Fuzzy Multi-criteria Decision Analysis Research Articles

An ecotourism suitability index for a world heritage city using GIS-multi criteria decision analysis techniques

The concept of ecotourism has experienced a significant surge in popularity over the past two decades, primarily driven by the multitude of adverse impacts associated with mass tourism. The objective of the study was to develop a comprehensive ecotourism suitability index to guide policymakers in implementing tourism development policies. Given the considerable appeal of the study area to both local and international tourists, it is essential to conduct a systematic evaluation to pinpoint suitable areas for ecotourism development. This necessity arises from the study area's placement within a fragile ecosystem and its proximity to a UNESCO World Heritage site. We employed a Geographic Information Systems (GIS) integrated environment coupled with a fuzzy Multi-Criteria Decision Analysis (MCDA) methodology. The GIS-MCDA integrated framework leverages the Analytic Hierarchy Process (AHP) and a weighted linear combination that seeks to amalgamate many features and criteria to assess ecotourism potential by integrating 20 criteria into six separate categories: landscape, topography, accessibility, climate, forest and wildlife, and negative factors. Weights were allocated to each criterion and factor based on the expert's opinions of their impact on the development of ecotourism. The final ecotourism suitability index comprised five unique classes: very high, high, moderate, less, and not suitable. Results reveal that out of the total areas, 45.4 % (259 km2) are within the high and very high suitable classes. The sensitivity analysis suggested that ecotourism potentials are more favorable to forest and accessibility variables. The generated index can be utilized as a road map since validation verified a 64 % accuracy. Given the dearth of earlier research, this study provides vital support for the development of sustainable ecotourism projects in the study area.

Hierarchical Fuzzy MCDA Multi-Risk Model for Detecting Critical Urban Areas in Climate Scenarios

One of the issues of greatest interest in urban planning today concerns the evaluation of the most vulnerable urban areas in the presence of different types of climate hazards. In this research, a hierarchical fuzzy MCDA model is implemented on a GIS-based platform aimed at detecting the urban areas most at risk in the presence of heatwave and pluvial flooding scenarios. The proposed model aims to detect the urban areas most vulnerable to both the two climatic phenomena and the two types of hazards as independent events; it partitions the physical component of an urban settlement into two subsystems: buildings and open spaces, and it determines the criticality of a subzone of the urban area of study by evaluating the vulnerabilities of the two subsystems to the two phenomena. The use of a hierarchical fuzzy MCDA model facilitates the modeling of the two subsystems and the assessment of their vulnerability to the two phenomena, and it provides a computationally fast tool for detecting critical urban areas. The model was tested on a study area made up of the districts of the central-eastern area of the city of Naples (Italy); it was divided into subzones made up of individual census areas. The most critical areas are represented by the subzones with criticality values higher than a specific threshold.

Location selection for waste disposal boxes: a geographic information systems based Pythagorean fuzzy multi-criteria decision analysis

Location selection for waste disposal boxes: a geographic information systems based Pythagorean fuzzy multi-criteria decision analysis

Version [1.1]- [pyFDM: A python library for uncertainty decision analysis methods

This paper describes an extension of the pyFDM library by extending the functionalities of the programming package with new Fuzzy Multi-Criteria Decision Analysis methods such as Combined Compromise Solution (CoCoSo), Stable Preference Ordering Towards Ideal Solution (SPOTIS), Weighted Aggregated Sum Product ASSessment (WASPAS), Weighted Product Model (WPM), and Weighted Sum Model (WSM). Furthermore, the proposed update includes new normalization techniques, new distance metrics, and defuzzification methods to allow users to examine the greater variety of research approaches in a Triangular Fuzzy environment. Furthermore, the update also extends with the implementations allowing for fuzzy calculations outside the multi-criteria field, which is allowed by the implementation of selected mathematical operations for Triangular Fuzzy Numbers. The opportunity to visualize the data within the fuzzy extension considered is also included. The extension of the pyFDM package broadens the scope of its usage in research approaches, making it a more suitable tool for multi-criteria decision analysis within the fuzzy environment.

