IT项目评标决策支持模型的研究
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摘要
现代社会的信息化进程促使IT项目在企业、政府等单位的发展过程中的地位越来越显著。许多重要的IT项目开始采用了招标投标的方式,以确保项目高质量、高效率、以合适的成本完成。但此类项目的评标具有金额大、指标多、投标商实力接近、指标关联度复杂和过程透明度低等特点,其高度的非结构化特征使评标决策过程变得愈加复杂。因此结合现代评标理论和模型算法,如本文的粗糙集和灰色系统理论,应用到IT类项目的评标工作,采用信息处理技术建立决策支持系统,用以优化评标准确度,提高评标效率,成为当今解决评标问题的有效手段。
本文首先分析总结了粗糙集、项目评标以及智能决策支持系统等方面取得的主要理论研究成果和实际应用。接着按照项目评标的原则分析,采取了德尔菲方法设计、建立并分析验证了评价指标体系,同时介绍了当前主要的评标方法、模型,给出了完成的项目评标工作流程。
然后,在详细介绍了粗糙集和灰聚类决策理论的基础上提出了基于这两者的项目评标决策模型:利用Rosetta软件进行属性约简和决策规则的提取,在此基础上结合层次分析法计算出各级指标的权重和属性重要度,利用灰聚类决策模型对投标商进行综合评价,简化了评标过程,提高了其客观性和科学性。
最后,参考智能决策支持系统的结构框架,采用基于关系型数据库的决策表操作、规则推理和模型综合评价的合作推理机制,对该评标决策支持模型的应用进行了实例研究,充分利用粗糙集、灰色系统理论方法的优势和信息技术的处理效率,提高了评标工作的准确性、公正性和用户的决策能力。
论文对IT项目评标模型的研究不仅可以扩展到其他类型的项目评价决策活动,也可以在其它领域的决策问题中起到一定的借鉴作用。
The informationization of our society makes IT projects play a more and more important role in this progress of enterprises, governments etc. In order to make sure projects are completed in high quality, efficiency and at a suitable cost, many important IT projects are started with the help of project bidding. However, the bidding evaluation of such projects has the following characteristics: the great amount of money involved, lots of indicators contained, closer capability among bidders, complicated relationship of indicators, low transparence of bidding process, and so on. Due to these highly un-structural features, the process of bidding decision making is much more difficult. Therefore, applying modern bidding theories and algorithm models, such as Rough Set and Grey System theories, to IT projects bidding evaluation process, with the help of information technology in decision support system building, could be an effective method to solve such problems. It could also optimize the veracity and improve the efficiency of the evaluation process.
First of all, the paper makes. a summary of the research achievements and practical applications of projects bidding evaluation theories, rough set, and Grey System theory. Then according to indicator system designing principles, the Delphi method is used to design and build evaluation indicator system. Current main project bidding evaluation methods and models are introduced here, and a complete evaluation working process is established.
Secondly, a project bidding evaluation decision model based on the detailed introduced theories of rough set and grey clustering is described in detail, including reducing the indicator properties, distilling decision rules using Rosetta software, computing for the importance weights of indicators based on Rough Set and AHP theory, and applying them to the grey clustering model to evaluate the bidders comprehensively. The model simplifies the evaluation process and improves the objectiveness and science of bidding.
Lastly, according to the intellectual decision support system theory, the paper does an example experiment of this bidding decision support model, with the reasoning mechanism of decision table operations using relation database, rule-based reasoning and comprehensive evaluation model, which fully takes advantages of rough set algorithm, grey system theory, decision support system framework and information technology. The model improves the exactness, impartiality and the decision making ability of clients.
The paper's research on IT projects bidding evaluation modeling could not only expand to other kinds of project decision making, but also could be certain references for decision problems in other working or research fields.
