced computing techniques in production planning and control[J].Production Planning and Control,1999, 10(4):301-304.
[118]丁永生.计算智能——理论、技术与应用[M].北京:科学出版社,2004:36-107.
[119]丁永生,邵世煌,任立红.DNA计算与软计算[M].北京:科学出版社,2002:12-116.
[120]陶吉利.基于DNA计算的遗传算法及应用研究[D].杭州:浙江大学博士论文,2007.
[121]王凌.智能优化算法及其应用[M].北京:清华大学出版社,2001:1-52.
[122]陈宝林.最优化理论与算法[M].北京:清华大学出版社,1989:1-39.
[123] Davis L. Job shop scheduling with genetic algorithm[C], In: Grefenstrette J, editor, Proceeding of the First International Conference on Genetic Algorithms, Lawrence Erlbaum Associates Hillsdale, NJ, Pittsburgh, PA,1985,136-140.
[124]刘西奎.DNA计算和遗传算法的编码与几个优化模型的研究[D].博士论文,武汉:华中科技大学,2003.
[125]黄布毅.DNA计算中若干理论问题的研究[D].博士论文,武汉:华中科技大学,2005.
[126]董亚非.若干DNA计算粘贴模型的研究[D].博士论文,武汉:华中科技大学,2004.
[127]王淑栋.四类DNA计算模型中一些理论与应用的研究[D].博士论文,武汉:华中科技大学,2004.
[128]曲惠琴.DNA计算若干问题研究[D].博士论文,上海:复旦大学,2005.
[129] Martyn Amos. Theoretical and Experimental DNA Computation[M].Springer,2005.
[130] Beave D. Computing with DNA[J]. Journal of Computational Biology. 1995,2(1):1-8.
[131] Neuhauser C, Krones M. The Genealogy of Samples in Models with Selection[J].Genetic s,1997,145(2):519-534.
[132] Smith W.D, Schweitzer A. DNA computers in vitro and vivo[J]. DIMACS Workshop on DNA-based Computing. Princeton,1995.
[133] Winfree E., Liu F., Wenzler L.A. and Seeman N.C. Design and self-assembly of two-dimensional DNA crystals. Nature.1998,394:539-544.
[134] Winfree E. On the computational power of DNA annealing and ligation. In DNA-based computers(Eds., Baum E.B. and Lipton R.J.). American Mathematical Society.1996,199-221.
[135] Kari L., Paun G. Salomaa A., Yu S. and Rozenberg G. DNA computing: Sticker systems and universality[J]. Acta Information.1998,35:401-420.
[136] Lioton R., DNA Solution of Hard Computation Problems[J].Science,1995,268(4):42- 54.
[137] Wu H.Y. An improved surface-based method for DNA computation[J].Biosystems, 2001,59(1):1-5.
[138] Cukras A.R., D. Faulhammer and R.J. Lipton et al. Chess games: a model for RNA based computation[J], BioSystems,1999,52(1-3):35-45.
[139] Li S.C., J. Xu. Operational Rules for Digital Coding of RNA Sequences Based on DNA Computing in high Dimensional Space[J],Bulletin of Science and Technology,2003,18(6):388-398.
[140] Dio H. and M. Furusawa. Evolution is promoted by asymmetrical mutations in DNA replication genetic algorithm with double-stranded DNA[J]. Fujitsu Scientific and Technical Journal(Japan),1996,32(2):248-255.
[141] M. Garzon, P. Neathery, R. Deaton et al. A new metric for DNA computing, in Proceedings of Genetic Programming 1997.1997,472-478.
[142] A.J. Hartemink, D.K. Giord and J. Khodor. Automated constraint-based nucleotide sequence selection for DNA computation, in Proceedings of 4th DIMACS Workshop on DNA Based Computers,1998,227-235.
[143] Chen L, V. Vempati and N. Aljaber. An application of genetic algorithms for flow-shop problems[J], European Journal of Operational Research.1995,80:389-396.
[144] T.F. Smith, M.S. Watemlan. Identification of common molecular subsequences[J]. J.Mol.Biol,1981(147):195-197.
[145] W.R. Pearson, D.J. Lipman. Improved tools for biological sequence comparison[J]. Proc.Natl.Acad.Sci,1988,85:2444-2448.
[146] Ouyang Q. et al. DNA Solution of the Maximal Clique Problem[J]. Science,1997,278 (17):446-449.
[147] Yoshikawa T., T. Furuhashi and Y. Uchikawa. The effects of combination of DNA coding method with pseudo-bacterial GA[C]. Proceedings of 1997 IEEE International Conference on Evolutionary Computation,1997,285-290.
[148]王万良,吴启迪.生产调度智能算法及其应用[M].北京:科学出版社,2007:36-62.
[149]刘民,吴澄.制造过程智能优化调度算法及其应用[M].北京:国防工业出版社,2008:53-101.
[150]黄席樾,张著洪,何传江等.现代智能算法理论及应用[M],科学出版社,2005:11-62.
[151]刘晓霞,谢里阳等.柔性作业车间多目标调度优化研究[J].沈阳:东北大学学报(自然科学版),2008,3:362-365.
[152]耿兆强,邹益仁.基于遗传算法的作业车间模糊调度问题的研究[J].计算机集成制造系统,2007,8(8):616-620.
[153] De Jong. Analysis of the Behavior of a Class of Genetic Adaptive Systems.PhD Thesis, Depart. Of Computer and Communication Sciences, University of Michigan, Ann Arbor,1975.
[154]雷英杰,张善文等.Matlab遗传算法工具箱及应用[M].西安:西安电子科技大学出版社,2005:62-140.
[155]刘维.精通Matlab与C/C++混合程序设计[M].北京:北京航空航天大学出版社,2008:48-131.
[156] Arita M., Hagiya M. and Suyama A. Joining and rotating data with molecules[C]. Proc. 1997 IEEE Int. Conf. on Evolutionary Computation. Indianapolis. IN USA. April 13~16.1997,243-248.
[157] ESPRIT Consortium AMICE. CIMOSA: Open System Architecture for CIM[C].(2nd Revised and Extended Edition)Berlin: Spring Verlag,1993.
[158] Didic M M. CIMOSA Model Creation and Execution for a Casting Process and Manufacturing Cell[J]. Computers in Industry,1994,(24):237-247.
[159] Kosanke K, Zelm M. CIMOSA modeling Processes[J]. Computers in Industry,1999,40(2-3):141-153.
[160] Larman, Craig. Applying UML and patterns: an introduction to object-oriented analysis and design and iterative development[M]. China Machine Press, 2006:121-163.
[161] Simon Robinson. Professional C# 3rd Edition[M]. Wrox Press,2004:238-265.
[162] Jeffrey Richter. Applied Microsoft .NET Framework Programming[M]. Microsoft Press,2005:57-75.
[163] John Kauffman,Thiru Thangarathinam. Beginning ASP.NET 2.0 Databases[M]. Wrox Press, 2002:83-109.
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