A fuzzy VIKOR method for supplier selection based on entropy measure for objective weighting

Recently, resolving the problem of evaluation and ranking the potential suppliers has become as a key strategic factor for business firms. With the development of intelligent and automated information systems in the information era, the need for more efficient decision making methods is growing. The VIKOR method was developed to solve multiple criteria decision making (MCDM) problems with conflicting and non-commensurable criteria assuming that compromising is acceptable to resolve conflicts. On the other side objective weights based on Shannon entropy concept could be used to regulate subjective weights assigned by decision makers or even taking into account the end-users’ opinions. In this paper, we treat supplier selection as a group multiple criteria decision making (GMCDM) problem and obtain decision makers’ opinions in the form of linguistic terms. Then, these linguistic terms are converted to trapezoidal fuzzy numbers. We extended the VIKOR method with a mechanism to extract and deploy objective weights based on Shannon entropy concept. The final result is obtained through next steps based on factors R, S and Q. A numerical example is proposed to illustrate an application of the proposed method.     

Database design practices for inverted files

Database research literature has proposed many procedures, both manual and automated, for database design; selection of secondary indexes for inverted file type database management systems (DBMS) has been repeatedly addressed. The empirical study reported here indicates that practical inverted file design has been relatively unaffected by this research.This paper characterizes the actual database design process used at inverted file DBMS installations along such dimension as: types of secondary keys constructed, the individuals who make index design decisions, the decisions that are changed (and when) after the initial database implementation, the factors that are considered in indexing decisions, and the literature which is used in the process. The study shows that key selection (as one example of a design decision) is addressed by ad hoc procedures and well conceived procedures are not used. Further, the results indicate that database design is dominated by users and systems analysts, indexes are frequently changed and a wide range of database performance and convenience factors are influential in practice. The paper concludes with some recommendations for database design support tools.     

Minimizing neighborhood evidential decision error for feature evaluation and selection based on evidence theory

Feature selection is an important preprocessing step in pattern recognition and machine learning, and feature evaluation arises as key issues in the construction of feature selection algorithms. In this study, we introduce a new concept of neighborhood evidential decision error to evaluate the quality of candidate features and construct a greedy forward algorithm for feature selection. This technique considers both the Bayes error rate of classification and spatial information of samples in the decision boundary regions.Within the decision boundary regions, each sample x_i in the neighborhood of x provides a piece of evidence reflecting the decision of x so as to separate the decision boundary regions into two subsets: recognizable and misclassified regions. The percentage of misclassified samples is viewed as the Bayes error rate of classification in the corresponding feature subspaces. By minimizing the neighborhood evidential decision error (i.e., Bayes error rate), the optimal feature subsets of raw data set can be selected. Some numerical experiments were conducted to validate the proposed technique by using nine UCI classification datasets. The experimental results showed that this technique is effective in most of the cases, and is insensitive to the size of neighborhood comparing with other feature evaluation functions such as the neighborhood dependency.     

Group decision making with linguistic preference relations with application to supplier selection

Linguistic preference relation is a useful tool for expressing preferences of decision makers in group decision making according to linguistic scales. But in the real decision problems, there usually exist interactive phenomena among the preference of decision makers, which makes it difficult to aggregate preference information by conventional additive aggregation operators. Thus, to approximate the human subjective preference evaluation process, it would be more suitable to apply non-additive measures tool without assuming additivity and independence. In this paper, based on λ-fuzzy measure, we consider dependence among subjective preference of decision makers to develop some new linguistic aggregation operators such as linguistic ordered geometric averaging operator and extended linguistic Choquet integral operator to aggregate the multiplicative linguistic preference relations and additive linguistic preference relations, respectively. Further, the procedure and algorithm of group decision making based on these new linguistic aggregation operators and linguistic preference relations are given. Finally, a supplier selection example is provided to illustrate the developed approaches.     

