Fuzzy Multi-criteria Approach Research Articles

A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach

Sustainable supply chain management has received much attention from practitioners and scholars over the past decade owing to the significant attention given by consumers, profit and not-for-profit organizations, local communities, legislation and regulation to environmental, social and corporate responsibility. Sustainable supply chain initiatives like supplier environmental and social collaboration can play a significant role in achieving the “triple bottom line” of social, environmental, and economic benefits. Supplier selection plays an important role in the management of a supply chain. Traditionally, organizations consider criteria such as price, quality, flexibility, etc. when evaluating supplier performance. While the articles on the selection and evaluation of suppliers are abundant, those that consider sustainability issues are rather limited. This paper explores sustainable supply chain initiatives and examines the problem of identifying an effective model based on the Triple Bottom Line (TBL) approach (economic, environmental, and social aspects) for supplier selection operations in supply chains by presenting a fuzzy multi criteria approach. We use triangular fuzzy numbers to express linguistic values of experts' subjective preferences. Qualitative performance evaluation is performed by using fuzzy numbers for finding criteria weights and then fuzzy TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is proposed for finding the ranking of suppliers. The proposed approach is illustrated by an example.

Optimal Software Release Using Time and Cost Benefits via Fuzzy Multi-Criteria and Fault Tolerance

As we know every software development process is pretty large and consists of different modules. This raises the idea of prioritizing different software modules so that important modules can be tested by preference. In the software testing process, it is not possible to test each and every module regressively, which is due to time and cost constraints. To deal with these constraints, this paper proposes an approach that is based on the fuzzy multi-criteria approach for prioritizing several software modules and calculates optimal time and cost for software testing by using fuzzy logic and the fault tolerance approach.

Integrated Software Quality Evaluation: A Fuzzy Multi-Criteria Approach

Software measurement is a key factor in managing, controlling, and improving the software development processes. Software quality is one of the most important factors for assessing the global competitive position of any software company. Thus the quantification of quality parameters and integrating them into quality models is very essential. Software quality criteria are not very easily measured and quantified. Many attempts have been made to exactly quantify the software quality parameters using various models such as ISO/IEC 9126 Quality Model, Boehm's Model, McCall's model, etc. In this paper an attempt has been made to provide a tool for precisely quantifying software quality factors with the help of quality factors stated in ISO/IEC 9126 model. Due to the unpredictable nature of the software quality attributes, the fuzzy multi criteria approach has been used to evolve the quality of the software.

A Fuzzy Multi-criteria Outranking Approach in Support of Business Angels’ Decision-Analysis Process for the Assessment of Companies as Investment Opportunities

In this paper, a multi-criteria method is presented for the evaluation and ranking of a selected set of companies as investment opportunities in aid of the relevant decision-analysis process of business angels. The multi-criteria methodology is based on the PROMETHEE method, which is further extended to deal with fuzzy input data in order to address the data imprecision and uncertainty of the decision process. The paper concludes by suggesting a web-based system implementation of the proposed methodology, which will significantly enhance modern business angels networks.

A study on collaborative recommender system using fuzzy-multicriteria approaches

In collaborative recommender systems, the overall ratings on items do not provide more detail about the reason behind the user's preferences. The multicriteria ratings give details about the user's preferences in multiple aspects and provide an opportunity to compute accurate recommendations. The user ratings collected by these systems are usually subjective, imprecise and vague, because it is based on user's perceptions and opinions. Fuzzy sets are an appropriate paradigm to handle the uncertainty and fuzziness of human behaviour. Because of these reasons, we propose a collaborative recommendation approach that uses the fuzzy linguistic approach to represent multicriteria user-item preference ratings, then finds similarities using fuzzy user-based and fuzzy item-based similarity measures and computes recommendations using fuzzy aggregation-based approach. The proposed approach's performance is evaluated empirically against traditional user-based and item-based recommendation algorithms using a music recommender system developed for this research. From the evaluation results, it is observed that the proposed approach shows improvement in recommendations than the traditional algorithms.

