Maintenance policy selection in manufacturing firms using the fuzzy MCDM approach

In manufacturing firms, there is a critical need for proper maintenance of manufacturing facilities. The maintenance process enhances customer satisfaction and reliability of the products, and increases the profit of the manufacturer. Therefore, a proper maintenance policy selection is a critical issue for manufacturers, as an inefficient maintenance policy affects not only the direct cost of the firm but also the other aspects. In the present study, maintenance policy selection at the level of the firm rather than the equipment level is shown, and for selection various criteria have been identified. The presented work not only provides the best alternatives but also provides an alternative ranking, which facilitates decision-makers in choosing alternatives according to their constraints. These selection criteria are different in nature, as some give a crisp value, whereas others are defined in linguistic terms. To select the appropriate maintenance policy, a distance-based fuzzy multicriteria decision-making (MCDM) approach has been employed. The proposed method provides the means for integrating the economic figure of merit with the strategic performance variables. The MCDM approach is efficient in incorporating data, in the form of linguistic variables, triangular fuzzy numbers, and crisp numbers, into the evaluation process of maintenance policy alternatives. A comprehensive example illustrates the application of the distance-based fuzzy MCDM approach.     

A hierarchical fuzzy TOPSIS approach to assess improvement areas when implementing green supply chain initiatives

Implementing green supply chain management (GSCM) initiatives can generate more business opportunities for firms. It also requires changes in firms’ operational capabilities and resources that may have an adverse effect on firms’ operations performance. In order to achieve sustainable economic and environmental performance, it is essential for companies to evaluate different green initiatives and assess improvement areas when implementing green initiatives. This study proposes a fuzzy hierarchical TOPSIS approach to support such an assessment. It enables decision makers to better understand the complete evaluation process and provide a more accurate, effective and systematic decision support tool. An illustrative case is presented to help researchers and practitioners understand the importance of developing an appropriate organisation strategy in implementing green practices.     

Lending decisions under uncertainty: a DEA approach

Credit markets have experienced phenomenal growth in the last years, and there is no evidence to expect that this trend will invert in the next decade. Given that, virtually every segment of the industry is called to improve its skills in credit risk management with the threefold aim of managing concentration risk, meeting regulatory requirements and enhancing revenues. Clearly, for financial institutions, exposure to credit risk continues to be the leading source of problems and the challenging issue of managing credit risk is considered a crucial topic. This paper demonstrates the use of a stochastic DEA model for credit scoring which is one of the prevailing analytical technique to evaluate credit risk. Although the principles contained in this paper are most clearly applicable to the business of lending, they can be applied to all activities where credit risk is present.     

Assessment approach to stage of lean transformation cycle based on fuzzy nearness degree and TOPSIS

This paper presents an assessment method to measure the lean transformation (LT) stage of an LT enterprise. Although there are many assessment tools to measure the various aspects of lean practices in enterprises, there is none to measure the stage of LT using the enterprise transformation characteristics from enterprise level. In this paper, the characteristic metrics and characteristic model of LT cycle were extracted from the basic capacity, process power and transformation results. Then, an assessment approach based on fuzzy nearness degree and TOPSIS is proposed to determine the stage of LT. Finally, an example is shown to highlight the procedure of the proposed method. This paper shows that the proposed model is very well suited as an assessment tool for enterprises in the manufacturing industry and other industries to evaluate the LT stage.     

Risk analysis of human error in interaction design by using a hybrid approach based on FMEA,SHERPA, and fuzzy TOPSIS

User experience is the focus of interaction design, and designing for errors is crucial for improving user experience. One method of designing for errors is to identify human errors and then initiate corrective actions on high-risk errors to reduce their adverse effects. In this study, we proposed a hybrid approach for risk analysis of human error concerning user experience of interactive systems. In this approach, systematic human error reduction and prediction approach (SHERPA) is first adopted to identify human error concerning user experience. Subsequently, failure mode and effect analysis (FMEA) is used to analyze the risk factors of the error, including occurrence, severity, and detection. Fuzzy technique for order preference by similarity to ideal solution (TOPSIS) is then used to calculate the risk priority number to rank the errors. Finally, corrective actions for high-risk errors are recommended. An in-vehicle information system was used to demonstrate the proposed approach. The results indicated that the proposed approach can effectively analyze the risk of human error concerning user experience and be used as a universal reliability approach for improving user experience in interaction design.     

A novel approach for supplier selection based on the Kano model and fuzzy MCDM

This paper proposes a three-phase approach for supplier selection based on the Kano model and fuzzy Multi Criteria Decision-Making. Since the supplier selection problem involves different criteria, quality attributes have been assumed to denote the importance weight of the criteria for supplier selection. Furthermore, to consider the inherent vagueness of human thought, a fuzzy logic has been utilised. Initially, the importance weight of the criteria has been calculated using a fuzzy Kano questionnaire and fuzzy analytic hierarchy process. In the second phase, the Fuzzy TOPSIS technique has been used to screen out in capable suppliers. Finally, in the third phase, the filtered suppliers which are qualified, once again will be evaluated by the same approach for the final ranking. The proposed approach has also been examined in a case study.     

