Multiple attribute decision-making methods for the dynamic operator allocation problem

This study explores two multiple attribute decision-making (MADM) methods to solve a dynamic operator allocation problem. Both methods use an analytic hierarchy process (AHP) to determine attribute weights a priori. The first method uses a technique for order preference by similarity to ideal solution (TOPSIS). The second method incorporates a fuzzy-based logic that uses linguistic variable representation, fuzzy operation, and fuzzy defuzzification. The TOPSIS uses deterministic performance ratings and attribute weights, whilst the fuzzy-based is a linguistic method. An applied case study drawn from existing literature is used to demonstrate and test findings. The proposed methods systematically evaluate alternative scenarios, with the result indicating promise for solving an operator allocation decision problem.     

An alternative multiple attribute decision making methodology for solving optimal facility layout design selection problems

In the present work, a systematic and an alternative multiple attribute decision making methodology is presented for selection of facility layout design selection problems. The proposed methodology is based on Preference selection index (PSI) method. In the proposed methodology appropriate facility layout design is selected for a given application without considering relative importance between facility layout design selection attributes. Two different types of facility layout design selection problems are examined to demonstrate, validate, and to check the reliability of proposed methodology. In addition, subjective cost benefit analysis is performed to study the benefits to cost to the company. Finally, the study has concluded that the facility layout design selection methodology based on PSI method is simple, logical, and more appropriate for solving the facility layout design selection problems.     

Pythagorean模糊环境下基于交叉熵和TOPSIS的多准则决策方法

考虑到Pythagorean模糊集（Pythagorean Fuzzy Set,PFS）具有的优势,提出了一个Pythagorean模糊环境下解决多准则决策（Multicriteria Decision Making,MCDM）问题的新方法。根据TOPSIS理论计算Pythagorean模糊环境下的正、负理想解,同时提出两个Pythagorean模糊集之间的交叉熵定义,并对其性质给予证明。计算每个方案各自和正、负理想解之间的交叉熵,再根据相对贴近度对所有方案进行排序。通过一个在绿色环境下的供应商选择的算例验证了有效性和实用性。     

Fuzzy heterogeneous multiattribute decision making method for outsourcing provider selection

One of the critical activities for outsourcing success is outsourcing provider selection, which may be regarded as a type of fuzzy heterogeneous multiattribute decision making (MADM) problems with fuzzy truth degrees and incomplete weight information. The aim of this paper is to develop a new fuzzy linear programming method for solving such MADM problems. In this method, the decision maker’s preferences are given through pair-wise alternatives’ comparisons with fuzzy truth degrees, which are expressed with trapezoidal fuzzy numbers (TrFNs). Real numbers, intervals, and TrFNs are used to express heterogeneous decision information. Giving the fuzzy positive and negative ideal solutions, we define TrFN-type fuzzy consistency and inconsistency indices based on the concept of the relative closeness degrees. The attribute weights are estimated through constructing a new fuzzy linear programming model, which is solved by using the developed fuzzy linear programming method with TrFNs. The relative closeness degrees of alternatives can be calculated to generate their ranking order. An example of the IT outsourcing provider selection problem is analyzed to demonstrate the implementation process and applicability of the method proposed in this paper.     

Linear programming method for multiattribute group decision making using IF sets

The purpose of this paper is to develop a linear programming methodology for solving multiattribute group decision making problems using intuitionistic fuzzy (IF) sets. In this methodology, IF sets are constructed to capture fuzziness in decision information and decision making process. The group consistency and inconsistency indices are defined on the basis of pairwise comparison preference relations on alternatives given by the decision makers. An IF positive ideal solution (IFPIS) and weights which are unknown a priori are estimated using a new auxiliary linear programming model, which minimizes the group inconsistency index under some constraints. The distances of alternatives from the IFPIS are calculated to determine their ranking order. Moreover, some properties of the auxiliary linear programming model and other generalizations or specializations are discussed in detail. Validity and applicability of the proposed methodology are illustrated with the extended air-fighter selection problem and the doctoral student selection problem.     

一种应用Rough集的多属性决策方法

将Rough集应用于多属性决策问题,通过引入属性的综合客观权重给出一种依据已有决策案例进行决策的新算法。其基本思想是： 首先根据已有决策案例建立决策表;然后应用条件属性关于决策属性的重要度、决策案例的局部权重和全局权重等概念确定各属性的综合客观权重; 再通过经验因子来调整综合客观权重和主观权重的比例,得到各属性的最终权重; 最后利用所得权重计算待决策对象的决策属性值,以此进行决策。文中通过对实例的分析,说明了该算法的实用性。     

On a novel uncertain soft set model: Z-soft fuzzy rough set model and corresponding decision making methods

In this paper, a kind of novel soft set model called a Z-soft fuzzy rough set is presented by means of three uncertain models: soft sets, rough sets and fuzzy sets, which is an important generalization of Z-soft rough fuzzy sets. As a novel Z-soft fuzzy rough set, its applications in the corresponding decision making problems are established. It is noteworthy that the underlying concepts keep the features of classical Pawlak rough sets. Moreover, this novel approach will involve fewer calculations when one applies this theory to algebraic structures. In particular, an approach for the method of decision making problem with respect to Z-soft fuzzy rough sets is proposed and the validity of the decision making methods is testified by a given example. At the same time, an overview of techniques based on some types of soft set models is investigated. Finally, the numerical experimentation algorithm is developed, in which the comparisons among three types of hybrid soft set models are analyzed.     