Manufacturing Outsourcing Decision-making based on Screening Core Activities and Fuzzy Multi-criteria Approach

Manufacturing Outsourcing Decision-making based on Screening Core Activities and Fuzzy Multi-criteria Approach

A fuzzy multicriteria approach for evaluating environmental performance of suppliers

In this paper, we present a fuzzy multicriteria approach for evaluating environmental performance of suppliers. The proposed approach consists of three steps. The first step involves identification of criteria for assessing environmental performance of suppliers. In step 2, the experts rate the selected criteria and the various alternatives (suppliers) against each of the criteria. Linguistic assessments are used to rate the criteria and the alternatives. These linguistic ratings are then combined through fuzzy TOPSIS to generate an overall performance score for each alternative. The alternative with the highest score is chosen as the one with highest environmental performance. The advantage of using fuzzy TOPSIS is that it distinguishes between Benefit (the more the better) and the Cost (the less the better) category criteria and selects solutions that are close to the positive ideal solutions and far from negative ideal solutions. In step 3, sensitivity analysis is conducted to evaluate the influence of criteria weights on the environmental performance evaluation of suppliers. The strength of the proposed approach is its practical applicability and ability to provide solution under partial or lack of quantitative information. A numerical application is provided to demonstrate the proposed approach.

A fuzzy multi-criteria decision analysis approach for the management of petroleum-contaminated sites

This paper presents an effective Fuzzy Multi-Criteria Decision Analysis (FMCDA) approach for contaminated site management. The development was based on: selecting eight criteria for remedial alternative evaluation and determining the weight of criteria importance under uncertainty; addressing various uncertainty issues in remedial alternative assessment process using a fuzzy-set approach based on the questionnaire survey results; evaluating and ranking remedial alternatives by establishing a fuzzy evaluation matrix of remediation alternatives through comprehensively considering different stakeholder opinions and uncertainties within a general decision analysis framework. A case study on the remedial alternative selection for a contaminated site in western Canada is conducted to illustrate the efficiency and applicability of the developed approach.

An integrated fuzzy multi-criteria approach for the performance evaluation of multiple manufacturing plants

The performance evaluation is regarded as a multiple criteria decision making (MCDM) problem and has a significant impact on the operations of the enterprise. This paper develops an integrated MCDM approach that combines the voting method and the fuzzy TOPSIS (technique for order preference by similarity to ideal solution) method to evaluate the performance of multiple manufacturing plants in a fuzzy environment. Fuzzy TOPSIS helps decision-makers carry out analysis and comparisons in ranking their preference of the alternatives with vague or imprecise data. Since the evaluation result is often greatly affected by the weights used in the evaluation process, the voting method is used in this study to determine the appropriate criteria weights. A case study demonstrating the applicability of the proposed model is presented. The case company is the world’s largest manufacturer of power supplies. It has three primary manufacturing bases located in Wujiang, Dongguan, and Tianjin, China. The proposed approach is used to evaluate the performance of the company’s five manufacturing plants in Wujiang, which produce switch power, telecom power, DC/DC converters, uninterruptible power systems (UPS) and AC/DC adapters.

Environmental performance evaluation of suppliers: A hybrid fuzzy multi-criteria decision approach

Increasing environmental problems enforce companies to be more environmental responsible. A company’s environmental performance is not only related to the company’s inner environmental efforts, but also it is affected by the suppliers’ environmental performance and image. As a stakeholder of the company, a supplier with bad environmental image affects badly the company’s environmental image. Also, raw materials and semi-finished products supplied from out of the company recourses- suppliers- affects the products environmental features such as life cycle, re-usability, re-manufacturability, hazardous substances, etc. Considering these direct and indirect effects, managers should also consider environmental performances of their suppliers in their supplier evaluation process. In this paper, a methodology for the evaluation of suppliers’ environmental performances is proposed. In this methodology, a hybrid Fuzzy-Analytic Network Process and Fuzzy-Preference Ranking Organization METHod for Enrichment Evaluations approach is utilized. Additionally, a numerical example is given to foster the better understanding of the methodology and the obtained results are analyzed with sensitivity analyses.

Notice of Violation of IEEE Publication Principles: A Benders Decomposition and Fuzzy Multicriteria Approach for Distribution Networks Remuneration Considering DG

In this paper, the remuneration of fixed costs of distribution networks with distributed generation is evaluated by means of an efficient planning strategy that includes the concept of fuzzy robustness of a given solution plan. As a key contribution, it is included the possibility of choice among different conductor sizes in order to assess the deep costs by overcapacity introduced by distributed generation. A single-stage multicriteria nonlinear problem is stated using three different criteria: investment cost in lines and transformers or distributed generators, power losses cost and unserved energy cost. Pareto or efficient plans are identified using the epsiv-Constraint/Weighting method and solved by Benders decomposition algorithm. Uncertainty associated to load demand and power injections of distributed resources are integrated using a fuzzy power flow in order to obtain the robustness indexes of each Pareto solution. The annualized fixed charge rate (AFCR) associated to new and existing distribution lines and transformers or distributed generators is assessed through a with-without analysis permitting to compute the annual avoided charge rate (AACR) in order to send incentives or charges to distributed generation promoters by the avoided or added investment costs on the system. The model is programmed in GAMS mathematical modeling language. The effectiveness of the proposal is demonstrated through a real 201-node distribution network.