Determining the factors affecting transportation demand management and selecting the best strategy: A case study

Demand management in transportation services, as well as demand sustainability, demand planning, and demand implementation are critical components in service quality and environmental sustainability. It is evident that developing a transportation system, particularly one responsive to society’s aspirations and demands, will result in long-term and sustainable success for enterprises. Transportation demand management and strategies can produce alternative perspectives at a desirable and sufficient level to handle problems in the field while considering the obstacles. Considering the elements influencing transportation demand management in logistics enterprises, this study aims to evaluate factors affecting transportation demand management and select the best strategy. The study was carried out on logistic firms with a corporate identity in the Turkish province of Samsun (Turkiye), engaged in international transportation activities to define the criteria affecting transportation demand management and select the best transportation demand strategy. The constituents from the literature review were analyzed using the q-rung orthopair fuzzy MCDA methodology. “Freight Transportation” was determined to be the most crucial measure affecting transportation demand management, while “Competition Legislation and Regulations Regulation” was the least important factor. The rapid expansion in the number of companies participating in freight transport and their capacity was evaluated as the essential aspect for the relevant logistics companies in transport demand management. The “Strategy for Prioritizing High Occupancy Vehicles” is the best transportation strategy based on the weights of the transportation demand management factors. Because of the implementation of this strategy in logistics organizations, this study is regarded as a significant indicator of competitiveness to ensure efficiency and effectiveness. The defined transport demand strategy also represents an exemplary model of the measures that need to be developed to increase the demand preference for sustainable transport.

A family of fuzzy multi-criteria sorting models FTOPSIS-Sort: Features, case study analysis, and the statistics of distinctions

A family of fuzzy multi-criteria sorting models, FTOPSIS-Sort, as a fuzzy extension of Multi-Criteria Decision Analysis (MCDA) ordinary method TOPSIS, is introduced and analyzed. Models from this family differ by approaches to determining functions of fuzzy numbers (approximate computations, standard fuzzy arithmetic, and transformation method) and by methods for ranking of fuzzy numbers (two defuzzification based ranking methods are considered). The features of developing and adjusting Fuzzy TOPSIS (FTOPSIS) models to sorting problematic are presented. The developed models are implemented in the case study on a healthcare supply chain alternative selection problem. For exploring distinctions in sorting alternatives by FTOPSIS-Sort models, the special algorithms have been developed along with their integrating with Monte Carlo simulation of a large number of input scenarios, each of which is a separate (and independent of the others) multicriteria problem on sorting alternatives. The results of such an analysis demonstrate a significant distinction in sorting alternatives by different FTOPSIS-Sort models. The latter has theoretical, methodological, and applied significance within the use of Fuzzy TOPSIS (Fuzzy MCDA) sorting models.

Understanding the exchange process between ground and surface water using mini drive point piezometer and mathematical models to identify suitable managed aquifer recharge sites.

In the current study, subsurface characteristics within the complex formation of the Shilabati basin system of West Bengal, India, extending over an area of 3888 km2, have been estimated using a cost-effective piezometer and MIKE FEFLOW package based on a steady-state numerical model. Pore size and fine particle content of streambeds are affected by two opposing flow contraptions. Such opposite flow conditions are likely to affect the hydraulic conductivity of the streambed. However, analogies of the hydraulic conductivity (Kh) of streambeds for losing and gaining streams have not been well documented in the recent past. The Kh value from the piezometer has been highest at the Dakshin Pairachali site (6.765 m/day), with the stream gaining water from the discharge of the local aquifer. Analysis of the stream-aquifer interaction using the FEFLOW model has allowed us to understand the groundwater water head of the basin ranging from 160.33 to 0.32 m.a.s.l (meters above sea level). The present study also constitutes the first attempt for the identification of suitable sites for the implementation of managed aquifer recharge (MAR) technology in West Bengal, India, to manage extreme drought events. The suitable sites have been identified by means of three fuzzy multi-criteria decision analysis based on nine criteria: river discharge, moisture content, porosity, drainage type, rainfall, land use type, geology, aquifer material, and hydraulic conductivity. To design a radial collector well and infiltration gallery for the selected site in an anisotropic, homogeneous, unconfined, and semi-infinite aquifer near a fully penetrating stream, a pumping test has been conducted to optimize a safe yield of 12.096 MLD (megaliters per day).