A fuzzy group decision making approach to new product concept screening at the fuzzy front end

Efficient new product concept screening plays an important role in new product development (NPD). Due to the inherent complexity, incompleteness and ambiguity in NPD, fuzzy approaches have been widely developed and used for new product concept screening. Because of the group behaviours (diversity of gate-team members and senior management’s preference), the new product concept screening is typical group decision-making problem. Moreover, it is usually more efficient to conduct the screening evaluation in a gate-team and make a screening decision outside the gate-team by senior management. Therefore, it is necessary to provide a result with a high degree of flexibility and credibility so as to better support the senior management to make a screening decision in NPD. Existing fuzzy approaches neglect these two issues. Towards this end, a three-stage fuzzy group decision-making approach is proposed to assist the senior management perform new product concept screening. A case study is borrowed from the literature to illustrate our proposed approach. Our approach is compared with two existing techniques from theoretical and empirical perspectives. The results show that the proposed approach can capture the group behaviours and present a relatively realistic and informative result which gives the senior management a high degree of flexibility and credibility in new product concept screening.     

A supplier performance evaluation framework using single and bi-objective DEA efficiency modelling approach: individual and cross-efficiency perspective

In view of complexities associated with supplier performance evaluation based on traditional business criterions (such as costs, quality levels, and delivery timelines) and emerging criterions (such as those related to environmental sustainability), we in this research evolve two different supplier efficiency measurement models that unify such criterions possessing characteristics of both desirable and undesirable outputs. The first model is a single-objective DEA efficiency assessment model wherein both types of outputs are integrated into a single composite efficiency measure. Using data from suppliers of Hyundai Steel Company, we determine composite efficiencies of each of these suppliers thus ranking them in terms of an overall efficiency score that would be useful as far as the first-cut supplier discrimination is concerned. However, due to the relative inability of evolved single-objective efficiency model to perform trade-offs amongst desirable and undesirable outputs and, owing to unidimensionality aspects, we evolve a goal programming based bi-objective efficiency model wherein trade-offs can be performed between both conventional and emerging dimensions criterions leading to different supplier evaluations for varied scenarios. We also integrate our evolved models with the cross-efficiency view of efficiency determination in order to enable the decision-makers to achieve peer-to-peer evaluation and maximum discrimination amongst suppliers.     

Determining design requirements in QFD using fuzzy mixed-integer goal programming: application of a decision support system

In product development, the identification of critical design requirements (DRs) is key to satisfying customer needs because it helps produce more successful products in a shorter time. Quality function deployment (QFD) is a tool used in product development to systematically determine the DRs so as to attain higher customer satisfaction. In the QFD process, the simultaneous optimisation of more than one conflicting objective is generally required. However, it is very difficult for decision makers to determine the goal value of each objective in imprecise and uncertain environments. In order to overcome this problem, the present study proposes a fuzzy mixed-integer goal programming model that determines a combination of optimal DR values. Different from the existing fuzzy goal programming models, the values of the DRs in the proposed model are taken as discrete. Finally, a new Decision Support System is developed. The new system integrates QFD and mathematical programming, enabling the design team to effectively compare product design alternatives and make product development easier and faster. The proposed methodology is illustrated using a real-world application in the Turkish white goods industry.     

A new approach for the selection of advanced manufacturing technologies: DEA with double frontiers

Selection of advanced manufacturing technologies (AMTs) is an important yet complex decision which requires careful consideration of various performance criteria. This paper proposes the use of data envelopment analysis (DEA) with double frontiers for the selection of AMTs, which considers not only the best (optimistic), but also the worst (pessimistic) relative efficiencies of each AMT. Compared with the traditional DEA, the DEA approach with double frontiers can identify the best AMT correctly and easily without the need to impose any weight restriction or the need to calculate the cross-efficiency matrix, which requires a large number of computations and may also result in inconsistent conclusions by aggressive and benevolent cross-efficiency models. Four numerical examples are examined using the DEA approach with double frontiers to illustrate its simplicity and usefulness in AMT selection and justification.     

