基于前景理论和VIKOR的风险型模糊多准则决策方法

针对准则值均为模糊数的风险型多准则决策问题,提出一种基于前景理论和VIKOR的多准则决策方法。首先,进行区间数、三角模糊数、梯形模糊数、直觉模糊数和语言值的无量纲化处理;然后,基于各个准则各种状态下各个方案的准则值排序,确定中位数参考点以及各个方案在各个准则下的综合前景值;接着,基于前景价值矩阵,给出基于VIKOR的扩展方法;最后,通过具体实例验证了所提出方法的有效性和可行性。     

考虑准则依赖的多准则变权决策新方法

为克服Choquet积分、网络分析法以及决策试行与评价实验室方法在处理具有准则依赖特征的多准则决策问题时存在的指数灾难、难以进行有效判断以及忽视因素自我影响强度等内在缺陷,吸纳网络分析法、数据包络分析以及非线性加权影响测度体系的核心思想,提出一种全新的考虑准则依赖的多准则变权决策方法。该方法不仅从超矩阵构造机理上实现了对系统方案的变权评价,而且更易于反映复杂决策问题的非线性、涌现性、复杂性等本质特征以及决策者的偏好判断信息。案例对比验证结果表明,所提方法是科学可行的,对于解决复杂系统多准则决策问题有着较强的实践应用可操作性。     

基于后悔理论的灰色随机多准则决策方法

针对准则值和状态概率均为区间灰数的灰色随机多准则决策问题,在考虑决策者的风险态度及心理行为的情境下,给出一种基于后悔理论的决策方法.首先将原始准则值转化为标准灰数, 同时进行规范化处理, 得到各状态下的标准灰色风险决策矩阵;然后依据后悔理论,定义灰色感知效用函数,构造决策者对方案集的灰色综合感知效用最大化的多目标优化模型,并解出最优权重向量,得到各方案的最优灰色感知效用值且排序;最后,通过实例验证所提出方法的可行性和有效性.     

基于改进前景理论的直觉模糊随机多准则决策方法

针对方案准则值为直觉模糊数、准则权重信息部分已知的随机多准则决策问题,提出一种基于改进前景理论的决策分析方法.首先,定义一个新的记分函数,据此可将直觉模糊数转化为实数.其次,考虑到决策者并非完全理性及决策者风险态度的差异性,将决策者分为保守型、 中间型及冒险型,引入改进前景理论,根据不同决策者类型调整参数,构建改进前景决策矩阵.再次,建立以准则值总差异最大化且准则权重差异最小化为目标的非线性二次偏差优化定权模型,计算准则权重.进而,结合改进前景决策矩阵及准则权重计算各方案的综合效用值,并以此确定方案的顺序排列.最后,通过算例验证所提出直觉模糊随机多准则决策方法的有效性和合理性.     

基于粗糙熵权的模糊多准则决策方法及应用

提出度量粗糙集不确定性的粗糙熵概念,并基于粗糙熵对属性的重要度给出了定义,从而提供一种求解模糊多准则决策模型中准则权重的方法．根据备选方案的模糊评价系统,给出相关的知识表达系统,并建立删除冗余属性后的备选方案综合评价优选矩阵,通过比较各备选方案与理想方案的近似度求出最优方案．最后,通过实例计算证明了上述模型及方法的有效性．     

基于粗糙数的BWM-TOPSIS 多准则群决策方法

多准则群决策是决策领域的研究热点, 如何在信息不确定性和评价主观性背景下选择合适的决策方法则是研究的难点. 为了解决这一问题, 提出一个新的基于粗糙数的多准则群决策方法. 首先, 提出基于粗糙数的最优最劣方法(RBWM) 确定评价准则的权重; 然后, 利用粗糙数改进的逼近理想解排序法(RTOPSIS) 评价备选方案并做出最优决策; 最后, 利用一个实例对所提出的群决策方法进行应用和灵敏度分析, 并与其他决策方法进行对比分析, 验证了所提出方法的有效性和准确性.

     

基于前景理论的信息不完全的模糊多准则决策方法

针对准则权重不完全确定且方案的准则值为梯形模糊数的多准则决策问题,提出一种基于前景理论的模糊多准则决策方法.该方法将决策者的风险心理因素引入多准则决策,根据前景理论及模糊数距离公式,定义梯形模糊数的前景价值函数,并以此构建方案综合前景值最大化的非线性规划模型,求解模型得出最优权向量,最终确定出方案的排序.最后通过实例说明了该方法的有效性和可行性.     

基于期望值的灰色随机多准则决策方法

定义了离散型灰色随机变量及其期望值和标准差.针对准则权重已知而方案的准则值为灰色随机变量的多准则问题,提出一种灰色随机多准则决策方法.该方法通过求得各方案在各准则下评价值的期望值和标准差,得到标准期望值决策矩阵;利用各准则权重和规范化矩阵计算出各方案的综合评价区间,采用区间灰数可能度的方法构建方案综合评价区间的评判矩阵,进而得到各方案的排序.最后通过算例表明了该方法的可行性和有效性.     

