A fuzzy TOPSIS based approach for e-sourcing

This study presents a methodology for solving the sealed bid, multi-attribute reverse auction problem of e-sourcing in which the sales item is defined by several attributes, the buyer is auctioneer, and the suppliers are the bidders. There is only one buyer and a number of suppliers. Both qualitative and quantitative attributes of benefit and cost types are considered for solving the winner determination (WD) problem of reverse auction. Here, the WD problem is considered as multi-criteria decision making problem (MCDM). In order to address the imprecision of suppliers or decision makers in formulating the preference value of various attributes in MCDM, a fuzzy TOPSIS based methodology along with a mechanism for determination of fuzzy linguistic value of each attribute is proposed in this article. Entropy method is utilised to enumerate the weights of various attributes automatically without involvement of decision makers. An illustrative example is presented to demonstrate the applicability of the proposed methodology.     

直觉模糊多属性决策方法综述

直觉模糊多属性决策是当前决策领域的一个研究热点,在实际决策中有着广泛的应用.按照直觉模糊集的发展形式:直觉模糊集、区间直觉模糊集、直觉三角模糊数和直觉梯形模糊数,分别介绍它们在多属性决策与群决策中的研究现状,并对其未来的发展方向进行了探讨与展望.     

Choquet integral based aggregation approach to software development risk assessment

Software is a crucial component of today’s business environment, and a superior risk management effort is required to adeptly steer software development projects. Software development risks are inherently dependent, in other words, mutually positive or negative assessments of some risks can influence the decision to accept or reject a project. This fact cannot be modeled with a traditional best compromise seeking method. Aggregation operations based on the family of fuzzy integrals include many operators and thus can express a variety of decision maker behaviors. This study proposes an integrated multi-criteria evaluation methodology for software development experts and managers to better enable them to position their projects in terms of the associated risks. The method relies on a special fuzzy operator, namely a two-additive Choquet integral that enables modeling various effects of importance and interactions among risks. The potential of the proposed methodology is exposed through a case study conducted in a Turkish software company.     

基于粒计算的多属性群决策分析

通过仿生人脑智慧性思维,提出了基于粒计算的多属性群决策求解思路.首先给出了粒计算结构模型的数学刻画,而后为细致描述单个决策人对决策问题在粒层间的往返思考,给出了相对意义下的粒信息熵度量,并通过定义“粒划分匹配率”和“粒划分覆盖率”,实现基于相似关系阈值进行单个决策人独立思考下的粒层搜索寻优计算.最终寻优结果体现于该决策人理解思维下的特征属性集权重向量值上.最后基于各个决策人思考粒层下的权重向量值,通过非线性优化模型的求解,在粒层间予以再次平衡寻优求解,获得决策群体一致认可的特征属性集最优权重向量值,实现对多属性群决策问题的一体化合理求解.仿真实例验证了本文方法的可行性和优越性.     

A possibility degree method for interval-valued intuitionistic fuzzy multi-attribute group decision making

The ranking of interval-valued intuitionistic fuzzy sets (IVIFSs) is very important for the interval-valued intuitionistic fuzzy decision making. From the probability viewpoint, the possibility degree of comparison between two interval-valued intuitionistic fuzzy numbers (IVIFNs) is defined by using the notion of 2-dimensional random vector, and a new method is then developed to rank IVIFNs. Hereby the ordered weighted average operator and hybrid weighted average operator for IVIFNs are defined based on the Karnik–Mendel algorithms and employed to solve multi-attribute group decision making problems with IVIFNs. The individual overall attribute values of alternatives are obtained by using the weighted average operator for IVIFNs. By using the hybrid weighted average operator for IVIFNs, we can obtain the collective overall attribute values of alternatives, which are used to rank the alternatives. A numerical example is examined to illustrate the effectiveness and flexibility of the proposed method in this paper.     

Effects of music tempo and task difficulty on multi-attribute decision-making: An eye-tracking approach

This study examined the effects of music tempo and task difficulty on the performance of multi-attribute decision-making according two alternative perspectives: background music as the arousal inducer vs. the distractor. An eye-tracking based experiment was conducted. Our results supported the arousal inducer perspective that, with the same level of decision time, participants made decisions more accurately with the presentation of faster than slower tempo music. Further, faster tempo music was found to improve the accuracy of harder decision-making only, not that of easier decision-making. More interestingly, our exploratory analysis on eye fixations found the occurrence of adaptive behavior, namely, that the search pattern of participants became more intra-dimensional under the faster tempo music as compared with the slower tempo music.     

Bootstrap method approach in designing multi-attribute control charts

In a production process, when the quality of a product depends on more than one correlated characteristic, multivariate quality control techniques are used. Although multivariate statistical process control is receiving increased attention in the literature, little work has been done to deal with multi-attribute processes. In monitoring the quality of a product or process in multi-attribute environments in which the attributes are correlated, several issues arise. For example, a high number of false alarms (type I error) occur and the probability of not detecting defects (type II error) increases when the process is monitored by a set of independent uni-attribute control charts. In this paper, to overcome these problems, first we develop a new methodology to derive control limits on the attributes based on the bootstrap method in which we build simultaneous confidence intervals on the attributes. Then, based upon the in-control and out-of-control average run length criteria we investigate the performance of the proposed method and compare it with the ones from the Bonferroni and Sidak’s procedure using simulation. The results of the simulation study show that the proposed method performs better than the other two methods. At the end, we compare the bootstrap method with the T ² control chart for attributes.     

Detection and classification mean-shifts in multi-attribute processes by artificial neural networks

To monitor the quality of a multi-attribute process, some issues arise. One of them being the occurrence of a high number of false alarms (type I error) and the other an increase in the probability of not detecting defects when the process is monitored by a set of independent uni-attribute control charts. In this paper, based upon the artificial neural network capabilities we develop a new methodology to overcome this problem. We design a perceptron neural network to monitor either the proportions of several types of product nonconformities (instead of using several np charts) or the number of different types of defects (instead of using several c charts) in a product. Moreover, while the proposed method possesses the ability to be applied for small sample sizes, it is also able to diagnose the mean shift online. We present two simulation experiments in which the proportions of several types of nonconformities are monitored. In addition, we present one more simulation experiment in which the number of different types of defect is controlled. We also compare the performance of the proposed methodology with the ones from the Mnp and T ² charts for multi-attribute processes. The results of the simulation studies are encouraging.     

正流量液压挖掘机行走跑偏诊断序列决策

针对正流量液压挖掘行走跑偏的问题,提出一种基于T-S故障树的故障诊断最优序列多属性决策方法。研究正流量液压挖掘机行走液压系统,分析行走跑偏成因。最后,将基于T-S故障树的故障最优诊断序列多属性决策方法应用于正流量液压挖掘机行走跑偏分析,利用T-S故障树求解正流量挖掘机行走液压系统各元件的概率重要度作为故障诊断排序的重要依据,利用多属性决策方法生成最优诊断序列,对快速精准、最小代价定位正流量液压挖掘机行走跑偏故障源起重大作用。