基于机器学习的供应链绩效智能分析方法研究
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摘要
面对制造行业快速发展、企业兼并与重组不断的局面,如何从供应链存在的管理问题出发,在对供应链管理水平进行分析评价和动态监控的基础上有针对性的实施改善,进而打造出敏捷、精益、绿色的供应链已成为国内诸多大企业走向国际化的管理阻碍。本文针对制造业复杂供应链绩效问题,依照知识获取的路径,提出一套基于机器学习的智能分析方法。主要讨论供应链绩效数据质量控制、供应链绩效关键因素提取以及供应链绩效管理问题的诊断推理,以实现供应链绩效分析的自动化与智能化。通过研究,本文获得以下一些研究成果。
(1)提出了供应链绩效数据质量控制方法。首先,基于全面数据质量管理的思想,结合抽样调查理论,给出一种多阶段闭环的数据质量控制流程。重点探讨了样本数据中的缺失值插补问题,基于缺失数据处理技术、矩阵分解理论以及数据挖掘技术,给出两种有效的插补算法:基于非负矩阵分解的插补算法和基于分类回归树的插补算法,通过数值实验证明了算法的有效性。
(2)提出一种基于选择性集成的稳健主成分算法,实现供应链关键因素的提取。针对供应链绩效分析的复杂性以及调查数据的不确定性,基于稳健统计理论,实现了投影寻踪主成分算法的稳健化。基于Bagging集成学习算法和选择性集成思想,利用信息融合技术,实现了主成分分析的选择性集成,从而拓展了集成学习在无监督算法中的应用。通过数值实验和实际供应链绩效数据分析证明了该方法的有效性。
(3)应用贝叶斯网实现供应链绩效问题诊断分析。首先分别给出基于模糊解释结构模型和Vague集合的贝叶斯网构建方法,有利于利用专家的先验知识构建模型。通过贝叶斯网的学习算法,得到供应链绩效关键因素对管理问题的影响路径。通过实际数据的分析以及与粗糙集等多种算法对比证明了算法的有效性。
(4)开展了针对国内典型制造企业供应链绩效的智能分析工作。以本文提出的相关理论方法为依托,对国内大型发动机制造企业C公司所在供应链进行绩效智能分析,包括:供应链绩效数据清洗、关键因素提取、影响路径获取。借鉴管理咨询的手段,对绩效进行全面分析。通过对比两种方法的分析结论证明本文所提出的理论方法具备可行性和有效性,可以实现供应链绩效分析的自动化、智能化。最后,基于本文提出的部分理论方法,开发了供应链绩效智能分析系统。
Considering the fast development of manufacturing, enterprise merging and recombination, creating the agile, lean, green supply chain, which is based on analysis and dynamic monitoring, is the main obstacle of the internationalization for domestic large enterprises. A set of intelligent analysis method based on machine learning theory was proposed, according to the manufacturing complex supply chain performance problems and the path of KDD. In order to realize supply chain performance analysis automation and intelligence, the supply chain performance data quality control, the key factors of supply chain performance and supply chain performance management problem in extracting diagnosis reasoning are discussed in this study. The main research results are as following.
(1) The supply chain performance data quality control was proposed. First, based on total data quality management theory, combining the sampling theory, a multi-stage closed-loop data quality control process has been given. Based on the missing data processing technology, decomposing matrix theory, and data mining technology, two kinds of effective imputation algorithm has been given, which based on the decomposition of the nonnegative matrices factorization and CART. The effectiveness of the algorithm is demonstrated by the numerical experiments.
(2) An algorithm of supply chain key factors extraction based on selective ensemble of robust principal component was proposed. According to the complexity of the supply chain performance analysis and investigation data, based on the robust statistic theory, realize the projection pursuit of main component algorithm is stable. Based on Bagging selective ensemble learning algorithm, information fusion technology, realize the principal component analysis selective ensemble.
(3) Application Bayesian network to realize supply chain performance diagnosis. Bayesian network construction methods based on fuzzy ISM and Vague set have been given. Through the Bayesian network learning algorithm, the supply chain performance of management problems of key factors affect path. The validity of the algorithm is verified through the analysis of the actual data and rough sets of comparison algorithm.
(4) Typical domestic manufacturing supply chain performance modeling and analysis practice. Based on the theory and method propose in this paper, a supply chain performance intelligent is analyzed for a domestic engine manufacturing enterprise. By comparing the two kinds of methods of analysis results, the validity and feasibility of supply chain performance analysis is verified, and the automation and intelligence of supply chain performance can be realized. Based on parts of method proposed in this paper, the supply chain performance intelligence analysis system has been developed.
(1)提出了供应链绩效数据质量控制方法。首先,基于全面数据质量管理的思想,结合抽样调查理论,给出一种多阶段闭环的数据质量控制流程。重点探讨了样本数据中的缺失值插补问题,基于缺失数据处理技术、矩阵分解理论以及数据挖掘技术,给出两种有效的插补算法:基于非负矩阵分解的插补算法和基于分类回归树的插补算法,通过数值实验证明了算法的有效性。
(2)提出一种基于选择性集成的稳健主成分算法,实现供应链关键因素的提取。针对供应链绩效分析的复杂性以及调查数据的不确定性,基于稳健统计理论,实现了投影寻踪主成分算法的稳健化。基于Bagging集成学习算法和选择性集成思想,利用信息融合技术,实现了主成分分析的选择性集成,从而拓展了集成学习在无监督算法中的应用。通过数值实验和实际供应链绩效数据分析证明了该方法的有效性。
(3)应用贝叶斯网实现供应链绩效问题诊断分析。首先分别给出基于模糊解释结构模型和Vague集合的贝叶斯网构建方法,有利于利用专家的先验知识构建模型。通过贝叶斯网的学习算法,得到供应链绩效关键因素对管理问题的影响路径。通过实际数据的分析以及与粗糙集等多种算法对比证明了算法的有效性。
(4)开展了针对国内典型制造企业供应链绩效的智能分析工作。以本文提出的相关理论方法为依托,对国内大型发动机制造企业C公司所在供应链进行绩效智能分析,包括:供应链绩效数据清洗、关键因素提取、影响路径获取。借鉴管理咨询的手段,对绩效进行全面分析。通过对比两种方法的分析结论证明本文所提出的理论方法具备可行性和有效性,可以实现供应链绩效分析的自动化、智能化。最后,基于本文提出的部分理论方法,开发了供应链绩效智能分析系统。
Considering the fast development of manufacturing, enterprise merging and recombination, creating the agile, lean, green supply chain, which is based on analysis and dynamic monitoring, is the main obstacle of the internationalization for domestic large enterprises. A set of intelligent analysis method based on machine learning theory was proposed, according to the manufacturing complex supply chain performance problems and the path of KDD. In order to realize supply chain performance analysis automation and intelligence, the supply chain performance data quality control, the key factors of supply chain performance and supply chain performance management problem in extracting diagnosis reasoning are discussed in this study. The main research results are as following.
(1) The supply chain performance data quality control was proposed. First, based on total data quality management theory, combining the sampling theory, a multi-stage closed-loop data quality control process has been given. Based on the missing data processing technology, decomposing matrix theory, and data mining technology, two kinds of effective imputation algorithm has been given, which based on the decomposition of the nonnegative matrices factorization and CART. The effectiveness of the algorithm is demonstrated by the numerical experiments.
(2) An algorithm of supply chain key factors extraction based on selective ensemble of robust principal component was proposed. According to the complexity of the supply chain performance analysis and investigation data, based on the robust statistic theory, realize the projection pursuit of main component algorithm is stable. Based on Bagging selective ensemble learning algorithm, information fusion technology, realize the principal component analysis selective ensemble.
(3) Application Bayesian network to realize supply chain performance diagnosis. Bayesian network construction methods based on fuzzy ISM and Vague set have been given. Through the Bayesian network learning algorithm, the supply chain performance of management problems of key factors affect path. The validity of the algorithm is verified through the analysis of the actual data and rough sets of comparison algorithm.
(4) Typical domestic manufacturing supply chain performance modeling and analysis practice. Based on the theory and method propose in this paper, a supply chain performance intelligent is analyzed for a domestic engine manufacturing enterprise. By comparing the two kinds of methods of analysis results, the validity and feasibility of supply chain performance analysis is verified, and the automation and intelligence of supply chain performance can be realized. Based on parts of method proposed in this paper, the supply chain performance intelligence analysis system has been developed.
引文
[1]中国制造业信息化.www.miechina.com[EB/OL].2010.
[2]李勤,秦中春,梁超杰.企业供应链管理的现状、问题与建议——基于93个样本企业的调查.http://articles.e-works.net.cn[EB/OL].2010.
[3] MIE-制造业信息化数据分析服务平台.www.mie-idc.cn[EB/OL].2010.
[4] T. Davis. Effective supply chain management [J].Sloan Management Review,1993,(2):35-46.
[5] J. W. Forrester. Industrial Dynamics [M].Cambridge: MIT Press, 1996.
[6]杨建华、桑莉.供应链管理的现状与发展趋势[J].工业工程,2003,6(3):13~17.
[7] Ellram L M. Supply Chain management: the industrial organization perspective [J]. International Journal of Physical Distribution and Logistics Management, 1991, 21(1):13~22.
