Fuzzy Multi-criteria Methods Research Articles

Optimal Wind Farm Siting Using a Fuzzy Analytic Hierarchy Process: Evaluating the Island of Andros, Greece

In recent decades, fuzzy logic and fuzzy multi-criteria decision-making systems have been applied in several fields. This paper aims to determine the optimal wind farm siting solution in a fuzzy environment. Therefore, the main research question of the present paper is whether and to what extent the uncertainty in the researcher’s judgments affects the ranking of wind farm siting solutions. The fuzzy analytical hierarchy method is applied to an existing case study of wind farm siting on the island of Andros, examining the stability of the final priorities of the alternatives under a regime of gradual increases in ambiguity, as well as whether the introduced ambiguity in the model corresponds to any uncertainty the researcher has during the process of scoring the criteria and alternatives. Five assessment criteria (wind potential, ground slope, distance from road network, distance from high-voltage network, and social acceptance of local population) and eight eligible suitable alternatives (A1–A8) for wind farm siting are considered in the computations. The methodology includes the fuzzification of initial decision-maker judgments, the calculation of fuzzy intermediate priorities (weights), the defuzzification of fuzzy intermediate priorities (weights), and the synthesis of intermediate priorities into final priorities of alternatives, according to the procedures of the crisp AHP (CAHP). Under the assumptions of the initial case study, the results show that the final priorities are quite robust when faced with increased ambiguity. In almost all the examined cases, the alternative initially chosen as the best, A1, is dominant, followed by A3. In addition, in all cases, social acceptance favors alternative A1, and wind velocity favors alternative A8. Therefore, fuzzy multi-criteria methods can be applied to determine an optimal wind farm siting solution when criteria with qualitative characteristics are used and the manifestation of preferences involves strong elements of subjectivity.

SPACED REPETITION CONCEPT DESIGN WITH FUZZY MULTI CRITERIA ANALYSIS AS A MEDIA TO IMPROVE NUMERACY LEARNING FOR ELEMENTARY SCHOOL STUDENTS

Numeracy Learning activities after the Covid-19 Pandemic has been decline in the quality of learning in a number of elementary school students. This research is limited to learning numeracy for low grade students, namely grade 1,2, and 3, because the basic concept of numeration begins with low grades There are several factors that make difficult for students to understand numeracy, the students often forget about the concepts that have been taught before. An Effective way of memorizing with the conventional way is memorizing with repeat pronunciations. To improve the quality of education, learning concepts that were previously carried out conventionally must be developed in a modern way using application. The purpose of this research is to improve the numeracy learning concept of low-grade students in primary school after Covid-19 pandemic which is more fun and modern by using the concept of spaced repetition based on android flashcards. The analysis of 135 student’s assessment is based on criteria such as tangible, reliability, empathy, responsive, and assurance. The decision support system using Fuzzy Multi Criteria Methods (MCDM) is also used to determine the weighting of the criteria and the effectiveness of learning using spaced repetition concept and it’s application. The result of the weighting using fuzzy multi criteria is obtained defuzzification that tangible 65.18, reliability 56.54, responsive 46.17, assurance 49.13, and empathy 29.62. Tangible has the highest results in this test, it means that the students prefer modern learning with android application with an attractive experienced. The correlation test obtained the result 0.76 which is a high value in decision making and could be accepted.

Decision support framework for inventory management combining fuzzy multicriteria methods, genetic algorithm, and artificial neural network

Decision support tools, within the Industry 4.0 perspective, have increasingly impacted different operations and supply chain management (OSCM) areas, such as inventory management. Within the digital transformation era, multicriteria decision-making (MCDM) and machine learning (ML) can be used to improve inventory management decisions. Despite their importance, the literature lacks empirical studies involving advanced solutions that combine both approaches to support practitioners in real-life settings. This is especially relevant for maintenance, repair, and operation (MRO) inventories, which usually present several SKUs with irregular demand patterns and difficult forecasting. Therefore, this study proposes a decision support framework for inventory management, combining MCDM and ML approaches, and applies it to a railway logistics operator to assist its MRO inventory management decision-making process. The first stage of the framework consists of applying a hybrid MCDM method that combines fuzzy logic with the analytic hierarchy process (AHP) and VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) methods to rank and select SKUs according to importance and criticality. Once the most critical SKUs are revealed, a second stage of the framework is introduced to forecast the demand for these SKUs through an ML model, which combines a genetic algorithm and an artificial neural network (GA-ANN). Research findings point to a considerable improvement in the accuracy of the demand forecast for SKUs compared to the previous forecast by the company, and the forecasting methods support vector machine (SVM) and exponential smoothing. The results of the combined approaches are consolidated into a management dashboard, which improves the agility and quality of the analyzed company's decision-making process in inventory management. Therefore, practitioners can also take stock of the proposed framework as a semi-automatic management artefact to enhance decision-making from a digital transformation view in a vital area of OSCM.