Evaluating centralized return centers in a reverse logistics network: An integrated fuzzy multi-criteria decision approach

In this paper, the centralized return centers location evaluation problem in a reverse logistics network is investigated. This problem is solved via an integrated analytic network process- fuzzy technique for order preference by similarity to ideal solution approach. Analytic network process allows us to evaluate criteria preferences while considering interdependence between them. On the other hand, technique for order preference by similarity to ideal solution decreases the number of computational steps compared to simple analytic network process evaluation. An important characteristic of the centralized return centers location evaluation problem, vagueness, is adapted to the methodology via the usage of fuzzy numbers in the technique for order preference by similarity to ideal solution approach. Finally, a numerical example is given to demonstrate the usefulness of the methodology. The results indicate that, this integrated multi-criteria decision making methodology is suitable for the decision making problems that needs considering multiple criteria conflicting each other. Also, by using this methodology, the interdependences between the criteria may be considered for these kinds of problems in a flexible and systematic manner.

Evaluation of software development projects using a fuzzy multi-criteria decision approach

Software development is an inherently uncertain activity. To deal with the uncertainty and vagueness from humans’ subjective perception and experience in decision process, this paper presents an evaluation model based on the fuzzy multi-criteria decision-making (MCDM) method for measuring the performance of software development projects. In an MCDM problem, a decision maker (DM) has to choose the best alternative that satisfies the evaluation criteria among a set of candidate solutions. It is generally hard to find an alternative that meets all the criteria simultaneously, so a good compromise solution is preferred. This problem may become more complex when multiple DMs are involved, each having not a common perception on the alternatives. Recently, a compromise ranking method (known as the VIKOR method) has been proposed to identify such compromise solutions, by providing a maximum group utility for the majority and a minimum of an individual regret for the opponent. In its actual setting, the method treats exact values for the assessment of the alternatives, which can be quite restrictive with unquantifiable criteria. This will be true especially if the evaluation is made by means of linguistic terms. For this reason we extend the VIKOR method so as to process such data and to provide a more comprehensive evaluation in a fuzzy environment. To demonstrate the potential of the methodology, the proposed extension is used for measuring the performance of enterprise resource planning (ERP) software products.

Fuzzy multi-criteria analysis approach for the evaluation and classification of cognitive performance factors in flexible manufacturing systems

Being affected from high technology, modern manufacturing systems have become more complex which means that performance factors of both individuals and manufacturing systems have become more critical due to the overloading of cognitive and decision-making tasks. In addition to this development, the classical performance measurement systems are insufficient to measure cognitive performance and the new measurement systems are needed. But converting the abstract factors of cognition to quantitative models is very difficult and complicated. In this paper, developing quantitative models for determination of cognitive performance factors is studied using the combination of cognitive mapping technique and the analytical hierarchy process in fuzzy theory to identify these factors, express and classify them quantitatively. The pairwise comparison matrices are constructed from cognitive maps, converted to fuzzy numbers using a scale, and the subjectiveness of decision-makers is prevented. Also, more accurate weights are obtained and the monitoring levels of performance factors are suggested. An example in Flexible Manufacturing Systems is provided throughout the paper in order to explain the concepts and discussion.

Evaluating IT/IS investments: A fuzzy multi-criteria decision model approach

IT/IS represents a substantial financial investment for many organizations. Whether or not to invest in new IT/IS is, therefore, a difficult decision. Simply considering human resource cost saving criteria is not enough, especially when a corporation has had many information systems. What kind of criteria should we consider when we evaluate IT/IS? How can a new IT/IS project be evaluated in an easy, cost-effective, and collective manner? We need a tool that will help decision-makers evaluate potential new investment projects. Apart from evaluating the new project per se, its compatibility and ability to integrate with an existing IT portfolio must also be considered. Hence, we propose a new approach based on the fuzzy multi-criteria decision model (FMCDM), featuring a 2-stage evaluation process with 26 criteria for IT/IS investment. All stakeholders in a corporation can decide the relative weights they give to the criteria when they evaluate a new IT/IS project by using linguistic values. Experts can also use linguistic values to evaluate all candidates easily. Only an Excel worksheet is needed to obtain an evaluation result. It is cost-effective and efficient. We conduct a case study to show how this model can be used and discuss the results.