Solving a sealed-bid reverse auction problem by multiple-criterion decision-making methods

This study presents a model for solving the sealed-bid, multiple-issue reverse auction problem, using multiple-criterion decision-making approaches, such that the interests of both the buyer and the supplier are satisfied. On the supplier side, the bid construction process is formulated as a fuzzy multiple-objective programming problem, and is solved using an exhausted enumeration algorithm which adjusts the production plan in accordance with the buyer’s demand, based on the current master production schedule (MPS) and the available-to-promise (ATP) inventory. The use of the information of MPS and ATP enables the supplier to make accurate estimates of the production costs associated with specific delivery dates, and thus facilitates the construction of a bid which is both profitable and likely to secure the contract. On the buyer side, the winner determination process is treated as a multiple-attribute decision-making problem, and is solved using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The validity of the proposed approach is demonstrated via an illustrative example.     

Pythagorean fuzzy TODIM approach to multi-criteria decision making

Recently, the TODIM (an acronym in Portuguese for Interactive Multi-criteria Decision Making) approach, which can characterize the decision makers’ psychological behaviours under risk, has been introduced to handle multi-criteria decision making (MCDM) problems. Moreover, Pythagorean fuzzy set is an effective tool for depicting uncertainty of the MCDM problems. In this paper, based on the prospect theory, we first extend the TODIM approach to solve the MCDM problems with Pythagorean fuzzy information. Then, we conduct simulation tests to analyze how the risk attitudes of the decision makers exert the influence on the results of MCDM under uncertainty. Finally, a case study on selecting the governor of Asian Infrastructure Investment Bank is made to show the applicability of the proposed approach.     

Linguistic Z-number fuzzy soft sets and its application on multiple attribute group decision making problems

In this study, the concept of linguistic Z-number fuzzy soft set ($\text{◂⋅▸}LZnFSS$) is proposed to describe multiple uncertainties in practical decision making problems. $\text{◂⋅▸}LZnFSS$ combines the concepts of fuzzy soft set, linguistic Z-number, and soft set, which could reflect both of the uncertainty in structure and the uncertainty in detailed evaluations. As an initial idea, the set operations on $\text{◂⋅▸}LZnFSSs$ are put forward, the properties of such operations are also discussed. With traditional soft set based decision procedure and fuzzy soft set based decision procedure, a novel linguistic Z-number fuzzy soft set based group decision procedure is developed to solve multiattribute group decision making with linguistic Z-numbers. Wherein an extended technique for order preference by similarity to ideal solution is also developed. Finally, a numerical example is shown to illustrate the practicality and effectiveness of the given method.     

基于Vague积分的多准则决策方法

现有决策方法忽略了各准则之间的影响,使得决策结果不一定符合实际。为了克服现有决策方法的不足和缺陷,提出一种基于Vague积分的新决策方法。该方法用Vague测度对各准则的重要性加以衡量,再利用Vague积分作为聚合算子得出各方案的整体评价,然后根据评价得出最优方案。最后,通过实例阐明新决策方法的有效性和正确性。     

Extension of TOPSIS to determine weight of decision maker for group decision making problems with uncertain information

In traditional TOPSIS method, the ideal solutions for alternatives are expressed in vectors. An important step in the process of group decision making is to determine the relative importance of each decision maker. In this paper, the weights of decision makers derived from individual decision are determined by using an extended TOPSIS method with interval numbers. The ideal decisions for all individual decisions are expressed in matrices. The positive ideal decision is the intersection of all individual decisions; the negative ideal decision is the union of all individual decisions. Comparisons with other methods are also made. A numerical example is examined to show the potential applications of the proposed method.     

A robust hybrid multi-criteria decision making methodology for contractor evaluation and selection in third-party reverse logistics

Due to green legislations, industries track the used products through reverse logistics contractors. A reverse logistics programme offers significant cost savings in procurement, transportation, disposal and inventory carrying. Since reverse logistics operations and the supply chains they support are considerably more complex than traditional manufacturing supply chains, it can be offered to third party contractors. But availability of more number of contractors make evaluating and selecting the most efficient Reverse Logistics Contractor (RLC) a challenging task and treated as a multi-criteria decision making problem. In this paper, a hybrid method using Analytical Hierarchy Process (AHP) and the Fuzzy Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) is proposed. AHP is used to obtain the initial weights and Fuzzy TOPSIS is used to get the final ranking. A case study demonstrates the application of the proposed method. Finally sensitivity analysis is carried out to confirm the robustness.     

