A hierarchical model of a linguistic variable

In this work a theoretical hierarchical model of dichotomous linguistic variables is presented. The model incorporates certain features of the approximate reasoning approach and others of the Fuzzy Control approach to Fuzzy Linguistic Variables. It allows sharing of the same hierarchical structure between the syntactic definition of a linguistic variable and its semantic definition given by fuzzy sets. This fact makes it easier to build symbolic operations between linguistic terms with a better grounded semantic interpretation. Moreover, the family of fuzzy sets which gives the semantics of each linguistic term constitutes a multiresolution system, and thanks to that any fuzzy set can be represented in terms of the set of linguistic terms. The model can also be considered a general framework for building more interpretable fuzzy linguistic variables with a high capacity of accuracy, which could be used to build more interpretable Fuzzy Rule Based Systems (FRBS).     

Performance evaluation for manufacturing systems under control-limit maintenance policy

Maintenance decision-making in large manufacturing systems is complex as it requires the integration of various information. A control-limit policy is popular in practice, where maintenance is carried out when the degradation state of a machine reaches a threshold value. In this paper, by developing a framework based on discrete-time Markov chain models, we evaluate the system performance under the control-limit policy, in manufacturing systems that consist of multi-state machines and intermediate buffers. An exact analysis is performed for a two-machine-one-buffer system and an approximation method based on system decomposition is developed for multi-stage systems. Both steady-state and transient performance is analyzed. Numerical examples are presented to demonstrate the accuracy of the proposed method and the impact of different parameters (e.g., buffer capacity, uncertainty in the maintenance duration) on the system performance.     

A fuzzy model for managing natural noise in recommender systems

E-commerce customers demand quick and easy access to products in large search spaces according to their needs and preferences. To support and facilitate this process, recommender systems (RS) based on user preferences have recently played a key role. However the elicitation of customers preferences is not always precise either correct, because of external factors such as human errors, uncertainty and vagueness proper of human beings and so on. Such a problem in RS is known as natural noise and can bias customers recommendations. Despite different proposals have been presented to deal with natural noise in RS none of them is able to manage properly the inherent uncertainty and vagueness of customers preferences. Hence, this paper is devoted to a new fuzzy method for managing in a flexible and adaptable way such uncertainty of natural noise in order to improve recommendation accuracy. Eventually a case study is performed to show the improvements produced by this fuzzy method regarding previous proposals.     

Performance evaluation and enhancement of multistage manufacturing systems with rework loops

The phenomena of machine failures, defects, multiple rework loops, etc., results in much difficulty in modeling rework systems, and therefore the performance analysis of such systems has been investigated limitedly in the past. We propose an analytical method for the performance evaluation of rework systems with unreliable machines and finite buffers. To characterize the rework flow in the system, a new 3M1B (three-machine and one-buffer) Markov model is first presented. Unlike previous models, it is capable of representing multiple rework loops, and the rework fraction of each loop is calculated based on the quality of material flow in the system. A decomposition method is then developed for multistage rework systems using the proposed 3M1B model as one of the building blocks. The experimental results demonstrate that the decomposition method provides accurate estimates of performance measures such as throughput and Work-In-Process (WIP). We have applied this method to several problems, such as the determination of the optimal inspection location and the identification of bottleneck machines in rework systems.     

Integrating Fuzzy C-Means and TOPSIS for performance evaluation: An application and comparative analysis

In this paper we introduce a multi-method multiple criteria approach for evaluating the performance of organizations. Performance analysis may include both strategic and operational performance, as well as financial and other less tangible factors. This paper introduces the use of Fuzzy C-Means and TOPSIS for organizational performance evaluation purposes. Using real company data and balanced scorecard accounting and performance dimensions the methodology is applied and evaluated. The predictive abilities of the technique from an organizational performance evaluation approach are evaluated using this data. One of the results from the illustrative application is that economic performance evaluation is not the best predictor of overall viability of some organizations, especially e-commerce based organizations.     

A hybrid comprehensive performance evaluation approach of cutter holder for tunnel boring machine

Cutter holder is a crucial component of tunnel boring machine (TBM), whose performance evaluation and selection needs to consider many factors, which is a challenging Multiple criteria decision-making (MCDM) problem. To enhance the TBM overall construction manifestation, it is fundamental to synthetically evaluate and accurately select the most suitable cutter holder from alternatives according to the engineering requirements and geological conditions. This paper develops a hybrid fuzzy comprehensive evaluation approach for cutter holder. In this approach, the weights of criteria are determined by Fuzzy Analytic Hierarchy Process (FAHP), and the max-min linear normalization is employed to integrate the information of qualitative and quantitative indicators of alternatives. Finally, the ranking and further comparison are achieved in the form of radar chart. A case study of cutter holder selection from six alternatives is carried out to validate the proposed approach. It shows that the proposed approach is effective, reasonable, complete and easy to operate, which can be promoted to the evaluation and selection of non-standard crucial components for large mechanical engineering equipment.     

