越览（88）——精读博士论文的6结论与展望

摘要：This tweet will introduce the conclusions and outlook of the doctoral dissertation "Research on multi-attribute group decision-mak

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“越览（88）——精读博士论文

《基于多粒度犹豫模糊语言信息的

多属性群决策方法研究》的6结论与展望”。

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Today, the editor brings you

"Yue Lan (88)：Multi-attribute large group

decision-making method based on

multi-granular hesitant fuzzy linguistic

information '6 Conclusions and outlook'".

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一、内容摘要（Summary of content）

本期推文将从思维导图、精读内容、知识补充三个方面介绍博士论文《基于多粒度犹豫模糊语言信息的多属性群决策方法研究》的6结论与展望。

This tweet will introduce the conclusions and outlook of the doctoral dissertation "Research on multi-attribute group decision-making methods based on multi-granularity hesitant fuzzy language information" from three aspects: mind map, intensive reading content, and knowledge supplement .

二、思维导图（Mind mapping）

三、精读内容（Intensive reading content）

在结论部分，首先介绍了本文的研究背景，在多属性群决策过程中，由于知识的模糊性、决策环境的不确定性和文化教育背景的差异，专家可能会使用多粒度犹豫模糊语言术语集表达其关于方案的评价信息。然后介绍了本文的研究内容，本文针对已有犹豫模糊语言型多属性群决策问题研究的不足，分别考虑专家使用平衡语言术语集、非平衡语言术语集以及不同类型语言术语集给出多粒度犹豫模糊语言术语集的多属性群决策问题，研究了基于多粒度犹豫模糊语言信息的多属性群决策模型和方法。

In the conclusion, the research background of this paper is first introduced. In the process of multi-attribute group decision-making, due to the fuzziness of knowledge, the uncertainty of the decision-making environment and the differences in cultural and educational background, experts may use multi-granularity hesitant fuzzy language term sets to express their evaluation information about the scheme.

Then the research content of this paper is introduced. In view of the shortcomings of existing research on hesitant fuzzy language-based multi-attribute group decision-making problems, this paper considers the multi-attribute group decision-making problems in which experts use balanced language term sets, unbalanced language term sets and different types of language term sets to give multi-granularity hesitant fuzzy language term sets, and studies the multi-attribute group decision-making model and method based on multi-granularity hesitant fuzzy language information.

其次介绍了本文的研究结论：（1）考虑专家使用平衡语言术语集给出多粒度犹豫模糊语言术语集的多属性群决策问题，提出了群决策共识达成模型和方案排序方法。（2）考虑专家使用非平衡语言术语集给出多粒度犹豫模糊语言术语集的多属性群决策问题，提出了群决策共识达成模型和方案排序方法。（3）针对大群体环境下专家使用不同类型多粒度犹豫模糊语言术语集表达评价信息的多属性决策问题，提出了一种决策方法。

Secondly, the research conclusions of this paper are introduced: (1) Considering the multi-attribute group decision-making problem in which experts use balanced language term sets to give multi-granularity hesitant fuzzy language term sets, a group decision consensus reaching model and a scheme ranking method are proposed. (2) Considering the multi-attribute group decision-making problem in which experts use unbalanced language term sets to give multi-granularity hesitant fuzzy language term sets, a group decision consensus reaching model and a scheme ranking method are proposed. (3) For the multi-attribute decision-making problem in which experts use different types of multi-granularity hesitant fuzzy language term sets to express evaluation information in a large group environment, a decision-making method is proposed.

在创新点部分，作者基于多粒度犹豫模糊语言型多属性群决策模型和方法的研究中总结了以下三个主要创新点：（1）针对多粒度平衡犹豫模糊语言型多属性群决策问题，提出基于最小调整优化的群体共识达成算法和基于指派模型的方案排序方法，弥补了已有研究不能处理多粒度犹豫模糊语言信息、反馈调整建议可解释性差、过度调整初始评价信息和存在信息损失的不足。（2）针对多粒度非平衡犹豫模糊语言型多属性群决策问题，提出了基于识别规则导规则的共识达成算法和基于TODIM的方案排序方法，弥补了已有研究存在反馈调整建议可解释性差和较少考虑专家心理行为的不足，为解决此类群决策问题提供了新的思路和方法。（3）针对基于多粒度犹豫模糊语言信息的多属性大群体决策问题，提出了基于语言分布的专家聚类算法，构建了基于精确度约束的共识达成模型和综合评价值重译模型，弥补了已有大群体决策方法较少考虑语言评价信息的多粒度性、犹豫性和多样性的不足，为专家灵活表达语言评价提供了便利。

In the innovation part, the authors summarized the following three main innovations based on the research on multi-granularity hesitant fuzzy linguistic multi-attribute group decision-making models and methods: (1) For multi-granularity balanced hesitant fuzzy linguistic multi-attribute group decision-making problems, a group consensus-reaching algorithm based on minimum adjustment optimization and a scheme ranking method based on assignment model are proposed, which make up for the shortcomings of existing studies that cannot handle multi-granularity hesitant fuzzy linguistic information, feedback adjustment suggestions are poorly interpretable, initial evaluation information is over-adjusted, and there is information loss. (2) For multi-granularity unbalanced hesitant fuzzy linguistic multi-attribute group decision-making problems, a consensus-reaching algorithm based on identification rule-derived rule and a scheme ranking method based on TODIM are proposed, which make up for the shortcomings of existing studies that feedback adjustment suggestions are poorly interpretable and less considerate of expert psychological behavior, providing new ideas and methods for solving such group decision-making problems. (3) Aiming at the problem of multi-attribute large-group decision-making based on multi-granularity hesitant fuzzy language information, an expert clustering algorithm based on language distribution is proposed, and a consensus reaching model based on accuracy constraints and a comprehensive evaluation value retranslation model are constructed. This makes up for the shortcomings of existing large-group decision-making methods that rarely consider the multi-granularity, hesitancy and diversity of language evaluation information, and provides convenience for experts to flexibly express their language evaluation.

最后，作者基于多粒度犹豫模糊语言术语集的多属性群决策模型与方法进行了展望。

Finally, the authors give an outlook on the multi-attribute group decision-making model and method based on multi-granularity hesitant fuzzy language term sets.

四、知识补充（Knowledge supplement）

指派模型是一种优化模型，用于解决如何将有限资源分配到任务或目标上，以实现整体效益最大化或成本最小化的问题。这类模型属于线性规划的一个特殊类型，常用于解决任务分配、工作安排、资源调度等问题。

The assignment model is an optimization model that is used to solve the problem of how to allocate limited resources to tasks or goals to maximize overall benefits or minimize costs. This type of model is a special type of linear programming and is often used to solve problems such as task allocation, work scheduling, and resource scheduling.

1. 匈牙利算法（Hungarian Algorithm）

一种经典的指派模型求解算法，利用线性规划方法快速找到最优解，尤其适合成本矩阵为方阵的情况。

A classic assignment model solving algorithm that uses linear programming methods to quickly find the optimal solution, especially suitable for the case where the cost matrix is a square matrix.

2. 线性规划方法（Linear programming method）

使用通用的线性规划求解器（如单纯形法、整数规划）来解决较复杂或扩展形式的指派问题。

Use general linear programming solvers (e.g., simplex method, integer programming) to solve more complex or extended forms of assignment problems.

3. 贪心算法（Greedy Algorithm）

在某些简单情形下，可以使用贪心策略快速找到次优解。

In some simple cases, a greedy strategy can be used to quickly find a suboptimal solution.

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