基于Web技术的IP网络性能测量系统的研究与实现

在分析了网络性能测量系统的功能和层次结构的基础上,设计并实现了一个利用Web技术测量IP网络性能的系统。系统基于分布式B／S架构,有效地简化了部署并增强了移植性,降低了开发和维护的成本。在模拟网络环境中的试验结果显示,系统测量结果准确可靠,测量速度快,代价低。     

Selecting suppliers using a new fuzzy multiple criteria decision model: the fuzzy balancing and ranking method

In this paper, we present a fuzzy multiple criteria decision making (FMCDM) model known as fuzzy balancing and ranking. In contrast to other MCDM models, our proposed model does not require the weights of decision making criteria. First, we appraise the performance of alternatives against criteria via linguistic variables which are expressed as triangular fuzzy numbers. The foregoing model obtains the alternative rankings through a four-stage process. Second, an outranking matrix is derived indicating that the frequency with which one alternative is superior to all other alternatives based on each criterion. Third, the outranking matrix is triangularised to obtain an implicit pre-ordering or provisional order of alternatives. Fourth, the provisional order of alternatives is subjected to various screening and balancing operations that require sequential application of a balancing principle to the so-called advantages–disadvantages table that combines the criteria with the pair-wise comparisons of alternatives. Additionally, to demonstrate the procedural implementation of the proposed model and its effectiveness, we apply it on a case study regarding the problem of supplier selection.     

An integrated fuzzy approach for the selection of outsourcing manufacturing partners in pharmaceutical R&D

The selection of the outsourcing manufacturing partners (OMPs) is an important issue for research and development (R&D) in the pharmaceutical industry. The selection process considers several main criteria, as well as a few sub-criteria for each main criterion. Such problems can be formulated as hierarchical structures and can usually be resolved with multi-criteria decision-making approaches. This paper presents an integrated fuzzy approach for selecting a suitable OMP in pharmaceutical R&D. In the integrated approach, fuzzy concepts are used for decision-makers’ subjective judgments to reflect the vague nature of the selection process. Fuzzy AHP and fuzzy TOPSIS are included in the integrated approach. Fuzzy AHP is used to determine the fuzzy weights of criteria and sub-criteria because it can effectively determine various criteria's weights in a hierarchical structure. Fuzzy TOPSIS aims to find the OMP with the best performance with respect to the sub-criteria. We exemplify the integrated approach using a numerical application to demonstrate its feasibility.     

Lung computed axial tomography image segmentation using possibilistic fuzzy C-means approach for computer aided diagnosis system

Soft computing is an associate rising field that plays a crucial half in the area of engineering and science. One of the most significant applications of soft computing is image segmentation. It focuses on an exploiting tolerance of imprecision and uncertainty. Segmentation supported soft computing remains a difficult task within the medical field. Medical images are habitually used in the segmentation process to extract the meaningful portions and to know and clarify the condition of the particular patient. In this article, we implement an efficient possibilistic fuzzy C-means (PFCM) approach to segment the lung portion in the computed tomography (CT) image and the result shows that it improves the segmentation accuracy upto 98.5012% and results are compared with existing segmenting approaches like fuzzy possibilistic C-means method, fuzzy bitplane method and so forth. Also, the PFCM approach increases the diagnostic accuracy of the computer aided diagnosis system using CT images. The radiologist may utilize this computer aided diagnosis system results as a second opinion of their diagnosed results.     

A new hybrid MCDM approach for RPN evaluation for a medical device prototype

The Failure Mode and Effect Analysis (FMEA) is a useful instrument born in the aerospace industry and widely used to improve a process or product's efficiency. Over the years, this instrument has been adopted in increasingly different contexts, such as HealthCare. This paper proposes an approach aimed at improving the defects typical of the classic FMEA in the design phase, that is, in a scenario full of uncertainties and with little information available, using a new hybrid Multiple-criteria decision-making (MCDM) method in order to obtain a priority index more performant than Risk Priority Number (RPN). In the proposed method, the three assessment criteria have a different weighting in the index's final computation, differently from the classical RPN. These weights are obtained with a scientific technique, thus avoiding that excessive subjectivity influences the final result. A more efficient priority index is obtained through a new hybrid approach that solves some classical RPN gaps. A case study concerning an endoscope Ear Nose Throat Entropy (ENT) prototype is examined to illustrate the proposed method. FMEA analysis in HealthCare is increasingly used for its flexibility and reliability. This study focuses on using new techniques to eliminate certain defects or exploit some qualities better. The use of a robust and elastic innovative MCDM method to calculate a new priority index and a scientific technique to obtain the weight of the selection are the interesting insights proposed in this paper.     

A SCOR-based model for supply chain performance measurement: application in the footwear industry

Supply Chain Operations Reference (SCOR) is a widely employed model for SC performance assessment, regardless its generic nature. This article presents a SCOR-based model for performance measurement in supply chains (SC) and apply it in the context of Brazilian footwear industry. The model has two dimensions: SCOR processes (source, make, deliver and return) and performance standards adapted from original SCOR (cost, quality, delivery and flexibility). This structure delivers a 4 × 4 matrix, with each component assessed under analytical hierarchy process. Using focus groups, SC’s experts weighted each component of the matrix regarding their relevance. Thereafter, SC’s managers indicated respective results. The SC’s overall performance was obtained by adding the performance of all indicators. The model application embraced one focal footwear manufacturer, four suppliers, three distribution channels and a return channel, with 85 indicators assessed. The achieved performance for the whole SC is 75.29%.The main gaps were found in deliver process (12.78 percentual points of difference between relevance and achieved proportions) and in flexibility performance (9.82). Further application is recommended in order to find consolidated results.     