Fuzzy complex numbers and their application for classifiers performance evaluation

There are a variety of measures to describe classification performance with respect to different criteria and they are often represented by numerical values. Psychologists have commented that human beings can only reasonably manage to process seven or-so items of information at any one time. Hence, selecting the best classifier amongst a number of alternatives whose performances are represented by similar numerical values is a difficult problem faced by end users. To alleviate such difficulty, this paper presents a new method of linguistic evaluation of classifiers performance. In particular, an innovative notion of fuzzy complex numbers (FCNs) is developed in an effort to represent and aggregate different evaluation measures conjunctively without necessarily integrating them. Such an approach well maintains the underlying semantics of different evaluation measures, thereby ensuring that the resulting ranking scores are readily interpretable and the inference easily explainable. The utility and applicability of this research are illustrated by means of an experiment which evaluates the performance of 16 classifiers using different benchmark datasets. The effectiveness of the proposed approach is compared to conventional statistical approach. Experimental results show that the FCN-based performance evaluation provides an intuitively reliable and consistent means in assisting end users to make informed choices of available classifiers.     

Moments of accumulated reward and completion time in Markovian models with application to unreliable manufacturing systems

Performance evaluation models are used by companies to design, adapt, manage and control their production systems. In the literature, most of the effort has been dedicated to the development of efficient methodologies to estimate the first moment performance measures of production systems, such as the expected production rate, the buffer levels and the mean completion time. However, there is industrial evidence that the higher moments of the production output may drastically impact on the capability of managing the system operations, causing the observed system performance to be highly different from what expected. This paper presents a methodology to analyze the cumulated output and the lot completion time moments of Markovian reward models. Both the discrete and continuous time cases are considered. The technique is applied to unreliable manufacturing systems characterized by general Markovian structures. Numerical results show how the theory developed in this paper can be applied to analyse the dependency of the output variability and the service level on the system parameters. Moreover, they highlight previously uninvestigated features of the system behavior that are useful while operating the system in practical settings.     

Analyzing the performance of a cluster-based architecture for immersive visualization systems

Cluster computing has become an essential issue for designing immersive visualization systems. This paradigm employs scalable clusters of commodity computers with much lower costs than would be possible with the high-end, shared memory computers that have been traditionally used for virtual reality purposes. This change in the design of virtual reality systems has caused some development environments oriented toward shared memory computing to require modifications to their internal architectures in order to support cluster computing. This is the case of VR Juggler, which is considered one of the most important virtual reality application development frameworks based on open source code.     

Human reliability analysis and optimization of manufacturing systems through Bayesian networks and human factors experiments: A case study in a flexible intermediate bulk container manufacturing plant

Human reliability analysis (HRA) and optimization in manufacturing systems are effective to reduce system failure. The purpose of this study is to examine the HRA and optimization through a Bayesian network (BN) model and human factors experiments (HFEs). This study was applied to a flexible intermediate bulk container manufacturing plant. The human physiological and psychological factors consisting of personal abilities of flexibility, coordination, memory, and attention were regarded as the only performance shaping factors in this study. With the BN model, the relationship between human factors and human errors was described qualitatively and the impact of the human factor on system failures was judged quantitatively. Then the workers’ abilities training with HFEs based on the fault diagnosis results was carried out. The total numbers of errors have been decreased by 69.06% and the system failure rate has been reduced significantly after training.     

Key factors for operational performance in manufacturing systems: Conceptual model,systematic literature review and implications

