摘要
为了解决车货匹配双方以不确定语言评价表征属性信息的最优匹配问题,提出了一种基于加权不确定语言Bonferroni平均（Weighted uncertain linguistic Bonferroni mean,WULBM）算子和双边匹配理论的车货匹配方法。首先,给出了基于双边匹配理论的车货双边匹配问题的描述;其次,利用WULBM算子集成多属性不确定语言关联信息以综合不确定语言信息;而后构造可能度矩阵,将其转化为满意度矩阵并构建体现主体公平性和满意度尽可能高的匹配优化模型,通过求解模型获得双方满意的匹配方案;最后,以一个车货匹配实例表明本文所提出方法的可行性和有效性。
In order to solve the problem of optimal matching under the uncertain linguistic evaluation information,a method of vehicle and cargomatching theory is proposedbased on the weighted uncertain linguistic Bonferroni mean operator and the two-sided matching. Firstly,the description of thevehicle and cargo matching problem based on the two-sided matching theory is presented. Secondly,WULBM operator is used to integrate multi attribute uncertain linguistic information. Then the probability matrix is constructed and transformed to the satisfaction matrix. The matching optimization model which reflects the best fairness and satisfaction is constructed. Finally,a numerical example of vehicle and cargo matching is given to illustrate the feasibility and effectiveness of the proposed methods.
出处
《系统科学学报》
CSSCI
北大核心
2018年第1期86-91,共6页
Chinese Journal of Systems Science
基金
国家自然科学基金资助(71371156)
关键词
不确定语言
车货匹配
双边匹配
WULBM算子
uncertain linguistic
matching of vehicle and cargoes
two-sided matching
WULBM operators
