一种基于TOPSIS法的单值中智多属性决策新方法

单值中智集(SVNS)是中智集(NS)的一种特殊情况,它可以描述现实世界中大量存在的不精确、不确定和不一致信息.由于语言评价的模糊性,传统的模糊评价方法在解决多属性决策(MADM)问题上效果不佳.针对这种情况,提出了一种基于TOPSIS法的单值中智多属性决策新方法.首先介绍了中智集的一些基本概念和运算规则,给出了两个单...     

Selecting green suppliers based on GSCM practices: Using fuzzy TOPSIS applied to a Brazilian electronics company

Due to an increased awareness and significant environmental pressures from various stakeholders, companies have begun to realize the significance of incorporating green practices into their daily activities. This paper proposes a framework using Fuzzy TOPSIS to select green suppliers for a Brazilian electronics company; our framework is built on the criteria of green supply chain management (GSCM) practices. An empirical analysis is made, and the data are collected from a set of 12 available suppliers. We use a fuzzy TOPSIS approach to rank the suppliers, and the results of the proposed framework are compared with the ranks obtained by both the geometric mean and the graded mean methods of fuzzy TOPSIS methodology. Then a Spearman rank correlation coefficient is used to find the statistical difference between the ranks obtained by the three methods. Finally, a sensitivity analysis has been performed to examine the influence of the preferences given by the decision makers for the chosen GSCM practices on the selection of green suppliers. Results indicate that the four dominant criteria are Commitment of senior management to GSCM; Product designs that reduce, reuse, recycle, or reclaim materials, components, or energy; Compliance with legal environmental requirements and auditing programs; and Product designs that avoid or reduce toxic or hazardous material use.     

旅游路线规划问题

对旅游爱好者游遍201个5A级景点的旅行方案进行研究.首先,建立以总的旅游时间达到最小的0-1数学规划模型,利用枚举法将景点划分成若干类,并将其视为TSP问题,用蚁群算法得出常住地为西安的旅游者游遍所有景点至少需要的时间和最优路线;其次,考虑了费用最小,旅游体验最好的因素,建立多目标规划模型,通过模糊隶属度函数的构造,将多目标规划模型进行线性加权组合转化为单目标规划问题进行求解.还引入TOPSIS模型,对常住地在北京的自驾游爱好者提供了一个简要版旅游计划.最后,用因子分析和灰关联分析相结合的方法遴选了相应5A级和4A级旅游景区,并给出该旅游爱好者合理的十年旅游规划.     

改进的TOPSIS决策方法在供应商选择中的应用

运用马氏距离替代欧式距离改进传统的TOPSIS方法,解决当属性间存在线性相关时欧式距离失效的缺陷;充分考虑对立集合并引入联系向量距离,解决可能存在的方案距离正理想解和负理想解距离都近的缺陷.然后通过决策者偏好系数将马氏距离和联系向量距离所得结果合成新的相对贴近度,从而同时克服传统TOPSIS方法的以上两个缺陷.最后通过供应商选择的实例来验证方法的有效性.     

基于模糊TOPSIS法的职业选择定量分析

由于职业选择受多种因素的影响,且许多因素又具有模糊不确定性,单纯地从定性的角度无法准确清晰地进行选择判断.为解决这一问题,本文运用模糊TOPSIS法对职业选择问题进行定量分析.首先,从个人兴趣爱好、单位的工资收入水平、地理位置、单位性质、发展前景、继续深造的条件和机会、发展机会等,建立职业选择的评价指标体系;然后,采用模糊集理论与专家综合评判相结合的方法求出模糊评价指标的概率并给出所有评价指标的权数;接着,以模糊数学和传统TOPSIS(逼近理想解的排序法)法为基础,建立职业选择评价方法;最后,以某大学生职业选择为例,验证该评价方法的有效性.结果表明,运用模糊TOPSIS法对职业选择定量分析是可行的.     

线路方案比选多目标决策分析

本文通过对线路方案比选的多目标决策分析,建立了线路方案比选的 ＴＯＰＳＩＳ模型,对线路方案非劣解集进行排序,提供方案的优先顺序．     

Extension of the TOPSIS method for decision making problems under interval-valued intuitionistic fuzzy environment

TOPSIS is one of the well-known methods for multiple attribute decision making (MADM). In this paper, we extend the TOPSIS method to solve multiple attribute group decision making (MAGDM) problems in interval-valued intuitionistic fuzzy environment in which all the preference information provided by the decision-makers is presented as interval-valued intuitionistic fuzzy decision matrices where each of the elements is characterized by interval-valued intuitionistic fuzzy number (IVIFNs), and the information about attribute weights is partially known. First, we use the interval-valued intuitionistic fuzzy hybrid geometric (IIFHG) operator to aggregate all individual interval-valued intuitionistic fuzzy decision matrices provided by the decision-makers into the collective interval-valued intuitionistic fuzzy decision matrix, and then we use the score function to calculate the score of each attribute value and construct the score matrix of the collective interval-valued intuitionistic fuzzy decision matrix. From the score matrix and the given attribute weight information, we establish an optimization model to determine the weights of attributes, and construct the weighted collective interval-valued intuitionistic fuzzy decision matrix, and then determine the interval-valued intuitionistic positive-ideal solution and interval-valued intuitionistic negative-ideal solution. Based on different distance definitions, we calculate the relative closeness of each alternative to the interval-valued intuitionistic positive-ideal solution and rank the alternatives according to the relative closeness to the interval-valued intuitionistic positive-ideal solution and select the most desirable one(s). Finally, an example is used to illustrate the applicability of the proposed approach.     

Incremental analysis for MCDM with an application to group TOPSIS

The study aims to exploit incremental analysis or marginal analysis to overcome the drawbacks of ratio scales utilized in various multi-criteria or multi-attribute decision making (MCDM/MADM) techniques. In the proposed 11-step procedure, multiple criteria of alternatives are first reorganized as two categories – benefits and costs – and decision information will be manipulated separately. The performances of alternatives are then evaluated on their incremental benefit–cost ratio, and the rank can be obtained by applying the group TOPSIS (technique for order preference by similarity to ideal solution) model (Shih et al., 2007). Two representations of cost, i.e., a cost index and utility index, are proposed in the model to better-fit real-world situations. In addition, some considerations on costs and input–output relations are also discussed in order to understand the essentials of incremental analysis. In the final part, a case of robot selection demonstrates the suggested model to be both robust and efficient in a group decision-making environment.