Mental workload and cognitive task automaticity: an evaluation of subjective and time estimation metrics

Abstract

The evaluation of mental workload is becoming increasingly important in system design and analysis. The present study examined the structure and assessment of mental workload in performing decision and monitoring tasks by focusing on two mental workload measurements: subjective assessment and time estimation. The task required the assignment of a series of incoming customers to the shortest of three parallel service lines displayed on a computer monitor. The subject was either in charge of the customer assignment (manual mode) or was monitoring an automated system performing the same task (automatic mode). In both cases, the subjects were required to detect the non-optimal assignments that they or the computer had made. Time pressure was manipulated by the experimenter to create fast and slow conditions. The results revealed a multi-dimensional structure of mental workload and a multi-step process of subjective workload assessment. The results also indicated that subjective workload was more influenced by the subject's participatory mode than by the factor of task speed. The time estimation intervals produced while performing the decision and monitoring tasks had significantly greater length and larger variability than those produced while either performing no other tasks or performing a well practised customer assignment task. This result seemed to indicate that time estimation was sensitive to the presence of perceptual/cognitive demands, but not to response related activities to which behavioural automaticity has developed.     

An intelligent supplier evaluation,selection and development system

Supplier evaluation and selection process has a critical role and significant impact on purchasing management in supply chain. It is also a complex multiple criteria decision making problem which is affected by several conflicting factors. Due to multiple criteria effects the evaluation and selection process, deciding which criteria have the most critical roles in decision making is a very important step for supplier selection, evaluation and particularly development. With this study, a hybridization of fuzzy c-means (FCM) and rough set theory (RST) techniques is proposed as a new solution for supplier selection, evaluation and development problem. First the vendors are clustered with FCM algorithm then the formed clusters are represented by their prototypes that are used for labeling the clusters. RST is used at the next step of modeling where we discover the primary features in other words the core evaluation criteria of the suppliers and extract the decision rules for characterizing the clusters. The obtained results show that the proposed method not only selects the best supplier(s), also clusters all of the vendors with respect to fuzzy similarity degrees, decides the most critical criteria for supplier evaluation and extracts the decision rules about data.     

基于语义的决策模型能力评估与选择方法

本体决策模型选择的最佳手段是使计算机在理解决策问题和决策模型自身能力的基础上进行.通过领域本体,决策问题和决策模型可以具备被计算机自动理解的形式化语义.在理解决策问题语义的基础上,系统可选择对应的求解模型类别并获取决策问题内在需求,进而根据对应的候选模型语义对其具备的能力进行评估,选择最适合于决策问题的决策模型.最后,实例分析结果表明了这种模型选择方法是有效且可行的.     

GACRM: A dynamic multi-Attribute decision making approach to large-Scale Web service composition

The problem of QoS-aware Web service composition (QWSC), i.e., how to select from a pool of candidate services to construct a composite service with the best overall QoS performance, is an NP-hard problem. To address a large-scale QWSC problem, a novel method is proposed based on information theory, multi-attribute decision making (MADM) and genetic algorithm. To capture complex judgments, the QWSC problem is formulated into a MADM representation which aims to find acceptable solutions assessed by multiple QoS attributes with varying distributions. To solve the MADM problem for QWSC, each QoS attribute is weighted in both a priori, subjective perspective and a posteriori, information-based perspective based on the discriminative capability of QoS attributes for a dynamic pool of candidate services. Furthermore, to solve the large-scale QWSC problem that conventional MADM methods cannot navigate, we develop a GACRM algorithm by integrating genetic algorithm (GA) with Compromise Ratio Method (CRM). Experiments demonstrate that GACRM obtains nearly the same solution ranking by the CRM but scales much better in terms of computation time for large-scale QWSC problems.     

A Visualization‐Based Analysis System for Urban Search & Rescue Mission Planning Support

We propose a visualization system for incident commanders (ICs) in urban search and rescue scenarios that supports path planning in post‐disaster structures. Utilizing point cloud data acquired from unmanned robots, we provide methods for the assessment of automatically generated paths. As data uncertainty and a priori unknown information make fully automated systems impractical, we present the IC with a set of viable access paths, based on varying risk factors, in a 3D environment combined with visual analysis tools enabling informed decision making and trade‐offs. Based on these decisions, a responder is guided along the path by the IC, who can interactively annotate and reevaluate the acquired point cloud and generated paths to react to the dynamics of the situation. We describe visualization design considerations for our system and decision support systems in general, technical realizations of the visualization components, and discuss the results of two qualitative expert evaluation; one online study with nine search and rescue experts and an eye‐tracking study in which four experts used the system on an application case.     