本文首先分析总结了粗糙集、项目评标以及智能决策支持系统等方面取得的主要理论研究成果和实际应用。接着按照项目评标的原则分析,采取了德尔菲方法设计、建立并分析验证了评价指标体系,同时介绍了当前主要的评标方法、模型,给出了完成的项目评标工作流程。
然后,在详细介绍了粗糙集和灰聚类决策理论的基础上提出了基于这两者的项目评标决策模型:利用Rosetta软件进行属性约简和决策规则的提取,在此基础上结合层次分析法计算出各级指标的权重和属性重要度,利用灰聚类决策模型对投标商进行综合评价,简化了评标过程,提高了其客观性和科学性。
最后,参考智能决策支持系统的结构框架,采用基于关系型数据库的决策表操作、规则推理和模型综合评价的合作推理机制,对该评标决策支持模型的应用进行了实例研究,充分利用粗糙集、灰色系统理论方法的优势和信息技术的处理效率,提高了评标工作的准确性、公正性和用户的决策能力。
论文对IT项目评标模型的研究不仅可以扩展到其他类型的项目评价决策活动,也可以在其它领域的决策问题中起到一定的借鉴作用。
The informationization of our society makes IT projects play a more and more important role in this progress of enterprises, governments etc. In order to make sure projects are completed in high quality, efficiency and at a suitable cost, many important IT projects are started with the help of project bidding. However, the bidding evaluation of such projects has the following characteristics: the great amount of money involved, lots of indicators contained, closer capability among bidders, complicated relationship of indicators, low transparence of bidding process, and so on. Due to these highly un-structural features, the process of bidding decision making is much more difficult. Therefore, applying modern bidding theories and algorithm models, such as Rough Set and Grey System theories, to IT projects bidding evaluation process, with the help of information technology in decision support system building, could be an effective method to solve such problems. It could also optimize the veracity and improve the efficiency of the evaluation process.
First of all, the paper makes. a summary of the research achievements and practical applications of projects bidding evaluation theories, rough set, and Grey System theory. Then according to indicator system designing principles, the Delphi method is used to design and build evaluation indicator system. Current main project bidding evaluation methods and models are introduced here, and a complete evaluation working process is established.
Secondly, a project bidding evaluation decision model based on the detailed introduced theories of rough set and grey clustering is described in detail, including reducing the indicator properties, distilling decision rules using Rosetta software, computing for the importance weights of indicators based on Rough Set and AHP theory, and applying them to the grey clustering model to evaluate the bidders comprehensively. The model simplifies the evaluation process and improves the objectiveness and science of bidding.
Lastly, according to the intellectual decision support system theory, the paper does an example experiment of this bidding decision support model, with the reasoning mechanism of decision table operations using relation database, rule-based reasoning and comprehensive evaluation model, which fully takes advantages of rough set algorithm, grey system theory, decision support system framework and information technology. The model improves the exactness, impartiality and the decision making ability of clients.
The paper's research on IT projects bidding evaluation modeling could not only expand to other kinds of project decision making, but also could be certain references for decision problems in other working or research fields.
引文
[1]赵曾海.招标投标操作实务[M].北京:首都经济贸易大学出版社,2006.
[2]张莹.我国招标投标的理论与实践研究[D](博士学位论文).杭州:浙江大学,2002.
[3]江卫丰,冯东梅.建筑工程施工评标常用方法存在问题研究[J].建筑经济,2007,7:276-278.
[4]http://www.chinabidding.com.cn中国国际招标网
[5]吴广泽.招投标中评标指标的分解、量化及评定[J].建筑经济,1998(6):32-34.
[6]Pawlak Z.Rough set-Theoretical aspects of reasoning about data[M].London:Kluwer Academic Publishers,1991.
[7]Pawlak Z.Rough Set,Rough Relations and Rough Functions[J].Fundamental Informaticae.1996.27(2.3):103-108.
[8]张文修,吴伟志.粗糙集理论介绍和研究综述[J].模糊系统与数学,2000,14(4):1-12.
[9]刘清.Rough集及Rough推理[M].北京:科学出版社,2001.
[10]刘清.Rough set理论:现状与前景[J].计算机科学,1997,24(4):1-5.
[11]Shan N,Ziarko W.Data-based acquisition and incremental modification of classification rules[J].Computational Intelligence,1995,11:357-370.
[12]Choubey S K,Deougun J S,Raghavan V,et al.A comparison of feature selection algorithm in the context of rough classifiers[C].In:5th IEEE Int Conf Fuzzy Systems,New Orleans,1996:1122-128.
[13]王珏等.基于Rough Set理论的“数据浓缩”[J].计算机学报,1998(5):393-400.
[14]Jelonek J,Krawiec K,Slowinski R.Rough set reduction of attributes and their domains for neural networks[J].Computational Intelligence,1995,11(2):339-347.
[15]Lingras P,Davies C.Rough Genetic algorithms[C].In:Proc 7th Intl Wksp on RSFD,Springer,1999:38-46.
[16]李永敏,朱善君等.根据粗糙集理论进行BP网络设计的研究[J].系统工程理论与实践,1999(4):62-70.
[17]Beaubouef T,Petry F E.A rough set model for relational databases[C].In:Proc of RSFD' 93,Springer-Verlag,1994:100-107.
[18]Greco S,Matarazzo B,Slowinksi R.Fuzzy measure technique for rough set analysis[C].In:6th Euro Congress Intelligent Techniques&soft Computing,Germany,1998:99-103.
