A novel credibility-based group decision making method for Enterprise Architecture scenario analysis using Data Envelopment Analysis

Analysis and selection of Enterprise Architecture (EA) scenarios is a difficult and complex decision making process directly effecting the long-term business strategies realization. This complexity is associated with contradictory objectives and significant uncertainties involved in analysis process. Although a large body of intuitive and analytical models for EA analysis has evolved over the last few years, none of them leads to an efficient and optimized ranking in fuzzy environments. Moreover, it is necessary to simultaneously employ some complementary methods to reflect the ambiguity and vagueness as the main sources of uncertainty. This paper incorporates the concept of Data Envelopment Analysis (DEA) model into EA scenario analysis through a group analysis under uncertain conditions. To resolve the vagueness and ambiguity of the EA analysis, fuzzy credibility constrained programming and p-robustness technique are applied, respectively. Not only is the proposed DEA model linear, robust, and flexible in aggregating experts’ opinion in a group decision making process, but it also is successful in discrimination power improvement – a major shortcoming associated with classic DEA model. The proposed model provides useful solutions to support decision making process for large-scale Information Technology (IT) development planning.     

Intermodal terminal planning by Petri Nets and Data Envelopment Analysis

A procedure for planning and resources’ management in intermodal terminals is presented. It integrates Timed Petri Nets (TPNs) and Data Envelopment Analysis (DEA) and consists of three steps: the terminal modeling via TPNs to model the regular behavior; the evaluation of whether the current configuration may cope with increased freight flows; if not, the analysis by cross-efficiency DEA of alternative planning solutions. The procedure provides the decision maker with number, capacity, and schedule of resources to tackle the flows increase. The method is evaluated by a real case study, showing that integrating TPNs and DEA allows taking planning decisions under conflicting requirements.     

Data Envelopment Analysis of clinics with sparse data: Fuzzy clustering approach

This paper presents a method for utilizing Data Envelopment Analysis (DEA) with sparse input and output data using fuzzy clustering concepts. DEA, a methodology to assess relative technical efficiency of production units is susceptible to missing data, thus, creating a need to supplement sparse data in a reliable and accurate manner. The approach presented is based on a modified fuzzy c-means clustering using optimal completion strategy (OCS) algorithm. This particular algorithm is sensitive to the initial values chosen to substitute missing values and also to the selected number of clusters. Therefore, this paper proposes an approach to estimate the missing values using the OCS algorithm, while considering the issue of initial values and cluster size. This approach is demonstrated on a real and complete dataset of 22 rural clinics in the State of Kansas, assuming varying levels of missing data. Results show the effect of the clustering based approach on the data recovered considering the amount and type of missing data. Moreover, the paper shows the effect that the recovered data has on the DEA scores.     

Decision making support system for house purchase by using fuzzy logic

In this paper, we suggest a decision making support system for house purchasers, using fuzzy inference and hierarchic structure of evaluation. Main part of this system consist of macro and micro evaluation. Essential factors are taken into account in macro evaluation, and unessential detailed factors are considered later in micro evaluation. By adopting this structure, many decision makers could get their most suitable result.     

基于数据仓库技术的决策分析系统概念设计

以数据仓库技术为基础的决策分析是数据处理的发展方向,目前正得到很多行业的应用。为提高科学决策能力,增强在市场经营等方面的正确判断能力,需要充分利用已经积累的大量的业务数据,以数据仓库技术为基础,构建数据处理和决策分析平台,进一步开发数据中蕴涵的信息,形成决策需要的对策方案和应对市场的反应策略。本文从数据仓库技术及数据分析技术出发,概念性地构建了可应用的数据仓库和分析系统,为今后在相关领域应用奠定了基础。     

使用Delphi的Decision Cube进行多维数据分析

从多维数据分析的角度出发，对Delphi的决策支持组件Dcision Cube进行了介绍，并介绍了如何利用Dicision Cube进行多维数据分析以及支持决策，最后简要对Decision Cube进行了总结。     

A study of optimal weights of Data Envelopment Analysis - Development of a context-dependent DEA-R model