[8]中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会.GB/T 18354-2006 [S],北京:中国标准出版社,2007.
[9] Simon Croom,Pietro Romano,Mihalis Giannakis. Supply Chain Management; an analytical framework for critical literature review, European Journal of Purchasing & Supply Management, 2000(6):67-83.
[10] A. Otto, H. Kotzab, Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain, European journal of operational research, 144, pp.306-320, 2003.
[11]桂良军.供应链成本管理研究[M].北京:中国经济出版社,2006.
[12] Supply-Chain Council. http://www.supply-chain.org.2006.
[13]马士华,林勇,陈志祥.供应链管理[M].北京:机械工业出版社,2000.
[14]马丽娟.关于供应链绩效评价的探讨[J].现代管理科学,2005(10).
[15] J. P. C. Kleijnen, M. T. Smits. Performance metrics in supply chain management. Journal of the Operational Research Society, 2003, 54(5): 507-514.
[16]马士华,李华焰,林勇.平衡记分法在供应链绩效评价中的应用研究.工业工程与管理,2003,7(4):5-10.
[17]中国电子商务协会供应链管理委员会.中国企业供应链管理绩效水平评价参考模型(SCPR1.0)[Z].2003
[18] Bhatnagar, Rohit, Sohal, et al. Supply chain competitiveness: measuring theimpact of location factors, uncertainty and manufacturing practices [J].Technovation, 2005, 25: 443-56.
[19] G. W. Dickson. An analysis of vendor selection systems and decisions, Journal of Purchasing, 1966, 2(1): 5-17.
[20] R. R. Lummus, R. J. Vokurka, K. L. Alber. Strategic supply chain planning. Production and Inventory Management Journal, 1998, 39(3):49-58.
[21] Cooke J A. Measure for measure[J]. Logistics, 1999, 38(7):111-113.
[22] Benita M. Beamon. Measuring Supply Chain Performance. International Journal of Operations and Production Management, 1999, 19 (3/4):275-292.
[23] H.A. Stefan. Systems perspective on supply chain measurements[J]. International journal of Physical Distribution and Logistics Management, 2000, 30(10):847-868.
[24]李贵春,李从东,李龙洙.供应链绩效评价指标体系与评价方法研究[J].管理工程学报,2004,18(1):104-106.
[25]綦方中,钟凌燕,潘晓弘.敏捷供应链绩效评价过程与方法研究[J].计算机集成制造系统,2006,12(9):1472-1476.
[26] S. Holmberg. A system perspective in supply chain measurement, International Journal of Physical Distribution & Logistics Management, 2000, 30(10):847-868.
[27] De Toni, S. Tonchia. Performance measurement systems: Models, characteristics and measures [J].International Journal of Operations & Production Management, 2001, 21(1):46-70.
[28] F. T. S.Chan.Performance measurement in a supply chain [J]. International Journal of Advanced Manufacturing Technology, 2003, 21:534-548.
[29] F. T. S. Chan, H.J.Qi. An innovative performance measurement method for supply chain management [J].Supply Chain Management: An International Journal, 2003, 18(3-4):209-223.
[30] Chunhua Tian, Yueting Chai, Yi Liu, Shouju Ren. Performance management in supply chain [C]. IEEE International Conference on Systems, Man and Cybernetics, 2003, 5:4934-4939.
[31] Gunasekaran A. Kobu. Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications [J]. International Journal for Production Research, 2007, 45(12):2819-2840.
[32] S. Li, S. Rao, T.S. Ragu-Nathan, et al. Development and validation of a measurement instrument for studying supply chain practices [J]. Journal of OperationsManagement, 2005, 23: 618-641.
[33] B.Fynes, C. Voss, S.D.Burca. The impact of supply chain relationship dynamics on manufacturing performance [J].International Journal of Operations & Production Management, 2005, 25(1):6-19.
[34] Zhaofang,X. Li,J. Fu. A new Approach of Supply Network performance measurement based on Supporting level and operational level[C].Technology and Innovation Conference,2006,1537-1545.
[35] Z. Li, X. Xu, Arun kumar. Supply chain performance Evaluation from Structural and operational levels[C].IEEE Conference Emerging Technologies and Factory Automation, 2007: 1131-1140.
[36] Chris Morgan. Supply network performance measurement: future challenges? [J]. The International Journal of Logistics Management, 2007, 18(2):255-273.
[37]霍佳震,隋明刚.企业绩效及供应链绩效评价研究现状[J].同济大学学报:自然科学版,2001,29(8):976-981.
[38]陈志祥.基于ASCM的供需协调绩效评价决策支持系统研究[J].厦门大学学报(自然科学版),2003,10:34-38.
[39] Anson.供应链运作绩效的测试和诊断http://tech.it168.com/ [EO/BL], 2007.
[40] M. M. Naim, P. Childerhouse, S. M. Disney, et al. A supply chain diagnostic methodology: determining the vector of change [J]. Computers & Industrial Engineering, 2002, 43(2): 135-157.
[41] Han-Ying Kao, China-Hui Huang, Han-Lin Li. Supply Chain Diagnostics with Dynamic Bayesian Networks [J]. Computers & Industrial Engineering, 2005, 49: 339-347.
[42] G. Tomas M. Hult, David J. Ketchen Jr., S. Tamer Cavusgil, et al. Knowledge as a strategic resource in supply chain [J]. Journal of Operations Management, 24(2006): 458-475.
[43] L. Whicker,M. Bernon, S. Templar, et al. Understanding the relationships between time and cost to improve supply chain performance [J]. International Journal of Production Economics, 2009, 121: 641-650.
[44]刘俊.基于SCOR绩效评价的供应链诊断模型[J].物流科技,2006,29(135):46~48.
[45]孙洁,李辉.供应链企业信任诊断中指标权重的多专家协商确定方法[J].管理工程学报,2009,23(4):59-63.
[46]史旗凯,郭菊娥.工业企业诊断中的三角模型[J].统计与决策,2007,(3):148-149.
[47]刘俊.基于SCOR绩效评价的供应链诊断模型[J].物流科技,2006,135(29):46-48.
[48]李辉,李向阳,孙洁.供应链伙伴关系诊断管理研究[J].计算机集成制造系统,2007,13(10):2001-2008.
[49]王冬冬,达庆利.基于模糊Petri网的供应链诊断建模分析[J].东南大学学报(自然科学版),2006,36:662-666.
[50]王冬冬.供应链敏捷化和绿色供应链的研究[D].东南大学博士学位论文,2006.
[51]陈国华,张根保,任显林,赵喜.基于故障树分析法的供应链可靠性诊断方法及仿真研究[J].计算机集成制造系统,2009,15(10):2034-2039.
[52]钟昌宝,魏晓平,聂茂林,姜殿玉.供应链环境下物流系统和谐性诊断[J].系统工程理论与实践,2010,30(1):30-37.
[53]任常锐,柴跃廷,刘义.供应链的复杂性分析[C].第五届全球智能控制与自动化大会论文集,2004,3981-3985.
[54] Dehua Zhou. An Empirical Study of the Role of Postponement Application in Reducing Supply Chain Complexity[J].IEEE,2002,448-453.
[55]柴跃廷,刘义.敏捷供需链管理[M].北京:清华大学出版社,2001.
[56]蒋白桦,王丹力,王宏安,戴国忠.从复杂性的角度分析供应链管理问题[J].计算机工程与应用,2002,(15):52-54.
[57]张涛,孙林岩.供应链不确定性管理:技术与策略[M].北京:清华大学出版社,2005.82-93.
[58]张涛,孙林岩,孙海虹,李刚.供应链的系统运作模式分析与建模——基于复杂自适应系统范式的研究[J].系统工程理论与实践,2003,11:8-13.
[59] H. B. Hwarng, N. Xie. Understanding supply chain dynamics: A chaos perspective [J]. European Journal of Operational Research, 2008, 184: 1163-1178.
[60]邹辉霞.供应链管理与复杂性科学[J].科学学与科学技术管理,2003, 24,1:57-60.
[61]穆东.供应链系统的复杂性与评价方法研究[M].北京:清华大学出版社,2010.
[62]王丹力,王宏安,戴国忠.供应链管理的复杂性研究[J].系统仿真学报,2002,14(11):1439-1442.
[63]宋华龄.企业系统管理复杂性评价[M].北京:经济管理出版社,2004.114-115.
[64]贾启君.基于系统动力学的复杂供应链系统协同策略建模分析研究[D].天津大学博士学位论文,2008.
[65]李凡长.机器学习理论及应用[M].安徽:中国科学技术大学出版社,2009.
[66]蒋艳凰,赵强利.机器学习方法[M].北京:电子工业出版社,2009.
[67] T. Dietterich. Machine-learning research: four current directions [J], The AI Magazine, 1998, 18(4):97-136.
[68]顾基发.综合集成方法与系统学研究[M].北京:科学出版社,2007.
[69]钱学森,于景元,戴汝为.一个科学的新领域-开放的复杂巨系统及其方法论[J].自然杂志,1990,13(1):3-10.
[70]戴汝为,王珏,田捷.智能系统的综合集成[M].浙江:浙江科学技术出版社,1995.