考虑决策者心理行为的灰色多属性群体决策方法

针对决策者在实际决策过程中表现出参照依赖和损失规避的现象,提出一种考虑决策者心理行为的灰色多属性群体决策方法.首先,利用“奖优罚劣”变换算子对原始决策信息进行规范化处理,以充分反映“奖优罚劣”思想;其次,依据决策群体信息,计算两两方案关于属性相比较时收益和损失的优势度,整合得到备选方案的全局优势度;再次,根据极大熵准则和群体意见一致性原则构建决策者权重优化模型,求出备选方案的综合价值并对其排序择优;最后,通过案例分析验证所提方法的可行性、合理性和实用性.     

基于语言评价和前景理论的多准则决策方法

针对风险决策问题,提出一种基于语言评价和前景理论的多准则决策方法．该方法首先将基于语言信息的决策矩阵转化为基于区间数的决策矩阵,并定义一个区间数之间的差函数,给出各准则的参考点,得到前景结果矩阵;然后,通过价值函数和决策权重函数计算方案在各准则下的前景值,并通过加权平均获得方案的前景值;最后,根据前景值的大小给每个方案排序并得到最优方案．实例分析结果表明了该方法的可行性．     

An intuitionistic fuzzy group decision making method using entropy and association coefficient

Group decision making is a process in which experts rank and choose the most desirable alternatives based on some accepted criteria. The aim of this paper was to introduce a method to solve group decision making problems with Atanassov’s intuitionistic fuzzy sets. First, the weight of each criterion is calculated using intuitionistic fuzzy entropy. Then, the total criteria weight vector is calculated by aggregating the calculated weights. Using the obtained weight vector, the alternatives are ranked based on the association coefficient of the performance of alternatives related to each criterion and the positive ideal intuitionistic fuzzy set value and the negative ideal intuitionistic fuzzy set value. Finally, to show the application of the proposed method, it is implemented in software vendor selection.     

Multi-class classification using hybrid soft decision model for agriculture crop selection

A hybrid soft decision model has been developed in this paper to take decision on agriculture crop that can be cultivated in a given experimental land by integrating few soft computing techniques. The proposed model comprises of three parts, namely weight calculation, classification and prediction. Twenty-seven input criteria were categorized into seven broad criteria, namely soil (11 sub-criteria), water (2 sub-criteria), season (no sub-criterion), input (6 sub-criteria), support (2 sub-criteria), facilities (3 sub-criteria) and risk (2 sub-criteria). In the proposed model, relative weights of main criteria were calculated using Shannon’s Entropy method and relative weights of sub-criteria in each main criterion were calculated using rough set approach. As VIKOR method is effective in sorting the alternatives, it is used to determine the ranking index of main criteria in this study. A soft decision system was constructed from the results of rough set method, VIKOR method and Shannon’s Entropy method. Classification rules were generated for five agriculture crops, namely paddy, groundnut, sugarcane, cumbu and ragi based on the soft decision system using bijective soft set approach. The developed model predicts each site in the validation dataset into one of the five crops. The performance of the proposed model has been sanity checked by agriculture experts.

     

A new soft computing model based on linear assignment and linear programming technique for multidimensional analysis of preference with interval type-2 fuzzy sets

Multiple criteria decision making (MCDM) approach plays an important role in life, since it is always necessary to make decisions through various alternatives based on specific criteria. In this paper, interval type-2 fuzzy sets (IT2FSs) are used because in most cases in the real-world the information is incomplete and ambiguous. A new group decision approach with linear assignment method (LAM) is proposed. In addition, weight of each evaluation factor according to subjective and objective data is constructed based on a new developed version of linear programming technique for multidimensional analysis of preference (LINMAP) method. In the proposed method, weights of decision makers (DMs) are computed based on a novel approach that applies a new modified method based on the concept of ideal solutions. Furthermore, a new IT2F-ranking method is introduced. To display the applicability of the presented soft computing method, firstly, a real case study of green supplier selection problem is adopted from the literature and solved. Moreover, the method is applied in a second case study of project evaluation and selection problem. Two applications show that the introduced method presents a proper soft computing framework that can handle real-world uncertain environments. Moreover, the method can consider importance of the DMs and evaluation criteria.     

An integrated intuitionistic fuzzy AHP and SWOT method for outsourcing reverse logistics

We consider the problem faced by a company that must outsource reverse logistics (RL) activities to third-party providers. Addressing RL outsourcing problems has become increasingly relevant issue in the management science and decision making literatures. The correct evaluation and ranking of the decision criteria/priorities determining the selection of the best third-party RL providers (3PRLPs) is essential for the competitive performance of the outsourcing company. The method proposed in this study allows to identify and classify these decision criteria. First, the relevant criteria and sub-criteria are identified using a SWOT analysis. Then, Intuitionistic Fuzzy AHP is used to evaluate the relative importance weights among the criteria and the corresponding sub-criteria. These relative weights are implemented in a novel extension of Mikhailov's fuzzy preference programming method to produce local weights for all criteria and sub-criteria. Finally, these local weights are used to assign a global weight to each sub-criterion and create a ranking. We discuss the results obtained by applying the proposed model to a case study of a real company. In particular, these results show that the most important priority for the company when delegating RL activities to 3PRLPs is to focus on the core business, while reducing costs constitutes one of its least important priorities.     