A fuzzy hybrid decision-aid model for selecting partners in the design chain

Due to reducing product life cycle and increasing product complexity, design chain management has become more important for industries to develop innovative products within a shorter lead-time. Selecting the design chain partners that is a key determinant of co-development success involves multiple selection criteria and constraints that are either qualitative or quantitative. Poor selection of design chain partners may cause higher development time and cost and less competitive products. This paper considers design chain partnership formation as a multi-criteria decision-making problem. Fuzzy set theory is used to represent information that is mostly imprecise and uncertain at this stage. A fuzzy hybrid decision-aid model that integrates a fuzzy multi-criteria outranking approach with a fuzzy integer programming model is proposed to tackle both qualitative and quantitative factors as well as the decision flexibility. A genetic algorithm approach is applied to select the set of partners that maximise the total performance score of the entire design chain and satisfy the constraints of target development time and cost at the same time. An example of mobile phone design chain is used to illustrate the developed concept.

A decision support system methodology for forecasting of time series based on soft computing

Exponential procedures are widely used as forecasting techniques for inventory control and business planning. A number of modifications to the generalized exponential smoothing (Holt–Winters) approach to forecasting univariate time series is presented, which have been adapted into a tool for decision support systems. This methodology unifies the phases of estimation and model selection into just one optimization framework which permits the identification of robust solutions. This procedure may provide forecasts from different versions of exponential smoothing by fitting the updated formulas of Holt–Winters and selects the best method using a fuzzy multicriteria approach. The elements of the set of local minima of the non-linear programming problems allow us to build the membership functions of the conflicting objectives. It is compared to other forecasting methods on the 111 series from the M-competition.

Groundwater Vulnerability Assessment for Organic Compounds: Fuzzy Multicriteria Approach for Mexico City

This study was based on a groundwater vulnerability assessment approach implemented for the Mexico City Metropolitan Area (MCMA). The approach is based on a fuzzy multi-criteria procedure integrated in a geographic information system. The approach combined the potential contaminant sources with the permeability of geological materials. Initially, contaminant sources were ranked by experts through the Analytic Hierarchy Process. An aggregated contaminant sources map layer was obtained through the simple additive weighting method, using a scalar multiplication of criteria weights and binary maps showing the location of each source. A permeability map layer was obtained through the reclassification of a geology map using the respective hydraulic conductivity values, followed by a linear normalization of these values against a compatible scale. A fuzzy logic procedure was then applied to transform and combine the two map layers, resulting in a groundwater vulnerability map layer of five classes: very low, low, moderate, high, and very high. Results provided a more coherent assessment of the policy-making priorities considered when discussing the vulnerability of groundwater to organic compounds. The very high and high vulnerability areas covered a relatively small area (71 km(2) or 1.5% of the total study area), allowing the identification of the more critical locations. The advantage of a fuzzy logic procedure is that it enables the best possible use to be made of the information available regarding groundwater vulnerability in the MCMA.

Fuzzy multi-criteria approach to ordering policy ranking in a supply chain

In this paper, a new fuzzy multi-criteria mathematical model for the selection of the best among a finite number of ordering policy of raw material in a supply chain is developed. The problem treated is a part of the purchasing plan of a company in an uncertain environment and it is very common in business practice. Optimization criteria selected describe the performance measures of ordering policies and generally their relative importance is different. It is assumed that the values of the optimization criteria are vague and imprecise. They are described by discrete fuzzy numbers and by linguistic expressions. The linguistic expressions are modeled by discrete fuzzy sets. The measures of belief that one ordering policy is better than another are defined by comparing fuzzy numbers. An illustrative example is given.

A fuzzy multi-criteria decision approach for software development strategy selection

This study proposes a methodology to improve the quality of decision-making in the software development project under uncertain conditions. To deal with the uncertainty and vagueness from subjective perception and experience of humans in the decision process, a methodology based on the extent fuzzy analytic hierarchy process modeling to assess the adequate economic (tangible) and quality (intangible) balance is applied. Two key factors of economic and quality are evaluated separately by fuzzy approaches and both factors' estimates are combined to obtain the preference degree associated with each software development project strategy alternative for selecting the most appropriate one. Using the proposed approach, the ambiguities involved in the assessment data can be effectively represented and processed to assure a more convincing and effective decision-making. Finally, a real case-study is given to demonstrate the potential of the methodology.