A framework for dynamic multiple-criteria decision making

The classic multiple-criteria decision making (MCDM) model assumes that, when taking a decision, the decision maker has defined a fixed set of criteria and is presented with a clear picture of all available alternatives. The task then reduces to computing the score of each alternative, thus producing a ranking, and choosing the one that maximizes this value.However, most real-world decisions take place in a dynamic environment, where the final decision is only taken at the end of some exploratory process. Exploration of the problem is often beneficial, in that it may unveil previously unconsidered alternatives or criteria, as well as render some of them unnecessary.In this paper we introduce a flexible framework for dynamic MCDM, based on the classic model, that can be applied to any dynamic decision process and which is illustrated by means of a small helicopter landing example. In addition, we outline a number of possible applications in very diverse fields, to highlight its versatility.     

基于前景理论的交互式群组风险决策方法

为了克服现有方法因立论假设过于理想而存在决策结果的科学性和满意性较差以及决策过程效率低下等缺陷,基于前景理论构建了能够吸纳多种模糊信息且能从中识别有效前景的优选模型及判别定理,并针对结果价值的交互学习提出了具有收敛性的交互式决策方法。数值模拟分析表明,所提出的方法既能反映群组成员风险偏好,保证决策结果具有科学性、满意性,又能平衡信息完善程度与其获取成本之间的矛盾关系,保证决策过程具有有效性和可行性。     

基于决策分类的决策表分解方法研究*

针对当前基于属性重要性的决策表属性集分解方法存在的不足,提出了一种新型的基于决策分类的决策表属性集分解方法。分析了近似分类质量和属性重要性与决策分类之间的关系,利用粗糙集理论,从提高子决策表中决策分类正确性的角度出发考虑条件属性与决策属性之间的关系,提出了决策表分解的条件属性选择量度并对决策表实施属性集分解。     

基于熵权Vague集的多目标决策方法

针对目前基于Vague集的多目标决策中目标权重的主观任意性以及评价函数的单一性问题,提出一种基于熵权Vague集的多目标决策方法。首先,将决策矩阵转化为目标优属度矩阵,再利用熵权系数法计算各个目标的客观权重,综合考虑客观权重和主观权重得到各个目标的权重向量区间;然后,通过计算方案的支持目标集、反对目标集和中立目标集得出方案的Vague估计值;最后,定义新的评价函数对方案进行排序并选出最优方案。通过算例验证了所提方法的合理性和有效性。     

基于优势关系的多属性决策问题研究

针对属性权重未知的多属性决策问题,指出决策对象与理想对象之间的优势度与其属性值的概率测度及属性值大小之间有等价关系;借鉴博弈决策中的极大极小算法,提出用属性值的优势关系确定属性权重,并利用优势关系对决策对象进行排序并择优:最后,对属性值为区间数的多属性决策实例进行了分析,同时将该算法与离差最大化算法的异同点进行了对比,表明了算法的有效性.     

The inclusion-based TOPSIS method with interval-valued intuitionistic fuzzy sets for multiple criteria group decision making

The technique for order preference by similarity to ideal solution (TOPSIS) method is a well-known compromising method for multiple criteria decision analysis. This paper develops an extended TOPSIS method with an inclusion comparison approach for addressing multiple criteria group decision-making problems in the framework of interval-valued intuitionistic fuzzy sets. Considering the relative agreement degrees and the importance weights of multiple decision makers, this paper presents a modified hybrid averaging method with an inclusion-based ordered weighted averaging operation for forming a collective decision environment. Based on the main structure of the TOPSIS method, this paper utilizes the concept of inclusion comparison possibilities to propose a new index for an inclusion-based closeness coefficient for ranking the alternatives. Additionally, two optimization models are established to determine the criterion weights for addressing situations in which the preference information is completely unknown or incompletely known. Finally, the feasibility and effectiveness of the proposed methods are illustrated by a medical group decision-making problem.     

模糊数直觉模糊几何集成算子及其在决策中的应用

模糊数直觉模糊集是直觉模糊集的拓展.针对模糊数直觉模糊信息的集成问题,定义了模糊数直觉模糊数的一些运算法则,基于这些法则给出了一些新的几何集成算子,即模糊数直觉模糊加权几何(FIFWG)算子、模糊数直觉模糊有序加权几何(FIFOWG)算子和模糊数直觉模糊混合几何(FIFHG)算子.在此基础上,提出一种属性权重确知且属性值以模糊数直觉模糊数形式给出的多属性群决策方法.最后通过实例分析结果证明了该方法的有效性.