The weight is one of the main issues of Data Envelopment Analysis (DEA), and relevant theoretical research indicates that many DEA mathematical models include redundant restraints on weight, resulting in underestimated efficiency, pseudo inefficiency, and difficulty in representing specific Input/Output relationships. This study proposes a context-dependent DEA-R model to address shortcomings resulting from redundant restraints on the weights of an efficient decision making unit (DMU), and converts the optimal weight to analyze the influences of redundant restraints on weights. The evaluation results of Taiwan medical centers show that the efficiency of the DMU is underestimated and pseudo inefficiency may occur due to redundant restraints on weight. Moreover, optimal weights are used as variables to conduct cluster analysis in order to determine the information of the weights. The results of cluster analysis indicate that it can assist DMUs in understanding the relationships between DMUs, and contribute to the development of a unique survival strategy for hospitals.     

New methods for ranking decision making units based on the dispersion of weights and Norm 1 in Data Envelopment Analysis

One of the drawbacks of Data Envelopment Analysis (DEA) is the problem of lack of discrimination among efficient Decision Making Units (DMUs) and hence, yielding large number of DMUs as efficient ones. The main purpose of this paper is to overcome this inability. One of the methods for ranking efficient DMUs is minimizing the Coefficient of Variation (CV) for inputs-outputs weights, which, was suggested by Bal et al. (2008). In this paper, we modify the model and introduce two new models for ranking efficient DMUs based on Norm 1 and using means of inputs-outputs weights. To illustrate purpose, numerical examples are given.     

Measurement of Fitness Function efficiency using Data Envelopment Analysis

Over the last years, Evolutionary Algorithms (EAs) have been proposed aiming to find the best configuration of the Artificial Neural Networks (ANN) parameters. Among several parameters of an EA that can influence the quality of the found solution, the choice of the Fitness Function is the most important for its effectiveness and efficiency, given that different Fitness Functions have distinct fitness landscapes. In other words, the Fitness Function guides the evolutionary process of the candidate solutions according with a given criterion of the performance. However, there is not an universal criterion to identify the best performance measure. Thus, what is the Fitness Function more efficient among a set of several possible options? This paper presents a methodology based on Data Envelopment Analysis (DEA) to find the more efficient Fitness Function among candidates. The DEA is used to determine the best combination of statistical measures to build the more efficient Fitness Function for a EA. The case study employed here consists of a hybrid system composed by Evolutionary Strategy and ANN applied to solve the time series forecasting problem. The data analyzed are composed by financial, agribusiness and natural phenomena. The results show that establishment of the Fitness Function is a crucial point in the EA design, being a key factor to obtain the best solution for a limited number of EA’s iteration.     

A soft computing method of performance evaluation with MCDM based on interval-valued fuzzy numbers

This study presented a new performance evaluation method for tackling fuzzy multicriteria decision-making (MCDM) problems based on combining VIKOR and interval-valued fuzzy sets. The performance evaluation problem often exists in complex administrative processes in which multiple evaluation criteria, subjective/objective assessments and fuzzy conditions have to be taken into consideration simultaneously in management. Here, the subjective, imprecise, inexact and uncertain evaluation processes are modeled as fuzzy numbers by means of linguistic terms, as fuzzy theory can provide an appropriate tool to deal with such uncertainties. However, the presentation of linguistic expressions in the form of ordinary fuzzy sets is not clear enough [15] and [21]. Interval-valued fuzzy sets can provide more flexibility [4] and [14] to represent the imprecise/vague information that results, and it can also provide a more accurate modeling. This paper presents the interval-valued fuzzy VIKOR, which aims to solve MCDM problems in which the weights and performances of criteria are unequal by using the concepts of interval-valued fuzzy sets. A case study for evaluating the performances of three major intercity bus companies from an intercity public transport system is conducted to illustrate the effectiveness of the method.     