[71]钟秉林,黄仁.机械故障诊断学[M].北京:机械工业出版社,1998.
[72]杨叔子,丁洪,史铁林等.基于知识的诊断推理[M].北京:清华大学出版社,1993.
[73]徐宾刚,屈梁生.转子故障贝叶斯诊断网络的研究[J].机械工程学报,2004,40(1):66-72.
[74]陈科.制造型供应链绩效建模分析研究与实践[D].天津大学博士学位论文,2009.
[75]田艳,高集荣.现代统计信息技术[M].北京:中国统计出版社,2008.
[76]金勇进.非抽样误差分析[M].北京:中国统计出版社,1996.
[77] S. Rahgunathna. Impact of information quality and decision-maker quality on decision quality: A theoretical model and simulation analysis [J]. Decision Support Systems, 1999, 26:275-286.
[78]邢建英.关于提高统计数据质量的几点思考[J].统计教育,2005,72:22-23.
[79] R. Y. A. Wang, D. M. Storng. Beyond accuracy: What data quality means to data Consumers [J].Journal of Management Information Systems,1996,12(4): 5-34.
[80]林金灿.对统计误差的几点认识[J].统计与预测,2002,116:32-34.
[81]王颖.企业统计数据质量影响因素研究[D].浙江大学硕士学位论文,2006.
[82]王华,金勇进.统计数据准确性评估:方法分类及适用性分析[J].统计研究,26(1),2009:32-39.
[83] R. Y. A. Wang. Product perspective on Total Data Quality Management [J]. Communications of the ACM, 1998, 41(2): 58-65.
[84] J. Lessler, W. Kalsbeek著,金勇进译.调查中的非抽样误差[M].北京:中国统计出版社,1997.
[85]刘延年.如何评价统计数据的质量与可靠性[J].统计研究,2002(8):61-63.
[86]成邦文,董丽娅,杨峻.研究与开发机构统计数据质量与异常点的对数正态分布检验与识别[J].统计研究,2000(1):42-45.
[87]成邦文,董丽娅.社会经济统计数据的误差分析[J].统计研究,2002(11):61-62.
[88]傅德印.利用探索性数据分析法对统计汇总数据进行质量控制的尝试[J].数理统计与管理,2001(1):17~20.
[89]中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会.GB/T 4883-2008 [S].北京:中国标准出版社,2009.
[90]赵慧,甘仲惟,肖明.多变量统计数据中异常值检验方法的探讨[J].华中师范大学学报(自然科学版),2003,2:6-10.
[91]程开明.统计数据预处理的理论与方法述评[J].统计与信息论坛,2007,22(6):98-103.
[92]刘明吉,王秀峰,黄亚楼.数据挖掘中的数据预处理[J].计算机科学,2000,(27):55-56.
[93] Groves R M, Singer E, Corning A. Leverage-salience theory of survey participation: description and an illustration. Public Opin,Q64:299~308.
[94]武建虎,贺佳,贺宪民,程红岩.多变量缺失数据的不同处理方法及分析结果比较[J].第二军医大学学报,2004,25(9):1013-1016.
[95] J. M. Robins, A. Rotnitzky, L. P. Zhao. Estimation of regression coefficients when some regressors are not always observed [J]. Journal of the American Statistical Association, 1994, 89: 846-866.
[96] J. L. Schafer, M. K. Olsen. Multiple imputations for multivariate missing-data problems: A data analysis’s perspective [J]. Multivariate Behavioral Research,1998, 33: 545-571.
[97] Lundstr?m, S?rndal. Calibration as a standard method for treatment of nonresponce[J]. Journal of Official Statistics, 1999, 15: 305-327.
[98] J. G. Bethlehem. Weighting Nonresponse Adjustments Based on Auxiliary Information [M]. Survey nonrespone, New York: Wiley, 2000.
[99]金勇进,邵军.缺失数据的统计处理[M].北京:中国统计出版社,2009.
[100] Jing Qin, Jun Shao and Biao Zhang. Efficient and Doubly Robust Imputation for Covariate-Dependent Missing Responses [J]. Journal of the American Statistical Association, 2008, 103: 797-810.
[101] J. Scheffe. Dealing with missing data [J]. Res. Lett. Inf. Math. Sci, 2002, 3:153-156.
[102] Shmuel Friedland, Amir Niknejad, Laura Chihara. A simultaneous reconstruction of missing data in DNA microarrays [J]. Linear Algebra and its Applications, 2006, 416: 8-28.
[103] D. D. Lee, H. S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 1999, 401, 788-791.
[104]翟亚利,吴翊.NMF初始化研究及其在文本分类中的应用[J].计算机工程,2008,34(16):191-197.
[105] D. D. Lee, H. S. Seung. Learning the Parts of Objects by Non-Negative Matrix Factorization [J]. Nature, 1999, 401(2):788-791.
[106] D. D. Lee, H. S. Seung. Algorithms for non-negative matrix factorization, Adv. Neural Inf. Process Systems, 2000, 13:556-562.
[107] Stefan Wild, James Curry, Anne Dougherty. Improving non-negative matrix factorizations through structured initialization [J], Pattern Recognition, 2004, 37: 2217-2232.
[108] P. M. Kuhnert, K. A. Do, R. McClure. Combining non-parametric models with logistic regression: an application to motor vehicle injury data [J]. Computational Statistics and Data Analysis, 2000, 34: 371-386.
[109] Sabine Verboven, Karlien Vanden Branden, Peter Goos. Sequential imputation for missing values [J]. Computational Biology and Chemistry, 31 (2007): 320–327.
[110] Troyanskaya, M. Cantor, G. Sherlock, et al, Missing value estimation methods for DNA microarrays [J]. Bioinformatics, 2001, 17: 520–525.
[111] K. Y. Kim, B. J. Kim, G. Su. Yi. Reuse of imputed data in microarray increases imputation efficiency [J]. BMC Bioinformatics 2004, 5: 160.
[112] H. Kim, G. H. Golub, H. Park. Missing value estimation for DNA microarray gene expression data: local least squares imputation [J]. Bioinformatics, 2005, 21: 187–198.
[113] S. Oba, M. A. Sato, I. Takemasa. A Bayesian missing value estimation method for gene expression profile data [J]. Bioinformatics, 2003, 19: 2088–2096.
[114]张宏亭,李学仁,孔韬.BP神经网络在缺失数据估计中的应用[J].计算机工程与设计,2007,28(14):3457-3459.
[115]赵磊,李国和,马现峰.基于支持向量机的缺失数据补齐方法[J].计算机工程与应用,2006, 36: 207-209.
[116]任若恩,王惠文.多元统计数据分析——理论,方法,实例[M].北京:国防工业出版社,1998.
[117]王惠文.偏最小二乘回归方法及其应用[M].北京:国防工业出版社,1999.
[118]王惠文,吴载斌,孟洁.偏最小二乘的线性与非线性方法[M].北京:国防工业出版社,2006.
[119]吴今培.现代数据分析[M].北京:机械工业出版社,2006.
[120] D. F. Specht. A general regression neural network, IEEE Transactions on Neural Transactions on Neural Network, 1991, 2, 568-576.
[121] L. Breiman, J.H.Friedman, R.A,Olshen, et al. Classification and Regression Trees [M]. Belmont, CA: Wadsworth, 1984.
[122]曹铭昌,周广胜,翁恩生.广义模型及分类回归树在物种分布模拟中的应用与比较[J].生态学报,2005,25(8):2031-2010.
[123]姚敏,沈斌,李明芳.基于多准则神经网络与分类回归树的电信行业异动客户识别系统[J].系统工程理论与实践,2004,(5):78-83.
[124]张立彬,张其前,胥芳等.基于分类回归树(CART)方法的统计解析模型的应用与研究[J].浙江工业大学学报,2002,30(4):315-318.
[125] I. S .Markham, R. G.Mathieu, B. A. Waray. A rule induction approach for determining the number of kanbans in a just-in-time production system [J]. Computers & Industrial Engineering, 1998, 34(4):717-727.
[126] E. H.Sorensen, K. L. Miller, C. K. Ooi .The decision tree approach to stock selection [J]. The Journal of Portfolio Management, 2000, 27(1): 42-52.
[127] Guo Wei, Zhao Nan, Shao Hongyu, IT investment efficiency analysis of equipment manufacturing industry based on two-stage nonparametric model[C], International Conference on Challenges in Environmental Science and Computer Engineering, CESCE 2010, 21-24.
[128]赵楠,郭伟,邵宏宇.基于DEA-Tobit模型的装备制造业信息技术投资效率分析[J].统计与决策,2009,21:85-86.
[129]赵楠,邵宏宇,郭伟.IT培训对装备制造业信息化投资效率影响的实证研究[J].情报杂志,2010,29(3):54-56.
[130] Sven Serneels, Tim Verdonck. Principal component analysis for data containing outliers and missing elements [J]. Computational Statistics & Data Analysis, 2008, 52:1712-1727.
[131] B. Walczak, D. L. Massart. Dealing with missing data [J]. Chemometr. Intell. Lab. Syst. 2001, 58: 15-27.
[132] M. Hubert, P. J. Roussecuw, K.Vandriessen. ROBPCA: a new approach to robust principal component analysis, Technometrics, 47(2005):64-79.
