Group multiple criteria sorting (MCS) has become a trend in dealing with a variety of practical problems. During the process of managing group MCS, it is critical to reduce conflicts among decision-makers (DMs). Given the key role of DMs’ attitudes in affecting consensus level, this study aims to propose a novel consensus-based approach to solve group MCS problems considering DMs’ attitudes with flexible expression linguistic distribution assessments (LDAs) that can capture massive DMs’ qualitative preferences. To achieve this goal, first, a minimum adjustment-based optimization model is built to guide individuals in revising their preferences, and a maximum assignment interval-based optimization model is constructed to derive consistent and possible assignments of each alternative while maintaining the accuracy levels of the original assignments. An attitudinal consensus index is then defined to measure the group consensus level, by which group DMs’ attitudes can be well considered in MCS problems. A sophisticated adaptive feedback adjustment mechanism is also developed and inserted into the consensus model, which provides support for consensus-reaching based on the advantages of both types of adaptive feedback adjustment mechanism strategies. Afterwards, to generate more straightforward and scientific assignment solutions, this study proposes a minimum information loss-based optimization model to identify the final categories of each alternative. Finally, an illustrative example for evaluating livable cities, followed by sensitivity and comparative analyses, is presented to demonstrate the applicability and advantages of the proposed MCS approach.

中文翻译：

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考虑决策者态度的语言分布群体决策下多准则排序的自适应共识模型

群体多标准排序（MCS）已成为处理各种实际问题的趋势。在管理集团MCS的过程中，减少决策者（DM）之间的冲突至关重要。鉴于 DM 的态度在影响共识水平中的关键作用，本研究旨在提出一种新颖的基于共识的方法来解决群体 MCS 问题，考虑到 DM 的态度，并采用灵活的表达语言分布评估（LDA）来捕获大量 DM 的定性偏好。为了实现这一目标，首先，构建基于最小调整的优化模型来指导个体修改其偏好，并构建基于最大分配区间的优化模型以导出每个备选方案的一致且可能的分配，同时保持预测的准确性水平。原来的作业。然后定义态度共识指数来衡量群体共识水平，通过该指数可以在 MCS 问题中很好地考虑群体 DM 的态度。还开发了复杂的自适应反馈调整机制并将其插入到共识模型中，基于两种自适应反馈调整机制策略的优势为达成共识提供了支持。随后，为了生成更直接、更科学的作业解决方案，本研究提出了一种基于最小信息损失的优化模型来确定每个备选方案的最终类别。最后，提出了一个评估宜居城市的说明性示例，并进行了敏感性和比较分析，以证明所提出的 MCS 方法的适用性和优势。