A methodology for personnel selection in business development: An interval type 2-based fuzzy DEMATEL-ANP approach

The employee selection procedure holds significant importance for every company or organization involved in current economic activities. It is vital to underscore staff selection since it plays a crucial role in determining a firm's success. When a corporation selects a suitable applicant for a position and initiates the recruiting process, it might mark a critical point for the firm and substantially impact its functioning. In the present study, I utilized the trapezoidal interval type-2 (IT2) fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) - Analytic Network Process (ANP) methodology to conduct person selection. Additionally, statistically significant values for both the primary and secondary criteria related to staff recruitment were determined. The employment of square comparison matrices is essential for implementing the DEMATEL-ANP approach. The number of subcriteria for this application is equal to that of the alternative. The approach was successfully executed without facing any obstacles. The aim of this research was to investigate the viability of employing IT2 Fuzzy Systems to handle and control uncertainty efficiently. In addition, the implementation of sensitivity analysis plays a vital role in the context of multicriteria decision-making (MCDM) techniques.

Evaluating the skills of the CMIP5 global climate models using multicriteria decision-making analysis in Ethiopia

This study evaluates the skills of 30 CMIP5 GCMs and the Multimodel Ensemble (MME) in reproducing the characteristics of observed precipitation (Pr), minimum (Tmin), and maximum (Tmax) temperature over the Middle Awash sub-basin (MASB) in Ethiopia. The MME of the climate variables was generated using the simple arithmetic mean method. The entire analysis was performed on the raw historical GCM simulations (before bias correction) and observed data for the periods 1981-2005 based on monthly and annual time series data over the annual and seasonal temporal resolutions. This study considered two approaches. The first one was an evaluation of GCMs employing five statistical performance metrics (SPMs), i.e., mean, CV, PBIAS, RSR, and r. The second approach involves the application of multicriteria decision-making (MCDM) analysis, adopting three SPMs (PBIAS, RSR, and r). The relative weights of the three metrics were determined by the entropy method. Besides, the weighted average and compromise programming techniques were employed to rank and select the best-performing GCMs. The findings from the first approach using five SPMs demonstrate that, for a given variable of interest, a GCM that performs well for one SPM may fail to produce the same for another SPM on the same temporal scale. Likewise, for the same SPM at different resolutions, a GCM may perform well for a one-time scale but poorly for another. These suggested that the results of GCM skills relied mainly on the SPM, time scale, and data formats chosen for analysis. Hence, it is critical to comprehensively evaluate the skill of GCMs using multiple performance metrics over a range of spatial and temporal settings and data formats. In addition, results of the MCDM analysis proved that the ensemble of GCMs, which provide adequate performance in simulating the salient features of Pr, Tmin, and Tmax concomitantly across the MASB, encompass CMCC-CMS, BCC-CSM1.1(m), CMCC-CM, BNU-ESM, CanESM2, and MPI-ESM-MR. However, it was observed that different GCMs performed much differently in characterizing various variables over a range of temporal scales and data formats. The MME also proved its superior potential in duplicating the climate of the study area over several individual GCMs. The overall findings attested that instead of aggregating the ranks from the three variables into one, it is recommended to treat each variable independently while developing a subset of best-performing GCMs for ensembling since each GCM responds differently to each variable under a set of conditions. Finally, the approaches and findings from this study will be valuable input for subsequent climate and hydrologic studies in the study area and beyond.

Classifying micropollutantsbyenvironmental riskin groundwater using screening analysis associated to a hybrid multicriteria method combining (Q)SAR tools, fuzzy AHP and ELECTRE

This study aimed to evaluate and monitor pesticides in groundwater of the Serra Geral aquifer, located in the Paraná Basin 3 (southern Brazil), using Liquid Chromatography coupled with a Quadrupole-Time-of-Flight Mass Spectrometer (LC-QTOF MS). A total of 117 samples, collected in three different moments, were analyzed over 36 months. Groundwater samples from 35 wells and four surface water points were monitored in each sampling campaign. A pesticide screening methodology was proposed with the tentative identification of 1607 pesticides and pesticide metabolites. The application of the proposed methodology resulted in the verification of 29 pesticides and pesticide metabolites, 7 as confirmed analytes and 22 as suspect compounds. (Q)SAR in silico predictions and GUS index calculation provided data on the potential environmental risk of the identified compounds, with eight endpoints considered. After in silico predictions, an alternative hybrid multicriteria method was applied, combining the weighting of endpoints of fuzzy AHP and micropollutants classification by environmental risk using ELECTRE. The fuzzy AHP results indicated the greatest importance of mutagenicity among the eight evaluated indicators, while the scarce influence of the physicochemical properties on the environmental risk suggested their exclusion from the model. Accordingly, the ELECTRE results highlighted the prevalence of thiamethoxam and carbendazim as the most dangerous for the environment. The application of the proposed method enabled the selection of the compounds that must be monitored, considering mutagenicity and toxicity predictions for environmental risk analysis.