A Hybrid MCDM for the Location of Urban Distribution Centers under Uncertainty: A Case Study of Casablanca, Morocco

In this article, the main subject is the problem of the location of Urban Distribution Centers (UDC) in an environment characterized by uncertainty. Thus, a decision support process based on fuzzy multi-criteria methods (F-MCDM) will be proposed as the solution. The CATWOE method is used for identifying the Stakeholders (PPs) concerned by the localization. Furthermore, direct interviews and the review of the literature help to enhance the decision criteria. The use of the F-SWARA method made it possible to weight the criteria by taking into consideration the opinion of the PPs. The F-ENTROPY method corrected the subjectivity of the weights given by the first method. The hybridization of the two is a first in the literature in this field and allows precise and realistic results. In the end, the different alternatives obtained by the F-VIKOR method are ranked. This approach was applied to the city of Casablanca, which is extremely impacted by the negative externalities of urban freight transport. The results of this study showed that zone four (AIN SBAA) is the best zone to implement the UDC in the city of Casablanca. The sensitivity analysis validated the robustness of the model.

Evaluation of the Technological Competences Through Dissemination of Knowledge in Technological Incubators with Faph

Unlike the industrial economy that valued vertical integration, the knowledge economy stimulates and values the formation of interorganizational alliances and business arrangements built in networks. Competitiveness shifts from a one-way, individual and endogenous process within firms to an open, multidirectional, collaborative and networked process. In this context, the objective of this study is to evaluate the technological competences (using the SECI model of knowledge conversion) present in technological incubators, through fuzzy multi-criteria methods. A hybrid mathematical methodology will be used with the integration of the Fuzzy Delphi (FDelphi) and Fuzzy Analytic Hierarchy Process (FAHP) methods. The FDelphi will serve to raise and validate the critical factors (criteria/subcriteria) present to evaluate technological competencies in technological incubators. The FAHP method will be applied to calculate the relative weights of the selected criteria/subcriteria that affect the problem in question. Regarding the results, the following criteria were obtained: C4 (Socialization-38%), that is, the way in which the employees of an incubator share the knowledge, the second one that stood out the most was C1 (External acquisition of knowledge-28%) sources from which employees acquire tacit and/or explicit knowledge of the incubator's external environment. This proposal is expected to improve the competitiveness of technology incubators by assessing technological skills and disseminating knowledge.

Métodos quantitativos para seleção de fornecedores sustentáveis: uma revisão sistemática da literatura

A temática ambiental e social, cada vez mais presente e atuante no cotidiano das operações das empresas, precisa ser tratada no contexto da seleção de fornecedores, pois a literatura acadêmica e profissional é carente nesta área, especialmente no que tange ao uso de métodos quantitativos e no tratamento de aspectos sociais. Para tanto, o objetivo deste artigo foi realizar uma revisão sistemática da literatura com um propósito descritivo-explicativo que permitisse coletar, identificar, selecionar e avaliar criticamente os métodos quantitativos utilizados na seleção de fornecedores sustentáveis do ponto de vista verde e social. Buscando ir além de uma revisão sistemática tradicional, este trabalho fez uso também de técnicas de análise de conteúdo via tag clouds e de análise de correspondência. As principais evidências encontradas na literatura sobre seleção de fornecedores verdes/sustentáveis foi o uso intensivo de técnicas combinadas e a ampla adoção da lógica fuzzy nas técnicas multicritério, programação matemática e inteligência artificial.

Optimisation of key performance measures in air cargo demand management

This article sought to facilitate the optimisation of key performance measures utilised for demand management in air cargo operations. The focus was on the Revenue Management team at Virgin Atlantic Cargo and a fuzzy group decision-making method was used. Utilising intelligent fuzzy multi-criteria methods, the authors generated a ranking order of ten key outcome-based performance indicators for Virgin Atlantic air cargo Revenue Management. The result of this industry-driven study showed that for Air Cargo Revenue Management, ‘Network Optimisation’ represents a critical outcome-based performance indicator. This collaborative study contributes to existing logistics management literature, especially in the area of Revenue Management, and it seeks to enhance Revenue Management practice. It also provides a platform for Air Cargo operators seeking to improve reliability values for their key performance indicators as a means of enhancing operational monitoring power.