Site Suitability Analysis for Potential Eco-Tourism Sites with Fuzzy Multi Criteria Decision Analysis in Nashik District

Site Suitability Analysis for Potential Eco-Tourism Sites with Fuzzy Multi Criteria Decision Analysis in Nashik District

An extended picture fuzzy multicriteria group decision analysis with different weights: A case study of COVID-19 vaccine allocation

The high contagion rates of COVID-19 and the limited amounts of vaccines forced public health authorities to develop vaccinations strategies for minimizing mortality, avoiding the collapse of health care infrastructure, and reducing their negative impacts to societies and economies. We propose a Multi Criteria Group Decision Making for prioritizing a set of COVID-19 vaccination alternatives, under a picture fuzzy environment, where the weights for Decisions Experts (DE) and criteria are unknown. A panel of six DEs assess six criteria for prioritizing four groups for vaccination. The weights for DE and criteria are handled in the form of fuzzy sets. Three types of weights are calculated: subjective, objective, and mixture weights.According to our results, three out of the six criteria hold 60% of the strategic importance: 1) allocation and distribution, 2) COVID-19 strains and 3) capabilities and infrastructures. However, persons with comorbidities became the group with the highest priority, followed by essential workers, women, and adults older than 40 years. Governments, decision makers, and policy makers can find rigorous scientific evidence for articulating effective vaccinations campaigns from this work, and contribute to minimize undesired outputs, such as high mortality rates or collapse of hospitals.

Comparison of hospital service performances under COVID-19 pandemics for pilot regions with low vaccination rates

It is essential to measure the quality and performance of health centers and propose policies in order for health services to continue without interruption during the pandemic period and for the continuous and proper implementation of new procedures in hospitals with COVID-19.The measurement of service quality and performance in hospitals should be provided not only for the smooth flow of health services that are vital for individuals but also for the elimination of hesitations in the treatment and vaccination processes related to COVID-19. Previously, models have been proposed by introducing some criteria to measure and evaluate hospital service performance in some extraordinary conditions, but such a study has not yet been put forward under pandemic conditions. Starting from this point, we aim to fill the gap in the literature by conducting a measurement study for hospitals in the pilot region, where COVID-19 cases are common but vaccination is observed at low rates. For this aim, the evaluation criteria are gathered under basic dimensions as in SERVPERF (Service Performance), which is a widely used tool for measuring service quality and a fuzzy multi-criteria decision analysis is proposed to measure the service performance of state hospitals for a pilot region. In the proposed methodology, the integrated methods consisting of CRITIC-TOPSIS have been extended with fermatean fuzzy sets. Expert opinions are taken via questionaries to determine hospital service performances. Based on the results obtained from the hospitals in the pilot region, the policies and strategies to be adopted by the hospitals serving under pandemic conditions worldwide to increase the service quality have been put forward. Additionally, the sensitivity of the parameters in the problem is measured, and then the validity of the obtained results is also validated. According to the results, assurance is determined as the most important main service performance factor during the pandemic period. So, the managers should develop strategies to address people's concerns about vaccines and increase people's trust in hospitals.

Ranking of Independent and Dependent Fuzzy Numbers and Intransitivity in Fuzzy MCDA

Ranking of fuzzy numbers (FNs) is a key stage within fuzzy multicriteria decision analysis (FMCDA). However, the influence of FNs dependence on their ranking, including ranking alternatives within FMCDA, has not been studied yet. In this article, for studying such an influence, the widely used defuzzification-based fuzzy ranking methods, centroid index, and integral of means, along with their modifications, which are pairwise comparison defuzzification ranking methods, are explored. The authors argue that classical defuzzification ranking methods are intended to deal with independent FNs, whereas their modifications may be used for ranking of dependent FNs. It is provided a proof in which, pairwise comparison Yuan’s and defuzzification integral of means ranking methods, are equivalent when ordering independent and can differ when ordering dependent FNs; at the same time, Yuan’s and modified integral of means ranking methods are equivalent when ordering both independent and dependent FNs. Intransitivity of the two modified ranking methods when ordering dependent FNs as well as intransitivity of alternatives in FMCDA for fuzzy multiattribute value theory (FMAVT) as an example is proved. The distinctions in ranking of dependent FNs by all ranking methods under consideration are explored through ordering alternatives within FMAVT. For this, a real-life case study is considered, and the distinctions in ordering alternatives by classical and modified ranking methods are demonstrated. Statistical analysis of distinctions in ordering alternatives by FMAVT with different ranking methods is implemented with the use of Monte–Carlo simulation. The significance of distinctions for the choice and ranking multicriteria problems, as well as for justification of utilizing ranking methods under consideration in FMCDA, is discussed.