A novel study of spherical fuzzy soft Dombi aggregation operators and their applications to multicriteria decision making

In many decision-making situations, we are not restricted to two kinds of aspects, such as membership degree or nonmembership degree, and sometimes we need to include the abstinence degree (AD). However, many fuzzy set theories fail to cover issues, such as an intuitionistic fuzzy soft set, Pythagorean fuzzy soft set and q-rung orthopair fuzzy soft set. All the above notions can only consider membership degree and a nonmembership degree in their structures. The spherical fuzzy soft set compensates for these drawbacks in its structure. Moreover, the Dombi t-norm and Dombi t-conorm are the fundamental apparatuses to generalize the basic operational laws of sum and product. Therefore, in this article, based on the dominant features of spherical fuzzy soft sets and valuable features of the Dombi t-norm and Dombi t-conorm, we initially developed the basic Dombi operational laws for spherical fuzzy soft numbers. Moreover, based on these newly developed operational laws, we introduced aggregation operators called spherical fuzzy soft Dombi average (weighted, ordered weighted, hybrid) aggregation operators. We discussed the basic properties of these aggregation operators. Additionally, we have developed a multiple criteria decision making (MCDM) approach using an explanatory example via our approach to show its effective utilization. Furthermore, a comparative study of our approach shows the superiority of our introduced notions.

A comparative assessment of flood susceptibility modelling of GIS-based TOPSIS, VIKOR, and EDAS techniques in the Sub-Himalayan foothills region of Eastern India

In the Sub-Himalayan foothills region of eastern India, floods are considered the most powerful annually occurring natural disaster, which cause severe losses to the socio-economic life of the inhabitants. Therefore, the present study integrated geographic information system (GIS) and three comprehensive and systematic multicriteria decision-making (MCDM) techniques such as Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Vise Kriterijumska Optimizacijaik Ompromisno Resenje (VIKOR), and Evaluation Based on Distance from Average Solution (EDAS) in Koch Bihar district for comparative assessment of the flood-susceptible zones. The multi-dimensional 21 indicators were considered, and multicollinearity statistics were employed to erase the issues regarding highly correlated parameters (i.e., MFI and long-term annual rainfall). Results of MCDM models depicted that the riparian areas and riverine "chars" (islands) are the most susceptible sectors, accounting for around 40% of the total area. The microlevel assessment revealed that flooding was most susceptible in the Tufanganj-I, Tufanganj-II, and Mathabhanga-I blocks, while Haldibari, Sitalkuchi, and Sitai blocks were less susceptible. Spearman's rank (r_s) tests among the three MCDM models revealed that TOPSIS-EDAS persisted in a high correlation (r_s = 0.714) in contrast to the relationships between VIKOR-EDAS (r_s = 0.651) and TOPSIS-VIKOR (r_s = 0.639). The model's efficiency was statistically judged by applying the receiver operating characteristic-area under the curve (ROC-AUC), mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) techniques to recognize the better-suited models for mapping the flood susceptibility. The performance of all techniques is found good enough (ROC-AUC =  > 0.700 and MAE, MSE and RMSE =  < 0.300). However, TOPSIS and VIKOR have manifested an excellent outcome and are highly recommended for identifying flood susceptibility in such active flood-prone areas. Thus, this kind of study addresses the role of GIS in the construction of the flood susceptibility of the region and the performance of the respective models in a very lucid manner.