[133] S. A. Billing, K. L. Lee. Nonlinear fisher discriminate analysis using a minimum squared error cost function and the orthogonal least squares algorithm [J]. Neural Networks, 2002, 15(2): 263-270.
[134] G. Baudat, F. Anouar. Feature vector selection and projection using kernels[J].
[135]毛勇,周晓波,夏铮等.特征选择算法研究综述[J].模式识别与人工智能,2007,20(2):211-218.
[136]曾黄麟.粗集理论及其应用[M].重庆:重庆大学出版社,1996.
[137] Z. Pawlak. Vagueness and uncertainty: a rough set perspective [J]. International Journal of Computational Intelligence, 1995, 11(2):227-232.
[138]何曙光,齐二石,何桢.多元质量特性过程能力分析的投影寻踪方法[J].管理科学,20(2),2007:9-14.
[139]金菊良,汪淑娟,魏一鸣.动态多指标决策问题的投影寻踪模型[J].中国管理科学,2004,12(1):64-67.
[140] H. L. Zhai, X. G. Chen, Z. D. Hu. A Projection Pursuit Approach to Variable Selection [J]. Chemometrics and Intelligence Laboratory, 2006, 80(1):130-135.
[141]付强,赵小勇.投影寻踪模型原理及其应用[M].北京:科学出版社,2006.
[142] Y. L. Xie, J. J. Wang, Y. Z. Liang, et al. Robust principal component analysis by projection pursuit, Journal of Chemo-metrics, 7(1993): 527-541.
[143]黄幼才.数据探测与抗差估计[M].北京:测绘出版社,1999.
[144] P. J. Roussecuw, C. Croux. Alternatives to median absolute deviation, Journal of the American Statistical Association,88(1993):1273-1283.
[145] L. Christopher, Brown, F. Ramon, et al. Adaptive M-estimators for Use in Structured and Unstructured Robust Covariance Estimation [J]. Bioinformatics, 20(2004):1728-1736.
[146] R. A. Maronna. Principal components and orthogonal regression based on robust scales, Technometrics, 2005, 47:264-273.
[147] J. G. Dy, C. E. Brodley. Feature selection for unsupervised learning [J].Journal of Machine Learning Research, 2004, 5: 845-889.
[148]王斌会,陈一非.基于MCD的稳健主成分算法及其实证分析[J].数理统计与管理,2006,25(4):462-468.
[149] P. J. Roussecuw, K.Vandriessen. A fast algorithm for minimum covariance determinant estimator, Technometrics,41(1999):212-223.
[150] W. J. Conover,崔恒健译.实用非参数统计[M].北京:人民邮电出版社,2006.
[151]陈希孺,柴根象.非参数统计教程[M].上海:华东师范大学出版社,1993.
[152] Z. H. Zhou, J. X. Wu, W. Tang. Ensembling neural networks: many could be better than all [J], 2002, 137(2):239-263.
[153] Xu-Cheng Yin, Chang-Ping Liu, Zhi Han. Feature combination using boosting [J], Pattern Recognition Letters, 2005, 26:2195-2205.
[154] L. Breiman. Bagging Predictors [J], Machine Learning, 1996, 24(2): 123-140.
[155] E. Bauer, R. Kohavi. An empirical comparison of voting classification algorithms: bagging, boosting and variants [J], 1999, 36(2):105-139.
[156] Sridhar S. Kannurpatti, Bharat B. Biswal. Bootstrap resampling method to estimate confidence intervals of activation-induced CBF changes using laser Doppler imaging [J]. Journal of Neuroscience Methods, 2005, 146: 61-68.
[157] Zhou ZH, Tang W. Clusterer ensemble. Technical Report, Nanjing: AI Lab., Department of Computer Science & Technology, Nanjing University, 2002.
[158]李国正,李丹.集成学习中特征选择技术[J].上海大学学报(自然科学版),2007,13(5):598-604.
[159] Strehl A, Ghosh J. Cluster ensembles—A knowledge reuse framework for combiningpartitionings. In: Dechter R, Kearns M, Sutton R, eds. Proc. of the 18th National Conf. on Artificial Intelligence. Menlo Park: AAAI Press, 2002. 93?98.
[160] H. C. Kim, S. N. Pang, H. M. Je, et al. Constructing support vector machine ensemble [J]. Pattern Recognition, 2003, 36(12):2757-2767.
[161]张广全.模糊测度论[M].贵阳:贵州科技出版社,1994.
[162] T. Murofushi, M. Sugeno. Fuzzy Measures and Intergrals [M].New York: Physica Verlag, 2000.
[163] M.Sugeno. Fuzzy measures and fuzzy integrals-A survey, Fuzzy Automatica and Decision Processes, North Holland,Amsterdam,1977:89-102.
[164] T. D. Pham, H. Yan. A Kriging Fuzzy Integral [J], Information Sciense, 1997, 98:157-173.
[165]何永勇,褚福磊,钟秉林.基于模糊积分理论的故障智能集成诊断策略的研究[J].机械科学与技术,2001,20(3):331-333.
[166]刘亚娟,刘韬,崔淑杰.基于距离浓度免疫算法的机械优化设计[J].哈尔滨工业大学学报,2009,36(6):986-989.
[167]张连文,郭海鹏.贝叶斯网引论[M].北京:科学出版社,2006.
[168] Kathryn Blackmond Laskey, Suzanne M. Mahoney. Network Engineering for Agile Belief Network Models [J]. IEEE Transactions on Knowledge and Data Engineering, 2000, 12(4): 487-498.
[169] K. Baumgartner, S. Ferrari, G. Palermo. Constructing Bayesian networks for criminal profiling from limited data [J]. Knowledge-Based Systems, 2008, 21(7): 563-572.
[170]陆汝钤.世纪之交的知识工程与知识科学[M].北京:清华大学出版社,2001.
[171] M. P. Wellman, J. S. Breese, R. P. Gold man. From knowledge bases to decision models [J]. Knowledge Engineering Review, 1992, 7 ( 1 ): 35-53.
[172]杨昌昊,竺长安,胡小建.基于贝叶斯网的复杂系统故障诊断方法[J],中国机械工程,20(22),2009:2726-2732.
[173]叶跃祥,糜仲春,王宏宇等.基于贝叶斯网络的不确定环境下多属性决策方法[J].系统工程理论与实践,2007,4:107-125.
[174]尹晓伟,钱文学,谢里阳.基于贝叶斯网络的多状态系统可靠性建模与评估[J].机械工程学报,2009,45(2):206-212.
[175]李海军,马登武,刘霄等.贝叶斯网络理论在装备故障诊断中的应用[M].北京:国防工业出版社,2009.
[176] Eitel J. M. Lauria, Peter J. Duchessi. A methodology for developing Bayesian networks: An application to information technology (IT) implementation [J, European Journal of Operational Research, 179, 2007: 234-252.
[177] BAESENS B, VERSTRAETEN G, VAN DEN POEL D, et al. Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers[J]. European Journal of Operational Research, 2004,156 (2): 508-523.
[178] P. Bidyuk, A. Terent. Methods of constructing Bayesian networks based on coring functions[J]. Cybernetics &Systems Analysis,2008, 44(2): 219-224.
[179]许丽佳,王厚军,龙兵.基于贝叶斯网络的复杂系统故障预测[J].系统工程与电子技术,2008,30(4):780-784.
[180]吴喜之.现代贝叶斯统计学[M].北京:中国统计出版社,2000.
[181]王双成.贝叶斯网络学习、推理与应用[M].上海:立信会计出版社,2009.
[182] J. Pearl. From Bayesian networks to causal networks[C]. Proceedings of the Adaptive Computing and Information Processing Seminar, Brunel Conference Centre, London, 1993, 25-27.
[183]厉海涛,金光,周经伦等.贝叶斯网络推理算法综述[J].系统工程与电子技术,2008,30(5):935-939.
[184]张尧庭,陈汉峰.贝叶斯统计推断[M].北京:科学出版社,1991.
[185] P. Giudici, R. Castelo. Improving markov chain Monte Carlo model search for data mining [J]. Machine Learning, 2003, 50:127-158.
[186]史会峰,谷根代,姜波.基于MCMC算法贝叶斯网络的学习[J].华北电力大学学报,2004,31(4):109-112.
[187]王广彦,马志军,胡起伟.基于贝叶斯网络的故障树分析[J].系统工程理论与实践,2004,24(6):76-83.
[188] K. W. Przytula. An efficient framework for the conversion of fault trees to diagnostic Bayesian network models[C]. Proceedings of 2006 IEEE Aerospace Conference. Piscataway, USA: IEEE Press, 2006: 1-14.
[189]杨善林,胡小建.复杂决策任务的建模与求解方法[M].北京:科学出版社,2007.
[190]杨善林,胡笑旋,李永森.基于案例和规则推理的贝叶斯网建模[J].哈尔滨工业大学学报,2006,38(10):1644-1648.
[191]倪志伟,YunYang,杨善林等.集成范例推理系统的研究[J].系统仿真学报, 2004, 16(4): 804-806.
[192]李果,高建民,陈富民等.基于多色集合的贝叶斯诊断网络构造方法研究[J].西安交通大学学报,2007,41(1):1262-1266.