Optimal configuration selection for reconfigurable manufacturing system using NSGA II and TOPSIS

Reconfigurable Manufacturing Systems provide the functionality and capacity needed, when needed. The Reconfigurable Machine Tool (RMT) plays a pivotal role in the fulfilment of this objective through their modular structure consisting of basic and auxiliary modules along with the open architecture software. In the present work, a novel approach based on the module interactions and machine capability is proposed to measure the machine reconfigurability and operational capability of an RMT. The developed performance measures along with cost are considered as the multiple objectives for the optimal machine assignment for a single part flow line allowing paralleling of similar machines. The multi-objective optimisation problem in hand is targeted in two phases. In the first phase, non-dominated sorting genetic algorithm II is applied to obtain the non-dominated solutions. In the subsequent stage, a multiple attribute decision-making approach is employed to rank the pareto frontiers. The proposed solutions are ranked based on Shannon entropy weight and Technique for Order Preference by Similarity to Ideal Solution method. The study reveals that the developed performance measures along with the hybrid approach have a great potential in handling the RMS optimisation and cost–benefit issues.     

Agility index measurement using multi-grade fuzzy approach integrated in a 20 criteria agile model

This paper reports a research carried out to assess the agility level of an organisation using a multi-grade fuzzy approach. Agility refers to the capability of an organisation to respond quickly in accordance with the dynamic demands of the customers. During this research, an agility index measurement model containing 20 criteria incorporated with the multi-grade fuzzy approach was designed. Subsequently, the data gathered from a manufacturing company was substituted in this model and the proposals for enhancing the agility level of this company were derived. The usage of the model contributed in this paper will indicate the actions required to enhance an organisation's agility level. This process will accelerate the absorption of agility characteristics in modern organisations.     

New methodology for hazardous waste classification using fuzzy set theory Part II. Intelligent decision support system

In part 1 of this paper, factors that influence hazards and eco/toxicity in composite hazardous wastes were described. In part 2, a computer-aided decision support tool based on fuzzy set theory is proposed to support the classification of composite wastes. Given the chemical properties, the nature of microorganisms that may be present, the behaviour of chemicals in humans and ecosystems, and the quantities of wastes, the computer-aided tool automatically classifies the waste as benign, partially hazardous, hazardous or highly hazardous. The functionality of the computer-aided decision tool is demonstrated through nine worked examples and the results are discussed in detail.     

Provident decision making by considering dynamic and fuzzy environment for FMS evaluation

When evaluating complexity, cost and risk increase, it is difficult to make a proper decision. In such situations it is necessary to develop a model which simulates a decision maker's mind and consider both a dynamic and a fuzzy environment. In this study future oriented indices are presented which enable us to consider the effect of future changes in the index value during the decision making process. These future oriented indices are named provident indices. Also in this study to effectively integrate these multiple criteria into the decision making process, based on the analysis of the decision situation in such assessments, a suitable concept is selected. This method is based on the preference ranking organisation method for enrichment evaluations (PROMETHEE) which brings together flexibility and simplicity for the user and is therefore chosen for the enhancement towards the evaluation of fuzzy data on preferences, scores and weights. The model developed to investigate these impacts cannot perfectly reproduce all the events of the real system, but it can consider a fair number of elements of variability, which should be identified and analysed by considering present conditions and prediction about criteria values in future periods. Such a model may provide solutions with a high degree of robustness and reliability, comparable with those obtained by direct experimentation, but with a low computational cost. The uniqueness of this paper lies in two important areas: first, the incorporation of variability fuzzy and provident measures in the performance of alternatives into the decision making process; and second, is in the application of fuzzy PROMETHEE that provides the decision maker with effective alternative choices by identifying significant differences among alternatives and appropriate choices through considered future periods, and presents graphic aids for better interpretation of results. A comprehensive numerical example of a flexible manufacturing system (FMS) is provided to illustrate the results of the analysis. In a real-world manufacturing environment, the dynamics of an FMS and its stochastic characteristics require a specific approach for evaluation. This paper specifically focuses on FMSs due to the complexities involved in their proper evaluation that include factors such as high operational and managerial expertise in system implementation phases, high costs and risks. Due to these, evaluation, justification, and implementation of an FMS have been areas of major concern and importance for practitioners and researchers. In this case, various strategic, economic and operational criteria that envelop quantitative, qualitative, tangible, and intangible factors are considered.