Integrated rough VIKOR for customer-involved design concept evaluation combining with customers’ preferences and designers’ perceptions

Customer-involved design concept evaluation (DCE) allows customers to take part in evaluating the design alternatives to get more popular design concept. Traditional customer-involved DCE methods still focus on the collection of customer responses and only consider cost and benefit characteristics of design criteria in multi-criteria decision-making (MCDM) based evaluation process. Few studies have customized the decision-making algorithms specifically aimed at customers’ preferences. This paper further explores the customers’ influences in the early stages of the product design development, and proposes a new rough number based MCDM model (i.e., VIKOR) incorporating customers’ preferences for design specifications along with designers’ perceptions for the characteristics of design criteria (cost and benefit) to perform concept evaluation under subjective environment, and this proposed method is named as integrated rough VIKOR (IR-VIKOR). The objective of this study is to identify the best design concept which maximizes the satisfactions of expectations from most customers as well as conforms to the characteristics of design criteria. Firstly, Shannon entropy is used to obtain the weightings and relative importance ratings of design criteria from the customers’ preferences. Secondly, the customers’ preferences for design attribute values, the importance ratings of design criteria and the characteristics of design criteria are combined together to define the ideal solutions to calculate the rough evaluation index of each design alternative in IR-VIKOR, and finally the ranking result is provided by IR-VIKOR to determine the best design concept. A practical design example is introduced to illustrate the evaluation process of this proposed method, and the empirical comparisons are further carried out to validate its superiority for DCE. Through the sensitivity analysis experiments including i) inside IR-VIKOR, and ii) between IR-VIKOR and other classical MCDM methods, the proposed method is proved to be a reliable and feasible customer-involved DCE approach.     

Prioritizing the alternatives of the natural gas grid conversion to hydrogen using a hybrid interval rough based Dombi MARCOS model

The aim of this study is to disclose an integrated multi-criteria decision making model as an instrument able to be used for hydrogen gas grid development via natural gas network conversion by considering four main criteria and sixteen sub-criteria. The proposed model consists of two stages using interval rough based Dombi MARCOS (Measurement Alternatives and Ranking according to the COmpromise Solution) method. In the first stage, the interval rough Bonferroni logarithmic methodology of additive weights is applied for determining the weight coefficients. In the second stage, the improved interval rough MARCOS method is used to rank the alternatives. A case study for hydrogen gas grid development in Romania set out the applicability and effectiveness of the proposed model. The proposed model is analysed from various points of view so that it can be used effectively in reality not only theoretically. This study analyzes four technical alternatives for hydrogen gas grid development. The results show that the conversion for mixtures natural gas with hydrogen a transporting a mix of gases is the best alternative among the four alternatives, for the next period and in the specific context described by this study. The current technical-economic criteria are rather a constraint when discussing gas infrastructures. For choosing a sustainable solution, a major role comes for decision makers, along with the desire to pay fairly from the consumer.     

Influence of interfaces roughness on elastic properties of layered media

We present a numerical study of the effect of the interfaces’ roughness on layered media’s upscaled elastic parameters. First, we consider a layered model with two types of elastic materials, assuming that the interfaces are not flat but rough, and apply the numerical upscaling technique to estimate the effective elastic properties of such models. After that, we apply a downscaling technique to reconstruct a layered media with flat interfaces but with uncertainties in elastic moduli of the layers. Next, we compute the covariance of the elements of the reconstructed stiffness matrix and prove that the logarithm of this matrix is linearly related to the logarithms of the standard deviation and the correlation length of the interfaces of the original problem. Finally, we use generated dataset to estimate covariance matrices of the stiffness matrix for arbitrary interface roughnesses.     

Rough集理论在矿井运输机故障诊断的研究

Rough集理论是一种数据分析和处理方法，使用Rough集理论可以对决策表进行简化。该文针对矿井运输机故障诊断问题．利用Rough集属性约简和值约简方法对决策表进行约简．根据约简的结果生成规则。结果表明：使用Rough集理论可以在保留分类能力不变的前提下，简化对诊断信息的需求，而对故障样例的诊断得到的规则基本可信。     

粗集数据挖掘技术在市场营销中的应用

基于粗糙集理论的启发式数据挖掘算法,先量化数据属性的重要性,再对属性排序,并对选择的属性子集进行约简条件判定.在选择若干次后获得满足约简条件的属性子集.利用该算法,通过预处理、数据约减等步骤从商场营销数据库中收集顾客属性数据,消去多余数据属性.并由此预测需求趋势,评估需求倾向的改变,以便进行决策.     