Fuzzy analytic hierarchy process with interval type-2 fuzzy sets

The membership functions of type-1 fuzzy sets have no uncertainty associated with it. While excessive arithmetic operations are needed with type-2 fuzzy sets with respect to type-1’s, type-2 fuzzy sets generalize type-1 fuzzy sets and systems so that more uncertainty for defining membership functions can be handled. A type-2 fuzzy set lets us incorporate the uncertainty of membership functions into the fuzzy set theory. Some fuzzy multicriteria methods have recently been extended by using type-2 fuzzy sets. Analytic Hierarchy Process (AHP) is a widely used multicriteria method that can take into account various and conflicting criteria at the same time. Our objective is to develop an interval type-2 fuzzy AHP method together with a new ranking method for type-2 fuzzy sets. We apply the proposed method to a supplier selection problem.

Prioritisation of performance indicators in air cargo demand management: an insight from industry

Purpose – Real operational data are used to optimise the performance measurement of air cargo capacity demand management at Virgin Atlantic Cargo by identifying the best KPIs from the range of outcome-based KPIs in current use. Design/methodology/approach – Intelligent fuzzy multi-criteria methods are used to generate a ranking order of key outcome-based performance indicators. More specifically, KPIs used by Virgin Atlantic Cargo are evaluated by experts against various output criteria. Intelligent fuzzy multi-criteria group making decision-making methodology is then applied to produce rankings. Findings – A useful ranking order emerges from the study albeit with the important limitation that the paper looked solely at indices focussing exclusively on outcomes while ignoring behavioural complexity in the production of outcomes. Originality/value – This paper offers a practical overview of the development of performance measures useful for air cargo capacity demand management.

Evaluation and Ranking of Organizational Resilience Factors by Using a Two-Step Fuzzy AHP and Fuzzy TOPSIS

We presented a novel fuzzy multicriteria decision making approach to evaluate and rank organizational resilience factors with respect to user preference orders. Due to vagueness of the decision data, the precise numerical data are inadequate for real-life business situations. Human judgements can be expressed by linguistic expressions which are modeled by fuzzy sets. The complexity of the considered problem calls for analytic methods rather than intuitive decisions. Two fuzzy multi-criteria methods are proposed for solving the treated problem: Fuzzy Analytic Hierarchical Process (FAHP) is applied to determine the relative importance of business processes and the relative importance of organizational resilience factors under each business process, and an extension of the fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) is applied to rank the organizational resilience factors. With respect to complexity and the type of considered management problem, we introduce a modified fuzzy decision matrix. The proposed algorithm has efficiently been applied in the assessment of organizational resilience factors to small and medium enterprises of the process industry.

A Comparison of Parametric and Fuzzy Multi-Criteria Methods for Evaluating Land Suitability for Olive in Jeffara Plain of Libya

Boolean approaches to land suitability treat both the spatial units and the value ranges as clearly defined. This is to ignore the continuous nature of land properties as well as the differences and uncertainties in measurement. The objective of this paper was to compare two approaches to land suitability evaluations; Parametric and Fuzzy Multi-Criteria Methods to model the opportunities for olive production in Jeffara Plain of Libya. In this paper a number of soil and landscape criteria were identified and their weights specified as a result of discussions with local experts. The Fuzzy MCE approach was found to be better than the parametric approach. The Fuzzy MCE approaches accommodate the continuous nature of many soil properties and produce more intuitive distributions of land suitabilities value for olive. The results of Fuzzy MCE showed that the majority of the study area is highly suitable for olive production, while the results obtained from the use the parametric method showed that most of the study area is moderately suitable for olive production.

Qualitative multicriteria methods for fuzzy evaluation problems: An illustration of economic-ecological evaluation

It is nowadays increasingly realised that environmental and resource problems generally have complicated economic and ecological dimensions. Therefore, there is a clear need for models offering a comprehensible and operational representation of a real-world environmental system. A great variety of quantitative (descriptive and forecasting) models has been developed for compound environmental systems, but there is still a need in a planning context for evaluation methods taking into account information of a mixed (qualitative and quantitative) type. This paper presents a new discrete multicriteria evaluation model whose impact (or evaluation) matrix may include either crisp, stochastic or fuzzy measurements of the performance of an alternative a n with respect to a criterion g m . No traditional weighting of criteria is used in this method. From an empirical point of view, this model is particularly suitable for economic-ecological modelling incorporating various degrees of precision of the variables measured. From a methodological point of view, two main issues will be faced here: (i) the problem of equivalence of the used procedures in order to standardise the various evaluations (of a mixed type) of the performance of alternatives according to different criteria; (ii) the problem of comparison of fuzzy numbers typical of all fuzzy multicriteria methods. An illustrative numerical example will be presented at the end of the paper.