[19]Ohrn A,Komorowski J.ROSETTA:A Rough set toolkit of analysis of data[C].In:Fifth International Workshop on Rough Sets and Soft Computing,Durham,USA,1997:403-407.
[20]Kounoenko L,Bratko I,Roskar F.Experiments in automatic learning of medical diagnostic rules[M].Jozef Stefan Institute,Ljubljana,Yugoslavia,1984.
[21]秦中广等.粗糙神经网络及其在中医智能诊断中的应用[J].计算机工程与应用,2001(18):34-36.
[22]巩玉玺等.粗集理论在证券投资决策中的应用[J].青岛建筑工程学院学报,2001,22(1):46-49.
[23]Deng Julong.Control Problems of Grey Systems[M].System and Control Letters,1982,5(1):288-294.
[24]邓聚龙.灰预测与灰决策[M].武汉:华中科技大学出版社,2000.
[25]熊和金,陈德军.基于灰色系统理论的数据挖掘技术[J].系统工程与电子技术,2004,26(2):184-186.
[26]潘红华,胡家升,张圣云等.基于灰色系统模型的预测函数控制方法研究[J].控制与决策,2004,19(1):117-119.
[27]鲍玉昆,张金隆.产业化类科技招标项目评标指标体系研究[J].科技进步与对策,2002(6):101-102
[28]阎雅纳.高校图书馆采购印刷型外刊评价体系的研究[D](硕士学位论文).大连:大连理工大学,2005.
[29]郭强.问卷设计手册[M].北京:中国时代经济出版社,2004.
[30]丛国栋.基于粗糙集的IT项目评标决策模型及其支持系统研究[D](硕士学位论文).武汉:华中科技大学,2004.
[31]李慧荣.通用IT项目成功标准评价指标的研究[D](硕士学位论文).杭州:浙江大学,2005.
[32]薛香恒.建设工程评标中模糊综合评判数学模型的建立及应用[J].南昌大学学报,2006,3(6):627-630.
[33]王凤秀.层次分析法在招标采购中应用的探讨[J].铁道物资科学管理,1999,3:28-29.
[34]吴贤标.层次分析法在工程评标中的应用[J].科技信息,2007,25:113-115.
[35]杨建刚.人工神经网络实用教程[M].杭州:浙江大学出版社,2001.
[36]鲍玉昆,张金隆.基于BP神经网络的科技项目评标模型[J].华中科技大学学报,2002,30(8):85-87.
[37]傅荣林.主成分综合评价模型的探讨[J].系统工程理论与实践,2001,11:68-74.
[38]秦寿康.主成分价值函数模型及其评价方案择优方法[J].系统科学与数学,2001,21(4):465-47.
[39]伞冶,叶玉玲.粗糙集理论及其在智能系统中的应用[J].智能系统学报,2007,2(2):40-47.
[40]孔繁胜.知识库系统原理[M].杭州:浙江大学出版社,2000.
[41]鲍雨梅,盛颂恩,孙礼弘.往复式压缩机故障诊断专家系统知识库设计[J].机械工程师,2003,2:18-21.
[42]Chen.K,Pettorossi A.,Ras Z.,Skowron A.Attributes and rough properties in information system[J].International Journal of Approximate Reasoning,1998,2(4):365-376.
[43]张文修,吴伟志,梁吉业等.粗糙集理论与方法[M].北京:科学出版社.2001.
[44]苗夺谦.Rough Set理论中连续属性的离散化方法[J].自动化学报,2001,27(3):296-302.
[45]刘明霄.基于粗糙集的属性约简及其应用研究[D](硕士学位论文).石家庄:河北工业大学,2007.
[46]安海忠,郑链,王广祥.粗糙集知识发现的研究现状和展望[J].计算机测量与控制,2003,11(2):81-84.
[47]http://rosetta.lcb.uu.se
[48]程玉胜.Rosetta实验系统在机器学习中的应用[J].安庆师范学院学报,2005,11(2):69-72.
[49]吴福保,李奇,宋文忠.基于粗集理论知识表达系统的一种归纳学习方法[J].控制与决策,1999,14(3):206-211.
[50]孙翎.基于粗糙集的CBR系统案例特征项权值确定[J].计算机工程与应用,2003,30:44-46.
[51]邓聚龙.灰色系统理论[M].武汉:华中理工大学出版社,1998.
[52]刘思峰,郭天榜,党耀国.灰色系统理论及其应用(第二版)[M].北京:科学出版社,1999.
[53]陈文伟.决策支持系统及开发(第二版)[M].北京:清华大学出版社,2000.