Customer-oriented product design using an integrated neutrosophic AHP & DEMATEL & QFD methodology

With the increasing product variety, companies aim to become better than their competitors by providing a superior product developed with a customer-oriented product design approach and a quality strategy. In order to achieve this, companies should well understand customer expectations and quickly be able to convert these expectations to technical characteristics. Since the expectations consist of mostly subjective judgments, this evaluation process contains vagueness and impreciseness. A triplet represents the uncertainty in subjective judgments: the degrees of belongingness or Truthiness (T), non-belongingness or Falsity (F), and indeterminacy (I). For this reason, in this paper, a neutrosophic Quality Function Deployment (QFD) methodology based on neutrosophic AHP and neutrosophic DEMATEL is developed and applied to the design of a car seat. In this methodology, the weighting of customer requirements is performed by neutrosophic AHP. The relationships among the technical characteristics are determined by neutrosophic DEMATEL for the customer-oriented product design, considering both impreciseness in the data and indeterminacy of the decision-makers. In other words, the contribution of this paper is that the proposed methodology provides better integration of the voice of customers into technical characteristics through a practical fuzzy multi-criteria decision analysis. Based on the results, it is revealed that seat height is the most important technical characteristic, followed by vertical travel range and horizontal travel range. Moreover, validity and verification of the proposed methodology have been tested with other methods presented in the literature. Sensitivity analyses have been carried out to show the flexibility of the given decisions under different cases. Lastly, possible implications on theoretical and managerial aspects have been discussed.

Fuzzy system F-CalcRank for calculating functions of fuzzy arguments and ranking of fuzzy numbers

Assessing functions of fuzzy arguments and ranking of fuzzy quantities are two key steps in fuzzy modeling and Fuzzy Multicriteria Decision Analysis (FMCDA). Approximate calculations along with the use of centroid index as a defuzzification based ranking methods are a generally accepted approach to applications in the fuzzy environment. This paper presents a novel fuzzy system, F-CalcRank, which is integration of two coupled fuzzy systems: F-Calc (Fuzzy Calculator) and F-Ranking (Fuzzy Ranking). F-Calc allows assessing functions of fuzzy numbers with the use of different approaches: approximate calculations, standard fuzzy arithmetic, and transformation methods. The input values to F-Calc are fuzzy numbers with the following membership functions: triangular and trapezoidal, Gaussian, bell shape, sigmoid, and piece-wise linear continuous or upper semicontinuous membership functions of any complexity, as well as fuzzy linguistic terms of a given term set. F-Ranking system is intended for ranking of a given set of fuzzy numbers, including those, which are inputs and/or outputs of the F-Calc system. F-Ranking includes six ranking methods: three defuzzification based and three pairwise comparison ones. The structure of F-CalcRank as well as input and output information and the user interfaces of both F-Calc and F-Ranking systems, which can also be used independently, are presented. Examples of computing functions of fuzzy arguments and ranking of fuzzy numbers using implemented methods as well as exploring a real case study in agro-ecology with the use of a math model in fuzzy environment are considered. These examples stress the features and novelty of F-CalcRank system as well as presented applied research. The computer modules created within F-CalcRank are a basis for different FMCDA models developed by the authors. F-CalcRank system is intended for university education, research and various applications in engineering and technology.

From MCDA to fuzzy MCDA: Presumption of model adequacy or is every fuzzification of an mCDA method justified?