PurposeThe performance of discrete items manufacturing systems (MS) is a primary concern of industrial firms. However, the understanding of the interrelations between performance and its key factors requires further advancements. Thus, several questions remain unanswered in the Operations and Production Management (OPM) field to understand and manage the relationship between these key factors. To address these challenges, this paper conceptualizes and examines the relevant antecedents and essential elements for the design and optimization of competitive MS.Design/methodology/approachFirst, drawing on the consolidated OPM literature, a novel conceptual model was developed incorporating the conceptual relationships essential to MS performance. Second, we conducted a systematic literature review based on the PRISMA protocol to analyze and validate the proposed conceptual model and to indicate the field’s current knowledge gaps and future research directions.FindingsFindings validated the proposed conceptual model by establishing the complex causal interrelations among key factors that influence discrete MS performance. Moreover, we found that the operational performance of discrete MS is multidimensional and directly dependent on the variables and mechanisms associated with the production flow. Findings also demonstrated that the degree of importance of the antecedents of MS performance vary and are temporally interrelated. Lastly, the paper advances the understanding of MS by revealing the predominance of quantitative approaches (e.g., discrete events simulation and closed mathematical models) in the literature as well as an emphasis on describing these approaches rather than characterizing MS appropriately.Research and Practical implicationsThis paper extends our knowledge on the operational performance challenges in discrete MS by proposing a visual, direct, and intuitive conceptual model that enables firms to better comprehend these complex challenges. This research also answers ongoing calls for investigations of the antecedents and elements of competitive MS design and optimization. Our findings show that decision-making in discrete MS is established temporally based on strategic, operational, and control definitions, influencing firms’ operational performance. Finally, since it draws on seminal OPM literature specializing in MS, this study informs scholars, industrial managers, and aid decision-making about discrete MS.Originality/valueThe first original aspect of this study lies in bridging the gaps identified in the OPM literature by providing a robust conceptual framework that highlights the key factors of operational performance in discrete MS. Its second original aspect is that it adopts different information sources in an independent and complementary way to achieve greater generalizability and robustness of the contributions.     

卫星系统综合效能分析研究

从卫星系统应用角度提出了基于信息支援保障的卫星系统综合效能评估指标体系，并应用层次分析法建立了效能分析的多属性综合评价模型与方法。为量化指标值，提出了基于领域专家知识的定性与定量相结合的量化指标方法。定量分析表明了多属性综合评价方法对复杂系统效能评估是有效的。     

A comparative study of ranking methods, similarity measures and uncertainty measures for interval type-2 fuzzy sets

Ranking methods, similarity measures and uncertainty measures are very important concepts for interval type-2 fuzzy sets (IT2 FSs). So far, there is only one ranking method for such sets, whereas there are many similarity and uncertainty measures. A new ranking method and a new similarity measure for IT2 FSs are proposed in this paper. All these ranking methods, similarity measures and uncertainty measures are compared based on real survey data and then the most suitable ranking method, similarity measure and uncertainty measure that can be used in the computing with words paradigm are suggested. The results are useful in understanding the uncertainties associated with linguistic terms and hence how to use them effectively in survey design and linguistic information processing.     

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.     

MBNQA criteria in education: assigning weights from a Malaysian perspective and proposition for an alternative evaluation scheme

In order to improve quality and productivity among American companies, the Malcolm Baldrige National Quality Award (MBNQA) was launched by the National Institute of Standards and Technology (NIST) under the US Department of Commerce in 1987. Over the years, the award has proved to be effective in improving companies' market share, customer satisfaction, employee morale, and also profitability. MBNQA has been a ‘role model’ in developing a national quality award for many other countries in the world. Furthermore, for organizational self‐assessment, the criteria framework of MBNQA has been in use throughout the world. Presently, the award is offered in three categories: Business, Education, and Health care. The present paper is concerned with MBNQA in Education. NIST has developed a comprehensive set of criteria to be fulfilled in order to be eligible to win the award. However, in the existing literature, it is not clear how the weights are assigned to the criteria and subcriteria. The present paper uses the analytic hierarchy process (AHP) to reassign criteria weights from a Malaysian perspective. Furthermore, the paper points out the fallacy of the present evaluation scheme and proposes an alternative one based upon the absolute measurement procedure of AHP. The modified scheme is expected to enhance the fairness of the evaluation of the award aspirants. The paper also shares the experiences gathered in data collection using AHP.     

钢管生产企业合同可行性综合评审及管理DSS的研究与开发

介绍了钢管生产企业质量管理中合同可行性综合评审及管理决策支持系统设计与开发的基本思路、体系结构、关键技术及实现方案,该系统具有对一类钢管生产企业合同可行性分析与管理的决策支持作用,且简单实用、可扩展性强。     

Development of a decision support system for supplier evaluation and order allocation

This study aims to develop models and generate a decision support system (DSS) for the improvement of supplier evaluation and order allocation decisions in a supply chain. Supplier evaluation and order allocation are complex, multi criteria decisions. Initially, an analytic hierarchy process (AHP) model is developed for qualitative and quantitative evaluation of suppliers. Based on these evaluations, a goal programming (GP) model is developed for order allocation among suppliers. The models are integrated into a DSS that provides a dynamic, flexible and fast decision making environment. The DSS environment is tested at the purchasing department of a manufacturer and feedbacks are obtained.     

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.