Processing and Transmission of Confidence in Recurrent Neural Hierarchies

This article addresses the construction of hierarchies from dynamic attractor networks. We claim that such networks, e.g., dynamic neural fields (DNFs), contain a data model which is encoded in their lateral connections, and which describes typical properties of afferent inputs. This allows to infer the most likely interpretation of inputs, robustly expressed through the position of the attractor state. The principal problem resides in the fact that positions of attractor states alone do not reflect the quality of match between input and data model, termed decision confidence. In hierarchies, this inevitably leads to final decisions which are not Bayes-optimal when inputs exhibit different degrees of ambiguity or conflict, since the resulting differences in confidence will be ignored by downstream layers. We demonstrate a solution to this problem by showing that a correctly parametrized DNF layer can encode decision confidence into the latency of the attractor state in a well-defined way. Conversely, we show that input stimuli gain competitive advantages w.r.t. each other as a function of their relative latency, thus allowing downstream layers to decode attractor latency in an equally well-defined way. Putting these encoding and decoding mechanisms together, we construct a three-stage hierarchy of DNF layers and show that the top-level layer can take Bayes-optimal decisions when the decisions in the lowest hierarchy levels have variable degrees of confidence. In the discussion, we generalize these findings, suggesting a novel possibility to represent and manipulate probabilistic information in recurrent networks without any need for log-encoding, just using the biologically well-founded effect of response latency as an additional coding dimension.     

Personnel Selection Fuzzy Model

The paper presents a two-level personnel selection fuzzy model: short list and hiring decision. The model is an attempt to minimize subjective judgment in the process of distinguishing between an appropriate employee and an inappropriate employee for a job vacancy. The model comprises an analytic hierarchy process of three levels. The lowest level relates to the preliminary selection or shortlist procedure. Modifying multi-objective models of decision-making, the main decision elements are assumed as linguistic fuzzy variables. The problem is considered broad, since the worth values of the variables are calculated as expected values of the fuzzy variables. The second level relates to the hiring decision or selection of a final candidate for an employment opportunity. The selector assesses his/her own expectations of the short-listed job applicants. The expectations are treated by a probabilistic–possibilistic approach. The top level is the expected utility of hiring the successful candidate. Compared to the traditional way of selecting an appropriate short-listed job applicant this model minimizes individual judgment at both short-listed and hiring decision levels. The model is illustrated by a case study.     

Personnel selection based on computing with words and fuzzy MULTIMOORA

Personnel selection process is aimed at choosing the best candidate to fill the defined vacancy in a company. It determines the input quality of personnel and thus plays an important role in human resource management. Given the uncertain, ambiguous, and vague nature of personnel selection process, it requires the application of multi-criteria decision making (MCDM) methods for robust recruitment. This paper hence is aimed at extending the fuzzy MULTIMOORA for linguistic reasoning under group decision making. The fuzzy MULTIMOORA was further modified in this study. The fuzzy MULTIMOORA for group decision making (MULTIMOORA–FG) enables to aggregate subjective assessments of the decision-makers and thus offer an opportunity to perform more robust personnel selection procedures. A personnel selection problem illustrated the group decision-making procedure according to MULTIMOORA–FG: the enterprise has formed an executive committee consisting of four decision-makers to choose the best candidate from another four participants to fill the vacancy. The committee has decided to consider eight qualitative attributes expressed in linguistic variables. The numerical example exhibited possibilities for improvement of human resources management as well as any other business decision area by applying MULTIMOORA–FG.     

Artificial data for pattern recognition

The paper describes the generation of three types of artificial data and their use as test material in pattern recognition research. Type A data: The user defines the perfect decision surface. The classes are separable and the pdf's flat. This type is useful in two ways: (i) To investigate whether a learning procedure can achieve a minimal-cost solution. (ii) To compare the powers of two classifiers. Type B data: The user defines the optimal decision surface. The classes are not separable; the degree of overlap between the classes can be controlled by the user. The pdf's are approximately flat, except in regions close to this optimal decision boundary. This type is useful in the following ways: (i) To study the effect of varying the overlap between classes upon a learning procedure. (ii) To compare the powers of two classifiers on a random problem. Type C data: This type is a model of natural, clustered data. The user specifies the location, height, and spread of a number of “hills” in the pdf (for each class). These parameters allow us to calculate the pdf's and hence the Bayes' classification, at any given point. This provides a powerful tool for the objective evaluation of a learning classifier, operating on a realistic problem.     