A fuzzy extension of a Multi-Criteria Decision Analysis (MCDA) method implies a choice of an approach to estimating corresponding functions of fuzzy variables and a method for ordering alternatives based on ranking of fuzzy quantities. The objective of this paper is the development and comparison of Fuzzy MCDA (FMCDA) models, which represent different approaches to fuzzy extensions of an ordinary MCDA method. To do so, different approaches to assessing functions of fuzzy numbers are considered along with several methods for ranking of fuzzy numbers. Distinctions in ranking alternatives, including the number and significance of distinctions based on a granulation of the output information, are explored for different FMCDA models by using Monte Carlo simulating input scenarios of fuzzy multi-criteria problems. In addition, both intra-distinctions and inter-distinctions are explored. According to the results, distinctions in ranking alternatives by different FMCDA models may be considered as significant both for ranking and choice multi-criteria problematiques. This research is of fundamental and applied importance and has no analogues.

Uncertain Multi-Criteria analysis of offshore wind farms projects investments – Case study of the Polish Economic Zone of the Baltic Sea

Investments in offshore wind farms can be an effective development stimulus for an economy recovering from the crisis caused by the COVID-19 pandemic. Therefore, an important issue is a multi-criteria analysis of planned investments in offshore wind farms. Such an analysis allows to identify the most important projects from the perspective of sustainable development. Taking into account the uncertainties and risks related to wind energy, and additionally taking into account the uncertainty of ex-ante evaluation, such studies need to analyse the uncertainty as widely as possible. The article presents a comprehensive methodological approach that allows for the maximum capture of uncertainties and risks related to energy investment projects in the area of wind farms. This approach involves using the fuzzy multi-criteria decision analysis (FMCDA) method, sensitivity analysis and robustness analysis based on the Monte-Carlo simulations. Additionally, the uncertainty related to the applied methodology was taken into account and the same problem was solved by using different FMCDA methods. Based on the applied approach, a case study was conducted and a ranking of offshore wind farms projects in Poland was created. It was found that the most promising project is Bałtyk II, followed by Bałtyk I, both projects being carried out by the Polenergia/Equinor consortium. However, if the preference model is not known, the Bałtyk I project has the greatest chance of taking the leasing place in the ranking (31.7%), ahead of the following projects: Baltic Power (20.4%) owned by PKN Orlen and Bałtyk III (18.6%) which is also owned by Polenergia/Equinor.

Forecasting and Assessment of the Energy Security Risk in Fuzzy Environment

Energy security is of key importance for states and international organizations. An important issue in energy security is the assessment of current and future energy security methods. While the assessment of the current methods is relatively easy, since it is based on recent information, the assessment of the future methods is burdened with uncertainty and is therefore much more difficult. Therefore, the aim of the article is to develop a new approach for assessing current and future energy security issues based on a complex security index, supported by the computationally transparent fuzzy multi-criteria decision analysis (MCDA) method. The use of the fuzzy MCDA methods allows one to capture the uncertainty of assessments and forecasts, and the forecasts themselves were based on the Holt’s method; the international energy security risk index (IESRI) was used as the source of the data to generate the forecasts. The research compared two data sources for forecasts (IESRI categories and metrics) and two methods of forecast fuzzification. As a result, the forecasted assessments and rankings of energy security for the 2020–2030 period were obtained. On the basis of these forecasts, general trends shaping energy security were also indicated.

Performance of Fuzzy Multi-Criteria Decision Analysis of Emergency System in COVID-19 Pandemic. An Extensive Narrative Review.

The actual coronavirus disease 2019 (COVID-19) pandemic has led to the limit of emergency systems worldwide, leading to the collapse of health systems, police, first responders, as well as other areas. Various ways of dealing with this world crisis have been proposed from many aspects, with fuzzy multi-criteria decision analysis being a method that can be applied to a wide range of emergency systems and professional groups, aiming to confront several associated issues and challenges. The purpose of this critical review was to discuss the basic principles, present current applications during the first pandemic wave, and propose future implications of this methodology. For this purpose, both primary sources, such as scientific articles, and secondary ones, such as bibliographic indexes, web pages, and databases, were used. The main search engines were PubMed, SciELO, and Google Scholar. The method was a systematic literature review of the available literature regarding the performance of the fuzzy multi-criteria decision analysis of emergency systems in the COVID-19 pandemic. The results of this study highlight the importance of the fuzzy multi-criteria decision analysis method as a beneficial tool for healthcare workers and first responders’ emergency professionals to face this pandemic as well as to manage the created uncertainty and its related risks.