不确定性多属性决策中的ER方法改进

基于对不确定性多属性决策问题中ER方法的研究,提出一种不确定性多属性决策中的改进ER方法,井证明该方法完全满足证据合成的4个公理．通过实例运算,进一步验证了新方法的有效性和合理性．     

A Data Complexity Analysis of Comparative Advantages of Decision Forest Constructors

Using a number of measures for characterising the complexity of classification problems, we studied the comparative advantages of two methods for constructing decision forests – bootstrapping and random subspaces. We investigated a collection of 392 two-class problems from the UCI depository, and observed that there are strong correlations between the classifier accuracies and measures of length of class boundaries, thickness of the class manifolds, and nonlinearities of decision boundaries. We found characteristics of both difficult and easy cases where combination methods are no better than single classifiers. Also, we observed that the bootstrapping method is better when the training samples are sparse, and the subspace method is better when the classes are compact and the boundaries are smooth. Received: 03 November 2000, Received in revised form: 25 October 2001, Accepted: 04 January 2002     

A knowledge-based decision support system for measuring the performance of government real estate investment

This paper describes a knowledge-based decision support system for measuring the performance of government real estate investment using DEA models. We propose an evaluation framework for real estate investment, including a database, a model base, and a knowledge base, to create a tool that a government can use to deal with decision-making problems via the Internet. This decision support system converts numerical data into information that can be used to evaluate possible real estate investments. Particularly, rules in the rule base are explained in more detail for illustrating the process of reasoning and KDSSGREI adapts quickly and accurately to infer and generate suggestions or actions. Data envelopment analysis (DEA) is used to perform efficiency analysis in this paper. Finally, we apply China's case to obtain strategies for reforming real estate investment.     

A fuzzy rough number extended AHP and VIKOR for failure mode and effects analysis under uncertainty

Failure mode and effects analysis (FMEA) is one of the most popular reliability analysis techniques due to its outstanding capabilities in identifying, assessing, and eliminating potential failure modes in a wide range of industrial applications. It provides a comprehensive view for investigating potential failures, causes, and effects in designs, products, and processes. However, traditional FMEA is extensively criticized for its defects in determining the criteria weights, identifying the risk priority of failure modes, and handling the uncertainty during the risk evaluation. To resolve these problems, this study proposes a novel fuzzy rough number extended multi-criteria group decision-making (FR-MCGDM) strategy to determine a more rational rank of failure modes by integrating the fuzzy rough number, AHP (analytic hierarchy process), and VIKOR (Serbian: VIseKriterijumska Optimizacija I Kompromisno Resenje). Above all, a fuzzy rough number is introduced to characterize experts’ judgment, aggregate group risk assessments, and tackle the uncertainty and subjectivity in the risk evaluation. Then a fuzzy rough number enhanced AHP is presented to determine the criteria weights. A fuzzy rough number enhanced VIKOR is proposed to rank the failure modes. A practical case study of the check valve is provided to validate the applicability of the proposed FMEA. Comparative studies demonstrate the efficacy of the proposed FR-MCGDM, with remarkable advantages in handling the uncertainty and subjectivity during failure modes evaluation.     

A fast technique for comparing graph representations with applications to performance evaluation

Finding efficient, effective ways to compare graphs arising from recognition processes with their corresponding ground-truth graphs is an important step toward more rigorous performance evaluation.In this paper, we examine in detail the graph probing paradigm we first put forth in the context of our work on table understanding and later extended to HTML-coded Web pages. We present a formalism showing that graph probing provides a lower bound on the true edit distance between two graphs. From an empirical standpoint, the results of two simulation studies and an experiment using scanned pages show that graph probing correlates well with the latter measure. Moreover, our technique is very fast; graphs with tens or hundreds of thousands of vertices can be compared in mere seconds. Ease of implementation, scalability, and speed of execution make graph probing an attractive alternative for graph comparison.Received: 1 October 2002, Accepted: 15 January 2003, Published online: 6 February 2004Correspondence to: D. Lopresti     

Proposing a new integrated model based on sustainability balanced scorecard (SBSC) and MCDM approaches by using linguistic variables for the performance evaluation of oil producing companies

Using the balanced scorecard approach based on sustainable development parameters is a powerful and useful methodology to evaluate the sustainable performance of organization or company. In this paper, a new approach based on sustainability balanced scorecard (SBSC) and multi criteria decision making (MCDM) approaches is developed for evaluating the performance of oil producing companies in Iran. For reflecting the interdependent relationships among factors influencing the problem under consideration, analytical network process (ANP), a branch of the MCDM techniques, is employed. However, using the ANP method for calculating the preference ratings of alternatives is a time-consuming and bothersome process; therefore, COPRAS (COmplex PRoportional ASsessment) technique is adopted to prioritize the feasible alternatives in terms of linguistic variables. Based on this study, the results demonstrate the effectiveness of the proposed model. The performance evaluation model proposed by using a combination of the MCDM methods and the SBSC approach helps authorities to make an attempt for achieving a competitive advantage.     