[54]任明仑,杨善林,朱卫东.智能决策支持系统:研究现状与挑战[J].系统工程学报,2002, 17(5):430-440.
[55]王兵,王玉莹,莫建军.导弹发射决策专家系统推理机研究与实现[J].系统工程与电子技术,2003,25(10):1242-1245.
[56]苟朝品,林筑英.智能决策支持系统总体设计初探[J].微计算机应用,1992,13(3):26-30.
[57]贾焰,王志英.知识库系统原理与技术[M].长沙:国防科技大学出版社,1993.
[58]孔繁胜.知识库系统原理[M].杭州:浙江大学出版社,2000.
[59]姜开宇,孙传亭,王敏杰等.注射模知识库系统的构建[J].模具工业,2004,3:3-6.
[60]王晓明,侯英玮.基于数据库的专家系统设计[J].计算机工程与应用,2001,20:95-97.
[61]吴陆恒,颜石.矿山选厂设备维修管理专家系统知识库的设计[J].矿山机械,2003,8:53-56.
[62]安利平,张建勇,仝凌云.专家系统中基于粗集的知识获取、更新与推理[J].计算机工程与设计,2004,25(1):42-46.
[2]张莹.我国招标投标的理论与实践研究[D](博士学位论文).杭州:浙江大学,2002.
[3]江卫丰,冯东梅.建筑工程施工评标常用方法存在问题研究[J].建筑经济,2007,7:276-278.
[4]http://www.chinabidding.com.cn中国国际招标网
[5]吴广泽.招投标中评标指标的分解、量化及评定[J].建筑经济,1998(6):32-34.
[6]Pawlak Z.Rough set-Theoretical aspects of reasoning about data[M].London:Kluwer Academic Publishers,1991.
[7]Pawlak Z.Rough Set,Rough Relations and Rough Functions[J].Fundamental Informaticae.1996.27(2.3):103-108.
[8]张文修,吴伟志.粗糙集理论介绍和研究综述[J].模糊系统与数学,2000,14(4):1-12.
[9]刘清.Rough集及Rough推理[M].北京:科学出版社,2001.
[10]刘清.Rough set理论:现状与前景[J].计算机科学,1997,24(4):1-5.
[11]Shan N,Ziarko W.Data-based acquisition and incremental modification of classification rules[J].Computational Intelligence,1995,11:357-370.
[12]Choubey S K,Deougun J S,Raghavan V,et al.A comparison of feature selection algorithm in the context of rough classifiers[C].In:5th IEEE Int Conf Fuzzy Systems,New Orleans,1996:1122-128.
[13]王珏等.基于Rough Set理论的“数据浓缩”[J].计算机学报,1998(5):393-400.
[14]Jelonek J,Krawiec K,Slowinski R.Rough set reduction of attributes and their domains for neural networks[J].Computational Intelligence,1995,11(2):339-347.
[15]Lingras P,Davies C.Rough Genetic algorithms[C].In:Proc 7th Intl Wksp on RSFD,Springer,1999:38-46.
[16]李永敏,朱善君等.根据粗糙集理论进行BP网络设计的研究[J].系统工程理论与实践,1999(4):62-70.
[17]Beaubouef T,Petry F E.A rough set model for relational databases[C].In:Proc of RSFD' 93,Springer-Verlag,1994:100-107.
[18]Greco S,Matarazzo B,Slowinksi R.Fuzzy measure technique for rough set analysis[C].In:6th Euro Congress Intelligent Techniques&soft Computing,Germany,1998:99-103.
[19]Ohrn A,Komorowski J.ROSETTA:A Rough set toolkit of analysis of data[C].In:Fifth International Workshop on Rough Sets and Soft Computing,Durham,USA,1997:403-407.
[20]Kounoenko L,Bratko I,Roskar F.Experiments in automatic learning of medical diagnostic rules[M].Jozef Stefan Institute,Ljubljana,Yugoslavia,1984.
[21]秦中广等.粗糙神经网络及其在中医智能诊断中的应用[J].计算机工程与应用,2001(18):34-36.
[22]巩玉玺等.粗集理论在证券投资决策中的应用[J].青岛建筑工程学院学报,2001,22(1):46-49.
[23]Deng Julong.Control Problems of Grey Systems[M].System and Control Letters,1982,5(1):288-294.
[24]邓聚龙.灰预测与灰决策[M].武汉:华中科技大学出版社,2000.
[25]熊和金,陈德军.基于灰色系统理论的数据挖掘技术[J].系统工程与电子技术,2004,26(2):184-186.
[26]潘红华,胡家升,张圣云等.基于灰色系统模型的预测函数控制方法研究[J].控制与决策,2004,19(1):117-119.