Developing a Fuzzy TOPSIS Model Combining MACBETH and Fuzzy Shannon Entropy to Select a Gamification App

Due to the important advantages it offers, gamification is one of the fastest-growing industries in the world, and interest from the market and from users continues to grow. This has led to the development of more and more applications aimed at different fields, and in particular the education sector. Choosing the most suitable application is increasingly difficult, and so to solve this problem, our study designed a model which is an innovative combination of fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with the Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) and Shannon entropy theory, to choose the most suitable gamification application for the Industrial Manufacturing and Organisation Systems course in the degree programmes for Electrical Engineering and Industrial and Automatic Electronics at the Higher Technical School of Industrial Engineering of Ciudad Real, part of the University of Castilla-La Mancha. There is no precedent in the literature that combines MACBETH and fuzzy Shannon entropy to simultaneously consider the subjective and objective weights of criteria to achieve a more accurate model. The objective weights computed from fuzzy Shannon entropy were compared with those calculated from De Luca and Termini entropy and exponential entropy. The validity of the proposed method is tested through the Preference Ranking Organisation METHod for Enrichment of Evaluations (PROMETHEE) II, ELimination and Choice Expressing REality (ELECTRE) III, and fuzzy VIKOR method (VIsekriterijumska optimizacija i KOmpromisno Resenje). The results show that Quizizz is the best option for this course, and it was used in two academic years. There are no precedents in the literature using fuzzy multicriteria decision analysis techniques to select the most suitable gamification application for a degree-level university course.

Feasibility stage screening for sustainable energy alternatives with a fuzzy multi-criteria decision analysis protocol

Many regions and urban areas are becoming more engaged in selecting the optimum future clean energy technology mix to best fit their local power requirements. At the feasibility stage, such analysis is difficult to perform quantitatively due to the substantial uncertainties associated with many of the key influencing criteria. Moreover, changing climate means the renewable energy mix most suited to many regions is also changing as local climates progressively change. A protocol is proposed and evaluated for conducting qualitative multi-criteria decision analysis (MCDA) of multiple clean energy alternatives, suitable for specific regional conditions, using the TOPSIS method. This begins with linguistic assessments of a large number of pertinent criteria (50 or more) taking into account the diverse preferences of the many stakeholders involved. The linguistic assessments are inverted to integer number, fuzzy and intuitionistic fuzzy scoring (IFS) systems. The IFS method is shown to integrate uncertainty in a more flexible way. The fuzzy and IFS TOPSIS methods adjust their impact matrices with three weight factors: (1) objective weights derived from calculated entropy for each criteria, (2) subjective weights associated with preferences expressed by individual representative stakeholders; (3) subjective weights applied to balance the preferences among stakeholder groups. The three methods are applied using regionally specific case studies to illustrate and compare the clean energy rankings they select for the conditions associated with the specific region evaluated. Fuzzy and IFS scoring systems generate slightly different rankings as they capture uncertainty in different ways.

Comparison of Fuzzy TOPSIS, Fuzzy VIKOR, Fuzzy WASPAS and Fuzzy MMOORA methods in the housing selection problem

Decision-making problems in an uncertain environment can be solved using fuzzy multi-criteria decision-making methods. One such problem is the problem of housing selection, where it is challenging to determine data explicitly. Many people struggle with the difficulty of determining what conditions they would like to live in. It is hard for them to determine their housing preferences based only on data from the offers. Numerical values do not clearly define many parameters. Both apartment buyers and realtors use values such as much or little, precisely represented by fuzzy numbers.This paper proposes a solution to the housing selection problem using fuzzy MCDA techniques such as Fuzzy MMOORA, Fuzzy WASPAS, Fuzzy TOPSIS and Fuzzy VIKOR. Then the obtained ranking of alternatives was compared using ranking similarity coefficients such as weighted Spearman coefficient and WS ranking similarity coefficient. The proposed approaches produce similar results, indicating that the decision models reflect preferences well. At the end of the paper, conclusions and further research directions are presented.