中文摘要：

针对具有不确定信息的多属性双边匹配决策问题,引入置信规则库推理方法,提出一种双边匹配决策.首先使用置信度评价信息来解决信息的不确定性、不完整性等问题,其次通过置信规则库推理方法将置信度评价信息转换成等级置信度信息,最后利用等级置信度信息建立0-1规划模型并求得最终的匹配方案.提出截断线性区间映射法用以解决当BRB系统的输入值达到阈值后会对输出产生错误的诱导,采用强行截断的方式将该输入值纳为不确定部分,当不足以采用截断方式时,使用区间映射法来减少对结果的不良影响.实例分析表明,使用置信规则库推理方法解决多属性双边匹配决策问题是可行和有效的.

英文摘要：

This thesis presents a tentative study on a new two-sided matching approach,which is proposed to solve the two-sided matching problem with uncertain information and multiple attributes.The multi-attributes matching decision making（MAMDM）problem is one of the most important key points in the two-sided matching study,which has evoked great attention for the scholars in recent years.A belief rule-base inference methodology using the evidence reasoning approach（RIMER）has been introduced in this thesis to solve the problem of MAMDM.At the beginning of this thesis,the authors explain the reason why they choose to use belief degree.The current research on the problem of MAMDM is mainly restricted to the study of a kind of two-sided matching,whose evaluation information is linguistic values or interval values.But there exists a lack of study in belief degree as evaluation value.As belief degree can be used to deal with different kinds of uncertain and incomplete information,using it as evaluation value may trigger a new breakthrough in the study of MAMDM.Through the analysis of simulation experi-ments datas and the application of RIMER,belief degrees evaluation information is converted into different levels of confidence information.Then a 0-1programming model is built by making use of different levels of confidence information to obtain a final matching scheme.It is also pointed out in the thesis that an output error may be caused when BRB（belief rule-base）input is higher than threshold value.To solve this problem,the authors propose that the input value can be incorporated into the uncertainty by the adoption of cutting method.If cutting method is not suitable,linear mapping method can be applied to reduce the influence of the results.The case study analysis shows that it is feasible and effective to adopt the new proposed approach to solve the problem of multiattributes matching decision making.