基于DEA的城市交通系统协同发展综合评价研究

从城市交通系统的各个子系统内与系统间的投入产出角度出发并结合城市交通可持续发展指标体系建立基于DEA的综合评价模型,给出城市交通系统“协同”和“发展”有效度的评价方法与指标,进而得到城市交通系统协同发展的综合评价指标对其进行综合评价。以某市近6年（2000年～2005年）的城市交通系统协同发展状况作为综合评价的实例进行研究和分析,并从综合评价研究中寻求未来城市交通系统协同发展的对策。     

A fuzzy quality control-decision support system for improving operational reliability of liquid transfer operations in laboratory automation

Quality control in liquid transfer operations can eliminate false assay results due to transfer failures, monitor the stability of laboratory equipment, provide baselines for continuous improvement, and enable automatic recovery actions for a number of fault conditions. This paper describes the work done to add real-time quality control capability in liquid transfer operations on a closed-loop controlled MEMS based liquid transfer device.Real-time data acquisition enables the interpretation of system variables. Using fuzzy logic as a decision support tool, an overall quality metric and failure mode is deduced from these variables for each channel per operation. Detection of ordinary fault conditions like clogged tips or unexpected empty source wells can activate automatic recovery actions without human intervention, leading to 24/7 utilization of an automated laboratory system. Exported quality data can be used for bio-informatics data analysis as well as documentation for regulatory agencies.     

A comparative analysis of operational performance of Cellular Mobile Telephone Service Providers in the Delhi working area using an approach of fuzzy ELECTRE

The purpose of this research paper is to develop a framework to analyze the operational performance of cellular mobile telephone service providers (CMTSP) in the Delhi working area, India. Delhi area has the highest teledensity in India. The present study uses a fuzzy ELECTRE (Elimination and Choice Expressing Reality) approach to compare the performance of cellular mobile telephone service providers. The data for the analysis have been taken from the Telecom Regulatory Authority of India (TRAI), April–June 2015. Most of the data vary from one month to another. Therefore, these small ranges of variation in the data are incorporated in this study using the fuzzy number. Total six major telecom service providers are considered in this analysis. The findings of the study suggest that the performances of Airtel and Reliance communications are in the first rank; Vodafone and Idea are in the second rank; Aircel is in the third rank, and Mahanagar Telephone Nigam Limited (MTNL) is outranked by all the other service providers. The performance is analyzed on the basis of three major parameters consisting six criteria, i.e. network availability (Base Transceiver Station accumulated downtime), connection accessibility (call setup success rate, channel congestion, and traffic channel congestion), and connection retainability (call drop rate and connection with good voice quality). It has been observed that MTNL is outranked due to the highest traffic channel congestion and highest call drop rate. Using this framework, a decision maker can develop a strategy to improve the performance by benchmarking operational parameters. The research analysis is limited to only GSM (Global System for Mobile communication) service providers in the Delhi working area, including Ghaziabad, Noida, Faridabad, and Gurgaon.     

A synthetic method for knowledge management performance evaluation based on triangular fuzzy number and group support systems

The purpose of this paper is to propose a systematic method to solve knowledge management performance evaluation (KMPE) problems. This method includes an integrated evaluation process starting from the measurement to the output of KMPE and combines subjective and objective indicators together. Firstly, we established an index system, involving the process of knowledge management, the organizational knowledge structure, economic benefits and efficiency. And based on this index system, a synthetic evaluation method is presented, using triangular fuzzy number to measure indexes and facilitating the KMPE with a group support system (GSS). To know better of the proposed method, we have an example to illustrate. Finally, the empirical study conducted in this paper indicates that the evaluation method has strong practicability and operability. Besides, the evaluation is enabled by using a group support system: the more objective scoring can be achieved due to synchronic/asynchronous and anonymous participation; Decision-makers improve their efficiency by the clear demonstration analysis results. The systematic method of KMPE based on the index system is able to improve organizations’ efficiency in performance evaluation process.