For several classes of decisions, the use of target-based decision analysis (TBDA) is more appropriate than utility analysis. Recent literature on TBDA mainly focuses on different expression forms of targets and performance levels, and on different types of aggregation operators in terms of multi-attribute preference functions. However, different expression forms are usually introduced separately in literature, which cannot fully support the inherent complexity of problems along with different perception and knowledge levels of decision makers during assessing targets and performance levels. Furthermore, although there are attractive features of multilinear target-based preference functions (MTPFs), its applications are seldom considered because of the complexity of identifying their coefficients. In order to solve these two issues, an integrated approach is proposed for multilinear hybrid-information target-based decision analysis, which can deal with diverse forms of targets and performance levels, and identify the coefficients of MTPFs. First, given targets and performance levels for each attribute being expressed in multiple forms simultaneously, a generalized procedure is proposed to transform different forms into probability distributions, and to measure probability of target achievement for each attribute. Second, a novel procedure to identify the coefficients of MTPFs is proposed. This procedure is based on the multilinear model and 2-additive fuzzy measures, which is based on the equivalence between multilinear model based on fuzzy measures and MTPFs. The approach is applied to a case study involving customer competitive evaluation of smart thermometer patches to demonstrate its feasibility and advantages.

对于几类决策，使用基于目标的决策分析 (TBDA) 比效用分析更合适。近期关于 TBDA 的文献主要关注目标的不同表达形式和性能水平，以及多属性偏好函数方面的不同类型的聚合算子。然而，文献中通常分别介绍不同的表达形式，不能充分支持问题的内在复杂性以及决策者在评估目标和绩效水平时的不同感知和知识水平。此外，尽管多线性基于目标的偏好函数（MTPF）具有吸引人的特性，但由于识别其系数的复杂性，很少考虑其应用。为了解决这两个问题，提出了一种基于多线性混合信息目标的决策分析的集成方法，该方法可以处理多种形式的目标和性能水平，并确定MTPF的系数。首先，给定同时以多种形式表达的每个属性的目标和性能水平，提出了一种通用过程，将不同的形式转换为概率分布，并测量每个属性实现目标的概率。其次，提出了一种识别 MTPF 系数的新程序。该程序基于多线性模型和2-加法模糊测度，它基于基于模糊测度的多线性模型与MTPF的等价性。