长沙理工大学学报(自然科学版)
基于证据推理的政府工程招标人与投标人合谋倾向强度测算方法
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陈赟(1963-),男,湖南邵阳人,长沙理工大学教授,博士生导师,主要从事工程项目管理和知识管理等方面的研究。E-mail地址:cheny@csust.edu.cn

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TU723.2;F284

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国家自然科学基金资助项目(71771031)


The method of calculating the collusion intensity of governmentproject tenderers and bidders based on evidence reasoning
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    摘要:

    为了测算出政府工程招标人与投标人的合谋倾向强度,首先在分析招投标全过程的主要事项与关键节点的基础上,凝练出招标人与投标人的合谋行为类型,筛选出招标人与投标人合谋倾向强度的测算因素,包括审查条款的偏向性、审查标准的差异性、招标文件的指向性、招标人干预评标的暗示性和专家评标的倾向性。然后构建了证据推理模型,通过建立识别框架与测算因素层次结构,运用AHP-熵权法求出测算因素的综合权重,构建判断矩阵,并利用合成公式测算出招标人与各投标人的合谋倾向强度,根据评判准则识别出与招标人合谋倾向最强的投标人。最后通过一个工程实例验证了证据推理的测算方法,结果表明该方法是可行且合理的。

    Abstract:

    In order to calculate the collusion intensity of government project tenderee and the bidder, based on the analysis of main items and key nodes in the overall bidding process, the type of collusion between the tenderee and the bidder is selected, and the calculation factors of the tenderee and the bidder's collusion intensity are screened out, including the bias of the review clause, the difference of the examination standard, the directivity of the bidding documents, the suggestion of the bid inviter's intervention, and the tendentiousness of the expert evaluation. The evidence reasoning model is constructed. By establishing the identification framework and the hierarchy of calculation factors, the comprehensive weight of calculation factors was calculated by AHP-entropy weight method; constructing the judgment matrix and using the synthesis formula to calculate the collusion intensity of the tenderee and the bidder; according to the judging standard, the bidder who have the strongest collusion with the tenderee are identified. An engineering example is used to verify the method of estimating evidence reasoning. The results show that this method is feasible and reasonable.

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陈赟,吴灿灿,朱文喜.基于证据推理的政府工程招标人与投标人合谋倾向强度测算方法[J].长沙理工大学学报(自然科学版),2018,(1):57-62.
CHEN Yun, WU Can-can, ZHU Wen-xi. The method of calculating the collusion intensity of governmentproject tenderers and bidders based on evidence reasoning[J]. Journal of Changsha University of Science & Technology (Natural Science),2018,(1):57-62.

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  • 在线发布日期: 2022-04-28
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