基于区间数贴近度的不确定多属性决策模型

A Model Based on Similarity Degree of Interval Number for Uncertain Multi-Attribute Decision-Making

针对只有部分权重信息且属性值以区间数形式给出的多属性决策问题,提出了一种基于区间数贴近度的决策方法.首先讨论了区间数贴近度的定义和性质;然后给出了解决不确定多属性决策问题的一般步骤.并依据传统的逼近理想解的基本思路,以实际评价值与理想解之间的贴近度最大化为目标建立优化模型,从而得到指标权重.进而计算出每个方案与正理想解的相对贴近度,即可得到所有方案的排序结果.方法能充分利用规范化评价的先验信息,评价结果客观可靠,不具有主观随意性.最后通过实例分析验证了该方法的有效性和实用性.

For the problem of multiple attribute decision-making with incomplete information on attribute weights to which the attribute values are given in terms of interval numbers,a decision method based on similarity degree of interval number is proposed.Firstly,the interval number similarity degree is defined and its quality is given.Then,the calculation steps to solve uncertain multiple attribute decision making are given.A mathematical programming model is established according as the traditional TOPSIS method.The objective is to maximize the similarity degree of real evaluating value and ideal solution.The optimal solution is the best index weight coefficients.According to the similarity degree,the relative similarity degree to the positive point is calculated to rank all alternatives.The proposed method can sufficiently utilize the normalized evaluation information and derive weights of attributes objectively. Finally,an illustrative example is given to demonstrate the feasibility and practicability of the method.