[193] D. Koller, A. Preffer. Object-oriented Bayesian networks [C]. Proceedings of the 13th Annual Conference on Uncertainty in Artificial Intelligence. San Mateo, CA: Morgan Kaufmann, 1997: 302-313.
[194]王众托.系统工程引论(第3版)[M].北京:电子工业出版社,2003.
[195] J. N. Warfield. Societal System: Planning, Policy and Complexity [M].New York: Wiley, 1976.
[196]于长锐,罗艳,徐福缘.复杂决策问题的多元化模型体系研究[J].管理科学学报,2004,7(2):88-94.
[197] Yu Aibing, Zhao Nan, Wang Yanlin, et al. Interpretative structural modeling for ceramic grindability system[J]. Key Engineering Materials, 2008, 365-360: 1994-1998.
[198] Kannan G, Haq AN. Analysis of interactions of criteria and sub-criteria for the selection of supplier in the built-in-order supply chain environment. International Journal of Production Research 2007; 45(17):3831–52.
[199] Govindan Kannana, Shaligram Pokharel, P. Sasi Kumar. A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider [J]. Resources, Conservation and Recycling 54 (2009): 28–36.
[200]白思俊.系统工程[M].北京:电子工业出版社,2006.
[201]黄丽,蔡长林.模糊解释结构模型[J].四川大学学报(自然科学版),1999,36(1):6-10.
[202]周德群,卓玲.集成DEMATEL/ISM的复杂系统层次划分研究[J],管理科学学报,2008,2(11):20-26.
[203] Wei-Wen Wu. Choosing knowledge management strategies by using a combined ANP and DEMATEL approach [J]. Expert Systems with Applications, 2008, 35: 828-835.
[204] YUAN-HORNG LIN, JENG-MING YIH. Fuzzy Logic Approach on Cognition Diagnosis with Application on Number Concept for Pupils [C]. Proceedings of the 7th International Conference on Machine Learning and Cybernetics ,Kumming, 12-15,July 2008.3575-3580.
[205] P. S. Raghuvanshi, Satish Kumar. On the Structuring of Systems with Fuzzy Relations [J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 29(4), 1999:547-553.
[206] C. Witteman, S. Renooij. Evaluation of a verbal-numerical probability scale [ J ]. International Journal of Approximate Reasoning, 2003 (1): 97 - 99.
[207]胡建中,许云飞,贾民平等.多代理诊断系统中决策融合研究[J].电子机械工程,21(4),2005:61-64.
[208] K. Atanassov. Intuitionistic fuzzy sets [J]. Fuzzy Sets Syst., 1986, 20: 87-96.
[209] Wen. L. Gau, Daniel. J. Buehrer. Vague sets [J]. IEEE Transactions on Systems, Man and Cybernetics, 1993, 23(2): 610-614.
[210] Anyang Su. Intelligence vague information analysis and decision support system[C]. Proceeding of the 4th World Congress of Intelligent Control and Automation, Shanghai, 2002: 1626-1629.
[211]周晓光,谭春桥,张强.基于Vague集的决策理论与方法[M].北京:科学出版社,2009.
[212] MAK BRENDA, MUNAKATA TOSHINORI. Rule extraction from expert heuristics: a comparative study of rough sets with neural networks and ID3 [J]. European Journal of Operational Research, 2002, 136: 212-229.
[213]何亚群,胡寿松.粗糙集中连续属性离散化的一种新方法[J].南京航空航天大学学报,2003,35(2):212-215.
[214] Huang C, Darwiche A. Inference in Bayesian Networks: A Procedural Guide [J].International Journal of Approximate Reasoning, 1994, 11:1-45.
[215] Kevin Murphy. How to use the Bayes Net Toolbox [EB/OL]. http://bnt.sourceforge.net/usage.html
[216] L.沃塞曼著,吴喜之译.现代非参数统计[M].北京:科学出版社,2008.
[217]马雁军.基于非参数回归技术的DEA两分步法优化与政府绩效评价[J].中国管理科学,2008,16(2):157-164.
[218] L. Simar, P. W. Wilson. Estimation and inference in two-stage, semi-parametric models of productive processes [J]. Journal of Econometrics, 2006, 20(1): 88-97.
[2]李勤,秦中春,梁超杰.企业供应链管理的现状、问题与建议——基于93个样本企业的调查.http://articles.e-works.net.cn[EB/OL].2010.
[3] MIE-制造业信息化数据分析服务平台.www.mie-idc.cn[EB/OL].2010.
[4] T. Davis. Effective supply chain management [J].Sloan Management Review,1993,(2):35-46.
[5] J. W. Forrester. Industrial Dynamics [M].Cambridge: MIT Press, 1996.
[6]杨建华、桑莉.供应链管理的现状与发展趋势[J].工业工程,2003,6(3):13~17.
[7] Ellram L M. Supply Chain management: the industrial organization perspective [J]. International Journal of Physical Distribution and Logistics Management, 1991, 21(1):13~22.
[8]中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会.GB/T 18354-2006 [S],北京:中国标准出版社,2007.
[9] Simon Croom,Pietro Romano,Mihalis Giannakis. Supply Chain Management; an analytical framework for critical literature review, European Journal of Purchasing & Supply Management, 2000(6):67-83.
[10] A. Otto, H. Kotzab, Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain, European journal of operational research, 144, pp.306-320, 2003.
[11]桂良军.供应链成本管理研究[M].北京:中国经济出版社,2006.
[12] Supply-Chain Council. http://www.supply-chain.org.2006.
[13]马士华,林勇,陈志祥.供应链管理[M].北京:机械工业出版社,2000.
[14]马丽娟.关于供应链绩效评价的探讨[J].现代管理科学,2005(10).
[15] J. P. C. Kleijnen, M. T. Smits. Performance metrics in supply chain management. Journal of the Operational Research Society, 2003, 54(5): 507-514.
[16]马士华,李华焰,林勇.平衡记分法在供应链绩效评价中的应用研究.工业工程与管理,2003,7(4):5-10.
[17]中国电子商务协会供应链管理委员会.中国企业供应链管理绩效水平评价参考模型(SCPR1.0)[Z].2003
[18] Bhatnagar, Rohit, Sohal, et al. Supply chain competitiveness: measuring theimpact of location factors, uncertainty and manufacturing practices [J].Technovation, 2005, 25: 443-56.
[19] G. W. Dickson. An analysis of vendor selection systems and decisions, Journal of Purchasing, 1966, 2(1): 5-17.
[20] R. R. Lummus, R. J. Vokurka, K. L. Alber. Strategic supply chain planning. Production and Inventory Management Journal, 1998, 39(3):49-58.
[21] Cooke J A. Measure for measure[J]. Logistics, 1999, 38(7):111-113.
[22] Benita M. Beamon. Measuring Supply Chain Performance. International Journal of Operations and Production Management, 1999, 19 (3/4):275-292.
[23] H.A. Stefan. Systems perspective on supply chain measurements[J]. International journal of Physical Distribution and Logistics Management, 2000, 30(10):847-868.
[24]李贵春,李从东,李龙洙.供应链绩效评价指标体系与评价方法研究[J].管理工程学报,2004,18(1):104-106.
[25]綦方中,钟凌燕,潘晓弘.敏捷供应链绩效评价过程与方法研究[J].计算机集成制造系统,2006,12(9):1472-1476.
[26] S. Holmberg. A system perspective in supply chain measurement, International Journal of Physical Distribution & Logistics Management, 2000, 30(10):847-868.
[27] De Toni, S. Tonchia. Performance measurement systems: Models, characteristics and measures [J].International Journal of Operations & Production Management, 2001, 21(1):46-70.
[28] F. T. S.Chan.Performance measurement in a supply chain [J]. International Journal of Advanced Manufacturing Technology, 2003, 21:534-548.
[29] F. T. S. Chan, H.J.Qi. An innovative performance measurement method for supply chain management [J].Supply Chain Management: An International Journal, 2003, 18(3-4):209-223.
[30] Chunhua Tian, Yueting Chai, Yi Liu, Shouju Ren. Performance management in supply chain [C]. IEEE International Conference on Systems, Man and Cybernetics, 2003, 5:4934-4939.
[31] Gunasekaran A. Kobu. Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995-2004) for research and applications [J]. International Journal for Production Research, 2007, 45(12):2819-2840.
[32] S. Li, S. Rao, T.S. Ragu-Nathan, et al. Development and validation of a measurement instrument for studying supply chain practices [J]. Journal of OperationsManagement, 2005, 23: 618-641.
[33] B.Fynes, C. Voss, S.D.Burca. The impact of supply chain relationship dynamics on manufacturing performance [J].International Journal of Operations & Production Management, 2005, 25(1):6-19.
[34] Zhaofang,X. Li,J. Fu. A new Approach of Supply Network performance measurement based on Supporting level and operational level[C].Technology and Innovation Conference,2006,1537-1545.
[35] Z. Li, X. Xu, Arun kumar. Supply chain performance Evaluation from Structural and operational levels[C].IEEE Conference Emerging Technologies and Factory Automation, 2007: 1131-1140.
[36] Chris Morgan. Supply network performance measurement: future challenges? [J]. The International Journal of Logistics Management, 2007, 18(2):255-273.