基于概率语言术语集的云服务选择

为了解决云服务评估决策中QoS(Quality of Service,服务质量)属性的动态性刻画不足以及传统决策方法中用户主观因素过强的缺点,提出了一种基于概率语言术语集（Probabilistic Linguistic Term Set,PLTS）的选择方法。通过相似性权重与可靠性权重结合获取推荐权重,加入决策矩阵中得到综合评估矩阵;通过层次分析法（Analytic Hierarchy Process,AHP）获取的属性权重与综合评估矩阵结合得到加权综合评估矩阵;并采用TOPSIS(Technique for Order Preference by Similarity to Ideal Solution,逼近理想解排序法)方法综合评估候选服务的性能。案例分析和对比分析表明,该模型能够有效提高云服务选择的准确率与执行效率,并为云环境下的多属性决策领域提供了新的思路。     

A rule-based group decision model for warehouse evaluation under interval-valued Intuitionistic fuzzy environments

We consider the issue of warehouse evaluation towards successful logistic and supply chain management. Suppose a company has managed a chain of owned warehouses, and now this company is in need of acquiring some new and profitable warehouse adding to its operation chain. A key business decisions here is how to choose the most profitable warehouses from a number of potential warehouses. In reality, the challenge is that the future profitability is unpredictable. Therefore, it is infeasible to rank potential warehouses directly for choice. To address such a problem, this paper proposes a new rule-based decision model. This model includes the following characteristics: (i) decision information is provided via interval-valued intuitionistic fuzzy values; (ii) multiple experts as a group of decision makers are involved; (iii) both subjective evaluations from experts and objective data of historical profitability are employed; (iv) both certain and uncertain information are exploited. The core decision mechanism is, making use of uncertain information of owned warehouses, to induce a collection of “if…then…”rules, and subsequently to exploit these rules for prediction of preference orders of all potential warehouses. Therein, we develop and integrate multiple techniques for the purposes of (a) aggregation of uncertain information; (b) construction of pairwise comparison; (c) induction of certain and uncertain rules; and (d) decision rules exploitation. We finally elaborate our discussion with a numerical example illustrating the application of the proposed decision mechanism to supply-chain domain problems.     

The Development and Implementation of the Data-Binding Algorithm in the State Civil Information System

This paper describes the development of a data-binding algorithm. The algorithm is based on a variant of the adaptive record similarity measure (ARSM). Optimization of this variant for solving the problem of data binding is carried out using a machine-learning algorithm. The approach to estimating the adequacy of ARSM for data binding consists in introduction and automated selection of the threshold values by the machine-learning algorithm, which is achieved by introducing the fitness function representing the modification F_measure.     

System synthesis with morphological clique problem: fusion of subsystem evaluation decisions

The article focuses on the analysis and evaluation of composite systems on the basis of ordinal estimates for system components. This viewpoint corresponds to system synthesis as an integration (fusion) of local decisions into a global one. We examine hierarchical (tree-like) models for composite systems and consider the following: (1) traditional multi-criteria evaluation approaches; (2) morphological clique problem with special discrete spaces for evaluation of system excellence; and (3) generalization of morphological clique problem. The generalization of morphological clique problem is based on the following: (a) structure of compatibility between system components and its influence to problem complexity, (b) some kinds of compatibility for the components (e.g., asymmetric, negative), and (c) poset-like scales for system components (local decisions) and their aggregation into a scale for a resultant global decision. We consider four basic versions of poset-like scales for system components and several corresponding extended modified discrete spaces of system excellence. Numerical examples illustrate the material (e.g., scales, aggregation of scales, algorithms).     

A GRA-based intuitionistic fuzzy multi-criteria group decision making method for personnel selection

Due to the increasing competition of globalization, selection of the most appropriate personnel is one of the key factors for an organization’s success.The importance and complexity of the personnel selection problem call for the method combining both subjective and objective assessments rather than just subjective decisions. The aim of this paper is to develop a new method for solving the decision making process. An intuitionistic fuzzy multi-criteria group decision making method with grey relational analysis (GRA) is proposed. Intuitionistic fuzzy weighted averaging (IFWA) operator is utilized to aggregate individual opinions of decision makers into a group opinion. Intuitionistic fuzzy entropy is used to obtain the entropy weights of the criteria. GRA is applied to the ranking and selection of alternatives. A numerical example for personnel selection is given to illustrate the proposed method finally.