Analysing ownership,locational and internalization advantages of Chinese construction MNCs using rough sets analysis

The rough sets analysis focusing on the ownership, locational and internalization (OLI) advantages of Chinese construction multinational corporations (MNCs) in the international market under Dunning's Eclectic Paradigm was adopted to overcome the sample size constraint in MNC research that seeks to examine the causality patterns of the factors identified. The rough sets methodology provides a solution that conventional statistical methods do not offer to ascertain how these factors are determined and their influence in the OLI analysis of the performance of Chinese construction MNCs. The rough sets approach to handling imperfect data with uncertainty and vagueness was adopted to describe dependencies between attributes, evaluate the significance of attributes and deal with inconsistencies. Based on a questionnaire survey of 31 Chinese construction MNCs, findings from the rough sets analysis suggest, among other things, that: (1) a firm is likely to perform well in terms of its ownership advantages when it has a significant advantage on its reputation and its accessibility to resources when compared with local contractors; (2) a firm may be expected to achieve better results in terms of its locational advantages if the large number of competitors from China in the host countries becomes its most important consideration; and (3) a firm that conscientiously avoids or reduces information search and business negotiation costs would tend to perform well in terms of its internalization advantages.     

A novel approach to lean control for Taiwan-funded enterprises in mainland China

The lean control approach has been successfully applied to reduce waste and improve customer service in numerous Taiwan-funded enterprises. Although numerous models have been developed to overcome its limitations, such as determining unnecessary moving, unnecessary inventory, and redundant transportation, they do not, however, identify focused stages in which to start lean control. To secure Taiwan-Funded Enterprises in Mainland China (TFEMC), in this paper, after using value stream mapping (VSM) to show the current state of manufacturing processes, rough set theory is adopted and used to identify types of focused stages, where lean controls are most required. A generalised label-correcting algorithm is then developed to determine the desired stages of lean manufacturing, which are difficult to show in the VSM. This methodology is suitable for a repetitive manufacturing environment of mixed type, i.e., job shop and flow shop, and achieves the following objectives: 1. Decreasing work in progress (WIP) inventory.

2. Reducing inventory and logistics cost globally.

The approach is novel and the illustrated case could be a role model of TFEMC because different types of problems can be solved with one algorithm; the reduct rules can be inducted to determine focus stages efficiently; and waste is reduced from the viewpoint of system thinking.     

Rule induction for hierarchical attributes using a rough set for the selection of a green fleet

Rough set theory (RST) has been the subject of much study and numerous applications in many areas. However, most previous studies on rough sets have focused on finding rules where the decision attribute has a flat, rather than hierarchical structure. In practical applications, attributes are often organized hierarchically to represent general/specific meanings. This paper (1) determines the optimal decision attribute in a hierarchical level-search procedure, level by level, (2) merges the two stages, generating reducts and inducting decision rules, into a one-shot solution that reduces the need for memory space and the computational complexity and (3) uses a revised strength index to identify meaningful reducts and to improve their accuracy. The selection of a green fleet is used to validate the superiority of the proposed approach and its potential benefits to a decision-making process for transportation industry.     

基于邻域粗糙集的大规模层次分类在线流特征选择

在分类学习任务中,数据的类标记空间存在层次化结构,特征空间伴随着未知性和演化性.因此,文中提出面向大规模层次分类学习的在线流特征选择框架.定义面向层次化结构数据的邻域粗糙模型,基于特征相关性进行重要特征动态选择.最后,基于特征冗余性进行鉴别冗余动态特征.实验验证文中算法的有效性.     

多粒度支持直觉模糊粗糙集的多属性决策方法

针对多属性决策中多个相互冲突的属性信息使决策者很难做出决策判断的问题,文中从支持直觉模糊集的角度研究该问题.首先,在支持直觉模糊集的基础上,结合多粒度粗糙集理论,构造乐观、悲观两种多粒度支持直觉模糊粗糙集模型,分析两种模型之间的相互关系,讨论相关性质.然后,利用t-模和t-余模定义拟合函数,提出多粒度支持直觉模糊粗糙集的多属性决策求解方法,同时定义得分函数和精确函数排序决策结果,提取相应的决策规则,设计算法.实例分析表明,文中方法使决策者在处理信息冲突的多属性决策问题时可根据实际需求选择最优决策方案