[37]霍佳震,隋明刚.企业绩效及供应链绩效评价研究现状[J].同济大学学报:自然科学版,2001,29(8):976-981.
[38]陈志祥.基于ASCM的供需协调绩效评价决策支持系统研究[J].厦门大学学报(自然科学版),2003,10:34-38.
[39] Anson.供应链运作绩效的测试和诊断http://tech.it168.com/ [EO/BL], 2007.
[40] M. M. Naim, P. Childerhouse, S. M. Disney, et al. A supply chain diagnostic methodology: determining the vector of change [J]. Computers & Industrial Engineering, 2002, 43(2): 135-157.
[41] Han-Ying Kao, China-Hui Huang, Han-Lin Li. Supply Chain Diagnostics with Dynamic Bayesian Networks [J]. Computers & Industrial Engineering, 2005, 49: 339-347.
[42] G. Tomas M. Hult, David J. Ketchen Jr., S. Tamer Cavusgil, et al. Knowledge as a strategic resource in supply chain [J]. Journal of Operations Management, 24(2006): 458-475.
[43] L. Whicker,M. Bernon, S. Templar, et al. Understanding the relationships between time and cost to improve supply chain performance [J]. International Journal of Production Economics, 2009, 121: 641-650.
[44]刘俊.基于SCOR绩效评价的供应链诊断模型[J].物流科技,2006,29(135):46~48.
[45]孙洁,李辉.供应链企业信任诊断中指标权重的多专家协商确定方法[J].管理工程学报,2009,23(4):59-63.
[46]史旗凯,郭菊娥.工业企业诊断中的三角模型[J].统计与决策,2007,(3):148-149.
[47]刘俊.基于SCOR绩效评价的供应链诊断模型[J].物流科技,2006,135(29):46-48.
[48]李辉,李向阳,孙洁.供应链伙伴关系诊断管理研究[J].计算机集成制造系统,2007,13(10):2001-2008.
[49]王冬冬,达庆利.基于模糊Petri网的供应链诊断建模分析[J].东南大学学报(自然科学版),2006,36:662-666.
[50]王冬冬.供应链敏捷化和绿色供应链的研究[D].东南大学博士学位论文,2006.
[51]陈国华,张根保,任显林,赵喜.基于故障树分析法的供应链可靠性诊断方法及仿真研究[J].计算机集成制造系统,2009,15(10):2034-2039.
[52]钟昌宝,魏晓平,聂茂林,姜殿玉.供应链环境下物流系统和谐性诊断[J].系统工程理论与实践,2010,30(1):30-37.
[53]任常锐,柴跃廷,刘义.供应链的复杂性分析[C].第五届全球智能控制与自动化大会论文集,2004,3981-3985.
[54] Dehua Zhou. An Empirical Study of the Role of Postponement Application in Reducing Supply Chain Complexity[J].IEEE,2002,448-453.
[55]柴跃廷,刘义.敏捷供需链管理[M].北京:清华大学出版社,2001.
[56]蒋白桦,王丹力,王宏安,戴国忠.从复杂性的角度分析供应链管理问题[J].计算机工程与应用,2002,(15):52-54.
[57]张涛,孙林岩.供应链不确定性管理:技术与策略[M].北京:清华大学出版社,2005.82-93.
[58]张涛,孙林岩,孙海虹,李刚.供应链的系统运作模式分析与建模——基于复杂自适应系统范式的研究[J].系统工程理论与实践,2003,11:8-13.
[59] H. B. Hwarng, N. Xie. Understanding supply chain dynamics: A chaos perspective [J]. European Journal of Operational Research, 2008, 184: 1163-1178.
[60]邹辉霞.供应链管理与复杂性科学[J].科学学与科学技术管理,2003, 24,1:57-60.
[61]穆东.供应链系统的复杂性与评价方法研究[M].北京:清华大学出版社,2010.
[62]王丹力,王宏安,戴国忠.供应链管理的复杂性研究[J].系统仿真学报,2002,14(11):1439-1442.
[63]宋华龄.企业系统管理复杂性评价[M].北京:经济管理出版社,2004.114-115.
[64]贾启君.基于系统动力学的复杂供应链系统协同策略建模分析研究[D].天津大学博士学位论文,2008.
[65]李凡长.机器学习理论及应用[M].安徽:中国科学技术大学出版社,2009.
[66]蒋艳凰,赵强利.机器学习方法[M].北京:电子工业出版社,2009.
[67] T. Dietterich. Machine-learning research: four current directions [J], The AI Magazine, 1998, 18(4):97-136.
[68]顾基发.综合集成方法与系统学研究[M].北京:科学出版社,2007.
[69]钱学森,于景元,戴汝为.一个科学的新领域-开放的复杂巨系统及其方法论[J].自然杂志,1990,13(1):3-10.
[70]戴汝为,王珏,田捷.智能系统的综合集成[M].浙江:浙江科学技术出版社,1995.
[71]钟秉林,黄仁.机械故障诊断学[M].北京:机械工业出版社,1998.
[72]杨叔子,丁洪,史铁林等.基于知识的诊断推理[M].北京:清华大学出版社,1993.
[73]徐宾刚,屈梁生.转子故障贝叶斯诊断网络的研究[J].机械工程学报,2004,40(1):66-72.
[74]陈科.制造型供应链绩效建模分析研究与实践[D].天津大学博士学位论文,2009.
[75]田艳,高集荣.现代统计信息技术[M].北京:中国统计出版社,2008.
[76]金勇进.非抽样误差分析[M].北京:中国统计出版社,1996.
[77] S. Rahgunathna. Impact of information quality and decision-maker quality on decision quality: A theoretical model and simulation analysis [J]. Decision Support Systems, 1999, 26:275-286.
[78]邢建英.关于提高统计数据质量的几点思考[J].统计教育,2005,72:22-23.
[79] R. Y. A. Wang, D. M. Storng. Beyond accuracy: What data quality means to data Consumers [J].Journal of Management Information Systems,1996,12(4): 5-34.
[80]林金灿.对统计误差的几点认识[J].统计与预测,2002,116:32-34.
[81]王颖.企业统计数据质量影响因素研究[D].浙江大学硕士学位论文,2006.
[82]王华,金勇进.统计数据准确性评估:方法分类及适用性分析[J].统计研究,26(1),2009:32-39.
[83] R. Y. A. Wang. Product perspective on Total Data Quality Management [J]. Communications of the ACM, 1998, 41(2): 58-65.
[84] J. Lessler, W. Kalsbeek著,金勇进译.调查中的非抽样误差[M].北京:中国统计出版社,1997.
[85]刘延年.如何评价统计数据的质量与可靠性[J].统计研究,2002(8):61-63.
[86]成邦文,董丽娅,杨峻.研究与开发机构统计数据质量与异常点的对数正态分布检验与识别[J].统计研究,2000(1):42-45.
[87]成邦文,董丽娅.社会经济统计数据的误差分析[J].统计研究,2002(11):61-62.
[88]傅德印.利用探索性数据分析法对统计汇总数据进行质量控制的尝试[J].数理统计与管理,2001(1):17~20.
[89]中华人民共和国国家质量监督检验检疫总局,中国国家标准化管理委员会.GB/T 4883-2008 [S].北京:中国标准出版社,2009.
[90]赵慧,甘仲惟,肖明.多变量统计数据中异常值检验方法的探讨[J].华中师范大学学报(自然科学版),2003,2:6-10.
[91]程开明.统计数据预处理的理论与方法述评[J].统计与信息论坛,2007,22(6):98-103.
[92]刘明吉,王秀峰,黄亚楼.数据挖掘中的数据预处理[J].计算机科学,2000,(27):55-56.
[93] Groves R M, Singer E, Corning A. Leverage-salience theory of survey participation: description and an illustration. Public Opin,Q64:299~308.
[94]武建虎,贺佳,贺宪民,程红岩.多变量缺失数据的不同处理方法及分析结果比较[J].第二军医大学学报,2004,25(9):1013-1016.
[95] J. M. Robins, A. Rotnitzky, L. P. Zhao. Estimation of regression coefficients when some regressors are not always observed [J]. Journal of the American Statistical Association, 1994, 89: 846-866.
[96] J. L. Schafer, M. K. Olsen. Multiple imputations for multivariate missing-data problems: A data analysis’s perspective [J]. Multivariate Behavioral Research,1998, 33: 545-571.
[97] Lundstr?m, S?rndal. Calibration as a standard method for treatment of nonresponce[J]. Journal of Official Statistics, 1999, 15: 305-327.
[98] J. G. Bethlehem. Weighting Nonresponse Adjustments Based on Auxiliary Information [M]. Survey nonrespone, New York: Wiley, 2000.
[99]金勇进,邵军.缺失数据的统计处理[M].北京:中国统计出版社,2009.
[100] Jing Qin, Jun Shao and Biao Zhang. Efficient and Doubly Robust Imputation for Covariate-Dependent Missing Responses [J]. Journal of the American Statistical Association, 2008, 103: 797-810.
[101] J. Scheffe. Dealing with missing data [J]. Res. Lett. Inf. Math. Sci, 2002, 3:153-156.