[27]鲍玉昆,张金隆.产业化类科技招标项目评标指标体系研究[J].科技进步与对策,2002(6):101-102
[28]阎雅纳.高校图书馆采购印刷型外刊评价体系的研究[D](硕士学位论文).大连:大连理工大学,2005.
[29]郭强.问卷设计手册[M].北京:中国时代经济出版社,2004.
[30]丛国栋.基于粗糙集的IT项目评标决策模型及其支持系统研究[D](硕士学位论文).武汉:华中科技大学,2004.
[31]李慧荣.通用IT项目成功标准评价指标的研究[D](硕士学位论文).杭州:浙江大学,2005.
[32]薛香恒.建设工程评标中模糊综合评判数学模型的建立及应用[J].南昌大学学报,2006,3(6):627-630.
[33]王凤秀.层次分析法在招标采购中应用的探讨[J].铁道物资科学管理,1999,3:28-29.
[34]吴贤标.层次分析法在工程评标中的应用[J].科技信息,2007,25:113-115.
[35]杨建刚.人工神经网络实用教程[M].杭州:浙江大学出版社,2001.
[36]鲍玉昆,张金隆.基于BP神经网络的科技项目评标模型[J].华中科技大学学报,2002,30(8):85-87.
[37]傅荣林.主成分综合评价模型的探讨[J].系统工程理论与实践,2001,11:68-74.
[38]秦寿康.主成分价值函数模型及其评价方案择优方法[J].系统科学与数学,2001,21(4):465-47.
[39]伞冶,叶玉玲.粗糙集理论及其在智能系统中的应用[J].智能系统学报,2007,2(2):40-47.
[40]孔繁胜.知识库系统原理[M].杭州:浙江大学出版社,2000.
[41]鲍雨梅,盛颂恩,孙礼弘.往复式压缩机故障诊断专家系统知识库设计[J].机械工程师,2003,2:18-21.
[42]Chen.K,Pettorossi A.,Ras Z.,Skowron A.Attributes and rough properties in information system[J].International Journal of Approximate Reasoning,1998,2(4):365-376.
[43]张文修,吴伟志,梁吉业等.粗糙集理论与方法[M].北京:科学出版社.2001.
[44]苗夺谦.Rough Set理论中连续属性的离散化方法[J].自动化学报,2001,27(3):296-302.
[45]刘明霄.基于粗糙集的属性约简及其应用研究[D](硕士学位论文).石家庄:河北工业大学,2007.
[46]安海忠,郑链,王广祥.粗糙集知识发现的研究现状和展望[J].计算机测量与控制,2003,11(2):81-84.
[47]http://rosetta.lcb.uu.se
[48]程玉胜.Rosetta实验系统在机器学习中的应用[J].安庆师范学院学报,2005,11(2):69-72.
[49]吴福保,李奇,宋文忠.基于粗集理论知识表达系统的一种归纳学习方法[J].控制与决策,1999,14(3):206-211.
[50]孙翎.基于粗糙集的CBR系统案例特征项权值确定[J].计算机工程与应用,2003,30:44-46.
[51]邓聚龙.灰色系统理论[M].武汉:华中理工大学出版社,1998.
[52]刘思峰,郭天榜,党耀国.灰色系统理论及其应用(第二版)[M].北京:科学出版社,1999.
[53]陈文伟.决策支持系统及开发(第二版)[M].北京:清华大学出版社,2000.
[54]任明仑,杨善林,朱卫东.智能决策支持系统:研究现状与挑战[J].系统工程学报,2002, 17(5):430-440.
[55]王兵,王玉莹,莫建军.导弹发射决策专家系统推理机研究与实现[J].系统工程与电子技术,2003,25(10):1242-1245.
[56]苟朝品,林筑英.智能决策支持系统总体设计初探[J].微计算机应用,1992,13(3):26-30.
[57]贾焰,王志英.知识库系统原理与技术[M].长沙:国防科技大学出版社,1993.
[58]孔繁胜.知识库系统原理[M].杭州:浙江大学出版社,2000.
[59]姜开宇,孙传亭,王敏杰等.注射模知识库系统的构建[J].模具工业,2004,3:3-6.
[60]王晓明,侯英玮.基于数据库的专家系统设计[J].计算机工程与应用,2001,20:95-97.
[61]吴陆恒,颜石.矿山选厂设备维修管理专家系统知识库的设计[J].矿山机械,2003,8:53-56.
[62]安利平,张建勇,仝凌云.专家系统中基于粗集的知识获取、更新与推理[J].计算机工程与设计,2004,25(1):42-46.