A novel hybrid method for selecting soccer players during the transfer season

The quality of its players is one of the most significant features determining the failure or success of a sports team. The wide array of factors contributing to the performance of the players together with the inherent financial limitations of the clubs have transformed the selection of players into a complex problem. The current paper presents an integrated approach that combines multiple‐criteria decision‐making analysis and mathematical programming to support the decision maker through the building process of a soccer team. First, the fuzzy analytic network process is applied to evaluate the significance of the different performance criteria for each position in the field. The score attained by the different players in each potential position is computed using PROMETHEE II. A biobjective integer programming model has been designed to evaluate the transfer status of the players. Finally, data envelopment analysis is used to identify the most efficient Pareto solution determining the status of each player. In order to demonstrate the applicability of the proposed approach, the position in the field and transfer status of 60 players being considered by a real soccer team have been determined.     

基于冲突主体不确定证据融合的灰靶决策方法

研究基于冲突利益主体不完全确定权重信息情景下的群决策方法.把不完全确定的权重信息作为证据,建立了所有证据之间的距离最小测算模型,提出了不完全确定权重数值上、下限范围估计模型;为解决冲突证据的融合问题,提出了基于证据相似性的证据协调加权因子;建立了基于灰靶决策的靶心距分布范围确定模型,以排定方案优劣顺序.最后基于该方法探讨了某棕地开发方案的评价应用.     

On prioritized weighted aggregation in multi-criteria decision making

This paper deals with multi-criteria decision making (MCDM) problems with multiple priorities, in which priority weights associated with the lower priority criteria are related to the satisfactions of the higher priority criteria. Firstly, we propose a prioritized weighted aggregation operator based on ordered weighted averaging (OWA) operator and triangular norms (t-norms). To preserve the tradeoffs among the criteria in the same priority level, we suggest that the degree of satisfaction regarding each priority level is viewed as a pseudo criterion. On the other hand, t-norms are used to model the priority relationships between the criteria in different priority levels. In particular, we show that strict Archimedean t-norms perform better in inducing priority weights. As Hamacher family of t-norms provide a wide class of strict Archimedean t-norms ranging from the product to weakest t-norm, Hamacher parameterized t-norms are used to induce the priority weight for each priority level. Secondly, considering decision maker (DM)’s requirement toward higher priority levels, a benchmark based approach is proposed to induce priority weight for each priority level. In particular, ?ukasiewicz implication is used to compute benchmark achievement for crisp requirements; target-oriented decision analysis is utilized to obtain the benchmark achievement for fuzzy requirements. Finally, some numerical examples are used to illustrate the proposed prioritized aggregation technique as well as to compare with previous research.     

群决策中随机信息的贝叶斯集结与统计模拟方法

针对方案属性值和权重都为随机变量的群决策问题,结合贝叶斯理论和随机模拟,提出一种集成专家主观概率分布集结和随机多属性决策方案选优的方法.该方法首先构建一个多元正态集结模型,将多个专家估计的属性值分布集结成单一分布,然后用随机模拟算法,生成不完全权重信息,并通过计算各方案获得特定排名的可信度因子,以及反映决策者风险偏好的整体排名可信度因子,得到各方案排序.实例分析验证了方法的有效性.     

Pythagorean fuzzy TOPSIS for multicriteria group decision-making with unknown weight information through entropy measure

In this study, a new technique for order preference by similarity to ideal solution (TOPSIS)-based methodology is proposed to solve multicriteria group decision-making problems within Pythagorean fuzzy environment, where the information about weights of both the decision makers (DMs) and criteria are completely unknown. Initially, generalized distance measure for Pythagorean fuzzy sets (PFSs) is defined and used to initiate a new Pythagorean fuzzy entropy measure for computing weights of the criteria. In the decision-making process, at first, weights of DMs are computed using TOPSIS through the geometric distance model. Then, weights of the criteria are determined using the entropy weight model through the newly defined entropy measure for PFSs. Based on the evaluated criteria weights, TOPSIS is further applied to obtain the score value of alternatives corresponding to each decision matrix. Finally, the score values of the alternatives are aggregated with the calculated DMs’ weights to obtain the final ranking of the alternatives to avoid the loss of information, unlike other existing methods. Several numerical examples are considered, solved, and compared with the existing methods.     

A new multiple criteria decision making method based on intuitionistic fuzzy information

The risk attitude of a decision maker is considered in the decision process. Inspired by mean-variance type utility functions in the financial risk management, a new class of decision functions are defined based on the weighted score function and the weighted accuracy function in the intuitionistic fuzzy setting. By choosing a suitable parameter value, the decision maker’s risk attitude can be flexibly reflected by our decision function. The new method can be applied for both the exactly known and partly known criteria weight situations. For the latter case, it is only necessary to solve one linear programming problem. The developed models and algorithms are then extended to multiple criteria decision making problems with the interval-valued intuitionistic fuzzy information. Numerical examples are provided to illustrate the practicality, flexibility and efficiency of our new algorithms.