[102] Shmuel Friedland, Amir Niknejad, Laura Chihara. A simultaneous reconstruction of missing data in DNA microarrays [J]. Linear Algebra and its Applications, 2006, 416: 8-28.
[103] D. D. Lee, H. S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 1999, 401, 788-791.
[104]翟亚利,吴翊.NMF初始化研究及其在文本分类中的应用[J].计算机工程,2008,34(16):191-197.
[105] D. D. Lee, H. S. Seung. Learning the Parts of Objects by Non-Negative Matrix Factorization [J]. Nature, 1999, 401(2):788-791.
[106] D. D. Lee, H. S. Seung. Algorithms for non-negative matrix factorization, Adv. Neural Inf. Process Systems, 2000, 13:556-562.
[107] Stefan Wild, James Curry, Anne Dougherty. Improving non-negative matrix factorizations through structured initialization [J], Pattern Recognition, 2004, 37: 2217-2232.
[108] P. M. Kuhnert, K. A. Do, R. McClure. Combining non-parametric models with logistic regression: an application to motor vehicle injury data [J]. Computational Statistics and Data Analysis, 2000, 34: 371-386.
[109] Sabine Verboven, Karlien Vanden Branden, Peter Goos. Sequential imputation for missing values [J]. Computational Biology and Chemistry, 31 (2007): 320–327.
[110] Troyanskaya, M. Cantor, G. Sherlock, et al, Missing value estimation methods for DNA microarrays [J]. Bioinformatics, 2001, 17: 520–525.
[111] K. Y. Kim, B. J. Kim, G. Su. Yi. Reuse of imputed data in microarray increases imputation efficiency [J]. BMC Bioinformatics 2004, 5: 160.
[112] H. Kim, G. H. Golub, H. Park. Missing value estimation for DNA microarray gene expression data: local least squares imputation [J]. Bioinformatics, 2005, 21: 187–198.
[113] S. Oba, M. A. Sato, I. Takemasa. A Bayesian missing value estimation method for gene expression profile data [J]. Bioinformatics, 2003, 19: 2088–2096.
[114]张宏亭,李学仁,孔韬.BP神经网络在缺失数据估计中的应用[J].计算机工程与设计,2007,28(14):3457-3459.
[115]赵磊,李国和,马现峰.基于支持向量机的缺失数据补齐方法[J].计算机工程与应用,2006, 36: 207-209.
[116]任若恩,王惠文.多元统计数据分析——理论,方法,实例[M].北京:国防工业出版社,1998.
[117]王惠文.偏最小二乘回归方法及其应用[M].北京:国防工业出版社,1999.
[118]王惠文,吴载斌,孟洁.偏最小二乘的线性与非线性方法[M].北京:国防工业出版社,2006.
[119]吴今培.现代数据分析[M].北京:机械工业出版社,2006.
[120] D. F. Specht. A general regression neural network, IEEE Transactions on Neural Transactions on Neural Network, 1991, 2, 568-576.
[121] L. Breiman, J.H.Friedman, R.A,Olshen, et al. Classification and Regression Trees [M]. Belmont, CA: Wadsworth, 1984.
[122]曹铭昌,周广胜,翁恩生.广义模型及分类回归树在物种分布模拟中的应用与比较[J].生态学报,2005,25(8):2031-2010.
[123]姚敏,沈斌,李明芳.基于多准则神经网络与分类回归树的电信行业异动客户识别系统[J].系统工程理论与实践,2004,(5):78-83.
[124]张立彬,张其前,胥芳等.基于分类回归树(CART)方法的统计解析模型的应用与研究[J].浙江工业大学学报,2002,30(4):315-318.
[125] I. S .Markham, R. G.Mathieu, B. A. Waray. A rule induction approach for determining the number of kanbans in a just-in-time production system [J]. Computers & Industrial Engineering, 1998, 34(4):717-727.
[126] E. H.Sorensen, K. L. Miller, C. K. Ooi .The decision tree approach to stock selection [J]. The Journal of Portfolio Management, 2000, 27(1): 42-52.
[127] Guo Wei, Zhao Nan, Shao Hongyu, IT investment efficiency analysis of equipment manufacturing industry based on two-stage nonparametric model[C], International Conference on Challenges in Environmental Science and Computer Engineering, CESCE 2010, 21-24.
[128]赵楠,郭伟,邵宏宇.基于DEA-Tobit模型的装备制造业信息技术投资效率分析[J].统计与决策,2009,21:85-86.
[129]赵楠,邵宏宇,郭伟.IT培训对装备制造业信息化投资效率影响的实证研究[J].情报杂志,2010,29(3):54-56.
[130] Sven Serneels, Tim Verdonck. Principal component analysis for data containing outliers and missing elements [J]. Computational Statistics & Data Analysis, 2008, 52:1712-1727.
[131] B. Walczak, D. L. Massart. Dealing with missing data [J]. Chemometr. Intell. Lab. Syst. 2001, 58: 15-27.
[132] M. Hubert, P. J. Roussecuw, K.Vandriessen. ROBPCA: a new approach to robust principal component analysis, Technometrics, 47(2005):64-79.
[133] S. A. Billing, K. L. Lee. Nonlinear fisher discriminate analysis using a minimum squared error cost function and the orthogonal least squares algorithm [J]. Neural Networks, 2002, 15(2): 263-270.
[134] G. Baudat, F. Anouar. Feature vector selection and projection using kernels[J].
[135]毛勇,周晓波,夏铮等.特征选择算法研究综述[J].模式识别与人工智能,2007,20(2):211-218.
[136]曾黄麟.粗集理论及其应用[M].重庆:重庆大学出版社,1996.
[137] Z. Pawlak. Vagueness and uncertainty: a rough set perspective [J]. International Journal of Computational Intelligence, 1995, 11(2):227-232.
[138]何曙光,齐二石,何桢.多元质量特性过程能力分析的投影寻踪方法[J].管理科学,20(2),2007:9-14.
[139]金菊良,汪淑娟,魏一鸣.动态多指标决策问题的投影寻踪模型[J].中国管理科学,2004,12(1):64-67.
[140] H. L. Zhai, X. G. Chen, Z. D. Hu. A Projection Pursuit Approach to Variable Selection [J]. Chemometrics and Intelligence Laboratory, 2006, 80(1):130-135.
[141]付强,赵小勇.投影寻踪模型原理及其应用[M].北京:科学出版社,2006.
[142] Y. L. Xie, J. J. Wang, Y. Z. Liang, et al. Robust principal component analysis by projection pursuit, Journal of Chemo-metrics, 7(1993): 527-541.
[143]黄幼才.数据探测与抗差估计[M].北京:测绘出版社,1999.
[144] P. J. Roussecuw, C. Croux. Alternatives to median absolute deviation, Journal of the American Statistical Association,88(1993):1273-1283.
[145] L. Christopher, Brown, F. Ramon, et al. Adaptive M-estimators for Use in Structured and Unstructured Robust Covariance Estimation [J]. Bioinformatics, 20(2004):1728-1736.
[146] R. A. Maronna. Principal components and orthogonal regression based on robust scales, Technometrics, 2005, 47:264-273.
[147] J. G. Dy, C. E. Brodley. Feature selection for unsupervised learning [J].Journal of Machine Learning Research, 2004, 5: 845-889.
[148]王斌会,陈一非.基于MCD的稳健主成分算法及其实证分析[J].数理统计与管理,2006,25(4):462-468.
[149] P. J. Roussecuw, K.Vandriessen. A fast algorithm for minimum covariance determinant estimator, Technometrics,41(1999):212-223.
[150] W. J. Conover,崔恒健译.实用非参数统计[M].北京:人民邮电出版社,2006.
[151]陈希孺,柴根象.非参数统计教程[M].上海:华东师范大学出版社,1993.
[152] Z. H. Zhou, J. X. Wu, W. Tang. Ensembling neural networks: many could be better than all [J], 2002, 137(2):239-263.
[153] Xu-Cheng Yin, Chang-Ping Liu, Zhi Han. Feature combination using boosting [J], Pattern Recognition Letters, 2005, 26:2195-2205.
[154] L. Breiman. Bagging Predictors [J], Machine Learning, 1996, 24(2): 123-140.
[155] E. Bauer, R. Kohavi. An empirical comparison of voting classification algorithms: bagging, boosting and variants [J], 1999, 36(2):105-139.
[156] Sridhar S. Kannurpatti, Bharat B. Biswal. Bootstrap resampling method to estimate confidence intervals of activation-induced CBF changes using laser Doppler imaging [J]. Journal of Neuroscience Methods, 2005, 146: 61-68.
[157] Zhou ZH, Tang W. Clusterer ensemble. Technical Report, Nanjing: AI Lab., Department of Computer Science & Technology, Nanjing University, 2002.
[158]李国正,李丹.集成学习中特征选择技术[J].上海大学学报(自然科学版),2007,13(5):598-604.
[159] Strehl A, Ghosh J. Cluster ensembles—A knowledge reuse framework for combiningpartitionings. In: Dechter R, Kearns M, Sutton R, eds. Proc. of the 18th National Conf. on Artificial Intelligence. Menlo Park: AAAI Press, 2002. 93?98.
[160] H. C. Kim, S. N. Pang, H. M. Je, et al. Constructing support vector machine ensemble [J]. Pattern Recognition, 2003, 36(12):2757-2767.
