The analytic hierarchy process (AHP) is an effective method for product selection support because it evaluates alternatives based on the total weight, which is a quantitative value. The total weight is obtained by multiplying the ratio of each evaluation criterion for the target user and the weights of alternatives in each evaluation criterion. The determination of weights in the qualitative evaluation criterion is difficult because of the necessity to compare the questionnaire results of all alternatives. This article proposes Decision Support System for Product Selection based on AHP, using the decision rule of Rough Set for Qualitative Evaluation. The proposed system makes decision rules based on the target user's judgment of “Good” or “Bad” on several samples in the qualitative evaluation criterion. Decision rules have a qualitative and quantitative evaluation value for each attribute, and the system calculates the weight of the AHP through normalization of these evaluation values. The user can know the results easily because the evaluation method of alternative samples in the proposed system is the same as that of correspondence with the shop assistant in an actual store. Based on two experiments, we confirm that the setup of attributes for decision rules of rough sets is one of the most important elements of the proposed system.

中文翻译：

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基于AHP的产品选择决策支持系统的开发，利用粗糙集的决策规则进行定性评估

层次分析法（AHP）是一种有效的产品选择支持方法，因为它根据总重量（是一个定量值）评估替代产品。总权重是通过将目标用户的每个评估标准的比率与每个评估标准中替代项的权重相乘得出的。由于必须比较所有备选方案的问卷调查结果，因此很难确定定性评估标准中的权重。本文提出了一种基于层次分析法的产品选择决策支持系统，利用粗糙集的定性评价规则。所提出的系统基于定性评估标准中目标用户对多个样本的“好”或“差”的判断制定决策规则。决策规则具有每个属性的定性和定量评估值，并且系统通过将这些评估值归一化来计算AHP的权重。由于所提出的系统中的替代样本的评估方法与实际商店中与店员的对应方法相同，因此用户可以轻松知道结果。基于两个实验，我们确认粗糙集决策规则的属性设置是所提出系统的最重要元素之一。由于所提出的系统中的替代样本的评估方法与实际商店中与店员的对应方法相同，因此用户可以轻松知道结果。基于两个实验，我们确认粗糙集决策规则的属性设置是所提出系统的最重要元素之一。由于所提出的系统中的替代样本的评估方法与实际商店中与店员的对应方法相同，因此用户可以轻松知道结果。基于两个实验，我们确认粗糙集决策规则的属性设置是所提出系统的最重要元素之一。