[161]张广全.模糊测度论[M].贵阳:贵州科技出版社,1994.
[162] T. Murofushi, M. Sugeno. Fuzzy Measures and Intergrals [M].New York: Physica Verlag, 2000.
[163] M.Sugeno. Fuzzy measures and fuzzy integrals-A survey, Fuzzy Automatica and Decision Processes, North Holland,Amsterdam,1977:89-102.
[164] T. D. Pham, H. Yan. A Kriging Fuzzy Integral [J], Information Sciense, 1997, 98:157-173.
[165]何永勇,褚福磊,钟秉林.基于模糊积分理论的故障智能集成诊断策略的研究[J].机械科学与技术,2001,20(3):331-333.
[166]刘亚娟,刘韬,崔淑杰.基于距离浓度免疫算法的机械优化设计[J].哈尔滨工业大学学报,2009,36(6):986-989.
[167]张连文,郭海鹏.贝叶斯网引论[M].北京:科学出版社,2006.
[168] Kathryn Blackmond Laskey, Suzanne M. Mahoney. Network Engineering for Agile Belief Network Models [J]. IEEE Transactions on Knowledge and Data Engineering, 2000, 12(4): 487-498.
[169] K. Baumgartner, S. Ferrari, G. Palermo. Constructing Bayesian networks for criminal profiling from limited data [J]. Knowledge-Based Systems, 2008, 21(7): 563-572.
[170]陆汝钤.世纪之交的知识工程与知识科学[M].北京:清华大学出版社,2001.
[171] M. P. Wellman, J. S. Breese, R. P. Gold man. From knowledge bases to decision models [J]. Knowledge Engineering Review, 1992, 7 ( 1 ): 35-53.
[172]杨昌昊,竺长安,胡小建.基于贝叶斯网的复杂系统故障诊断方法[J],中国机械工程,20(22),2009:2726-2732.
[173]叶跃祥,糜仲春,王宏宇等.基于贝叶斯网络的不确定环境下多属性决策方法[J].系统工程理论与实践,2007,4:107-125.
[174]尹晓伟,钱文学,谢里阳.基于贝叶斯网络的多状态系统可靠性建模与评估[J].机械工程学报,2009,45(2):206-212.
[175]李海军,马登武,刘霄等.贝叶斯网络理论在装备故障诊断中的应用[M].北京:国防工业出版社,2009.
[176] Eitel J. M. Lauria, Peter J. Duchessi. A methodology for developing Bayesian networks: An application to information technology (IT) implementation [J, European Journal of Operational Research, 179, 2007: 234-252.
[177] BAESENS B, VERSTRAETEN G, VAN DEN POEL D, et al. Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers[J]. European Journal of Operational Research, 2004,156 (2): 508-523.
[178] P. Bidyuk, A. Terent. Methods of constructing Bayesian networks based on coring functions[J]. Cybernetics &Systems Analysis,2008, 44(2): 219-224.
[179]许丽佳,王厚军,龙兵.基于贝叶斯网络的复杂系统故障预测[J].系统工程与电子技术,2008,30(4):780-784.
[180]吴喜之.现代贝叶斯统计学[M].北京:中国统计出版社,2000.
[181]王双成.贝叶斯网络学习、推理与应用[M].上海:立信会计出版社,2009.
[182] J. Pearl. From Bayesian networks to causal networks[C]. Proceedings of the Adaptive Computing and Information Processing Seminar, Brunel Conference Centre, London, 1993, 25-27.
[183]厉海涛,金光,周经伦等.贝叶斯网络推理算法综述[J].系统工程与电子技术,2008,30(5):935-939.
[184]张尧庭,陈汉峰.贝叶斯统计推断[M].北京:科学出版社,1991.
[185] P. Giudici, R. Castelo. Improving markov chain Monte Carlo model search for data mining [J]. Machine Learning, 2003, 50:127-158.
[186]史会峰,谷根代,姜波.基于MCMC算法贝叶斯网络的学习[J].华北电力大学学报,2004,31(4):109-112.
[187]王广彦,马志军,胡起伟.基于贝叶斯网络的故障树分析[J].系统工程理论与实践,2004,24(6):76-83.
[188] K. W. Przytula. An efficient framework for the conversion of fault trees to diagnostic Bayesian network models[C]. Proceedings of 2006 IEEE Aerospace Conference. Piscataway, USA: IEEE Press, 2006: 1-14.
[189]杨善林,胡小建.复杂决策任务的建模与求解方法[M].北京:科学出版社,2007.
[190]杨善林,胡笑旋,李永森.基于案例和规则推理的贝叶斯网建模[J].哈尔滨工业大学学报,2006,38(10):1644-1648.
[191]倪志伟,YunYang,杨善林等.集成范例推理系统的研究[J].系统仿真学报, 2004, 16(4): 804-806.
[192]李果,高建民,陈富民等.基于多色集合的贝叶斯诊断网络构造方法研究[J].西安交通大学学报,2007,41(1):1262-1266.
[193] D. Koller, A. Preffer. Object-oriented Bayesian networks [C]. Proceedings of the 13th Annual Conference on Uncertainty in Artificial Intelligence. San Mateo, CA: Morgan Kaufmann, 1997: 302-313.
[194]王众托.系统工程引论(第3版)[M].北京:电子工业出版社,2003.
[195] J. N. Warfield. Societal System: Planning, Policy and Complexity [M].New York: Wiley, 1976.
[196]于长锐,罗艳,徐福缘.复杂决策问题的多元化模型体系研究[J].管理科学学报,2004,7(2):88-94.
[197] Yu Aibing, Zhao Nan, Wang Yanlin, et al. Interpretative structural modeling for ceramic grindability system[J]. Key Engineering Materials, 2008, 365-360: 1994-1998.
[198] Kannan G, Haq AN. Analysis of interactions of criteria and sub-criteria for the selection of supplier in the built-in-order supply chain environment. International Journal of Production Research 2007; 45(17):3831–52.
[199] Govindan Kannana, Shaligram Pokharel, P. Sasi Kumar. A hybrid approach using ISM and fuzzy TOPSIS for the selection of reverse logistics provider [J]. Resources, Conservation and Recycling 54 (2009): 28–36.
[200]白思俊.系统工程[M].北京:电子工业出版社,2006.
[201]黄丽,蔡长林.模糊解释结构模型[J].四川大学学报(自然科学版),1999,36(1):6-10.
[202]周德群,卓玲.集成DEMATEL/ISM的复杂系统层次划分研究[J],管理科学学报,2008,2(11):20-26.
[203] Wei-Wen Wu. Choosing knowledge management strategies by using a combined ANP and DEMATEL approach [J]. Expert Systems with Applications, 2008, 35: 828-835.
[204] YUAN-HORNG LIN, JENG-MING YIH. Fuzzy Logic Approach on Cognition Diagnosis with Application on Number Concept for Pupils [C]. Proceedings of the 7th International Conference on Machine Learning and Cybernetics ,Kumming, 12-15,July 2008.3575-3580.
[205] P. S. Raghuvanshi, Satish Kumar. On the Structuring of Systems with Fuzzy Relations [J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, 29(4), 1999:547-553.
[206] C. Witteman, S. Renooij. Evaluation of a verbal-numerical probability scale [ J ]. International Journal of Approximate Reasoning, 2003 (1): 97 - 99.
[207]胡建中,许云飞,贾民平等.多代理诊断系统中决策融合研究[J].电子机械工程,21(4),2005:61-64.
[208] K. Atanassov. Intuitionistic fuzzy sets [J]. Fuzzy Sets Syst., 1986, 20: 87-96.
[209] Wen. L. Gau, Daniel. J. Buehrer. Vague sets [J]. IEEE Transactions on Systems, Man and Cybernetics, 1993, 23(2): 610-614.
[210] Anyang Su. Intelligence vague information analysis and decision support system[C]. Proceeding of the 4th World Congress of Intelligent Control and Automation, Shanghai, 2002: 1626-1629.
[211]周晓光,谭春桥,张强.基于Vague集的决策理论与方法[M].北京:科学出版社,2009.
[212] MAK BRENDA, MUNAKATA TOSHINORI. Rule extraction from expert heuristics: a comparative study of rough sets with neural networks and ID3 [J]. European Journal of Operational Research, 2002, 136: 212-229.
[213]何亚群,胡寿松.粗糙集中连续属性离散化的一种新方法[J].南京航空航天大学学报,2003,35(2):212-215.
[214] Huang C, Darwiche A. Inference in Bayesian Networks: A Procedural Guide [J].International Journal of Approximate Reasoning, 1994, 11:1-45.
[215] Kevin Murphy. How to use the Bayes Net Toolbox [EB/OL]. http://bnt.sourceforge.net/usage.html
[216] L.沃塞曼著,吴喜之译.现代非参数统计[M].北京:科学出版社,2008.
[217]马雁军.基于非参数回归技术的DEA两分步法优化与政府绩效评价[J].中国管理科学,2008,16(2):157-164.
[218] L. Simar, P. W. Wilson. Estimation and inference in two-stage, semi-parametric models of productive processes [J]. Journal of Econometrics, 2006, 20(1): 88-97.