Implications for sustainable healthcare operations in embracing telemedicine services during a pandemic

Technological interventions in the healthcare sector, namely, telemedicine services, have helped the government and people in these extraordinarily challenging times of ongoing COVID-19 pandemic. We identify and group key success factors relevant to telemedicine services under 7 contextual criteria. Furthermore, we explore the causal relations among them using the decision-making trial and evaluation laboratory (DEMATEL) method. Then, by applying the Bayesian best-worst method (BWM), we compute the relative importance of these criteria. Thereafter, we rank six hospitals that have provided telemedicine services through a comparative evaluation using the VIsekriterijumsko KOmpromisno Rangiranjie (VIKOR) method. The threefold findings of our study reveal that (i) the technological criteria provide the highest causal impact, while the environmental criteria provide the least causal impact. (ii) The hierarchical model of criteria, achieved through the Bayesian BWM score, shows that the criteria weights for both technological and organizational criteria are maximum (0.205) and minimum (0.087), respectively. (iii) The evaluation of six hospitals with VIKOR based on seven criteria ranks the Himalayan hospital as first, showing that it is best in providing telemedicine services to patients. Public health policymakers could use the results of our study to devise an effective plan for patient care in crisis, like COVID-19.

Integration of fuzzy-weighted zero-inconsistency and fuzzy decision by opinion score methods under a q-rung orthopair environment: A distribution case study of COVID-19 vaccine doses

Owing to the limitations of Pythagorean fuzzy and intuitionistic fuzzy sets, scientists have developed a distinct and successive fuzzy set called the q-rung orthopair fuzzy set (q-ROFS), which eliminates restrictions encountered by decision-makers in multicriteria decision making (MCDM) methods and facilitates the representation of complex uncertain information in real-world circumstances. Given its advantages and flexibility, this study has extended two considerable MCDM methods the fuzzy-weighted zero-inconsistency (FWZIC) method and fuzzy decision by opinion score method (FDOSM) under the fuzzy environment of q-ROFS. The extensions were called q-rung orthopair fuzzy-weighted zero-inconsistency (q-ROFWZIC) method and q-rung orthopair fuzzy decision by opinion score method (q-ROFDOSM). The methodology formulated had two phases. The first phase 'development' presented the sequential steps of each method thoroughly.The q-ROFWZIC method was formulated and used in determining the weights of evaluation criteria and then integrated into the q-ROFDOSM for the prioritisation of alternatives on the basis of the weighted criteria. In the second phase, a case study regarding the MCDM problem of coronavirus disease 2019 (COVID-19) vaccine distribution was performed. The purpose was to provide fair allocation of COVID-19 vaccine doses. A decision matrix based on an intersection of 'recipients list' and 'COVID-19 distribution criteria' was adopted. The proposed methods were evaluated according to systematic ranking assessment and sensitivity analysis, which revealed that the ranking was subject to a systematic ranking that is supported by high correlation results over different scenarios with variations in the weights of criteria.

Multicriteria decision-making based on distance measures and knowledge measures of Fermatean fuzzy sets

Fermatean fuzzy sets are more powerful than fuzzy sets, intuitionistic fuzzy sets, and Pythagorean fuzzy sets in handling various problems involving uncertainty. The distance measures in the fuzzy and non-standard fuzzy frameworks have got their applicability in various areas such as pattern analysis, clustering, medical diagnosis, etc. Also, the fuzzy and non-standard fuzzy knowledge measures have played a vital role in computing the criteria weights in the multicriteria decision-making problems. As there is no study concerning the distance and knowledge measures of Fermatean fuzzy sets, so in this paper, we propose some novel distance measures for Fermatean fuzzy sets using t-conorms. We also discuss their various desirable properties. With the help of suggested distance measures, we introduce some knowledge measures for Fermatean fuzzy sets. Through numerical comparison and linguistic hedges, we establish the effectiveness of the suggested distance measures and knowledge measures, respectively, over the existing measures in the Pythagorean/Fermatean fuzzy setting. At last, we demonstrate the application of the suggested measures in pattern analyis and multicriteria decision-making.

Use of (Q)SAR genotoxicity predictions and fuzzy multicriteria decision-making for priority ranking of ethoxyquin transformation products

Ethoxyquin (EQ; 6-ethoxy-2,2,4-trimethyl-1,2-dihydroquinoline) has been used as an antioxidant in feed for pets and food-producing animals, including farmed fish such as Atlantic salmon. In Europe, the authorization for use of EQ as a feed additive was suspended, due to knowledge gaps concerning the presence and toxicity of EQ transformation products (TPs). Recent analytical studies focusing on the detection of EQ TPs in farmed Atlantic salmon feed and fillets reported the detection of a total of 27 EQ TPs, comprising both known and previously not described EQ TPs. We devised and applied an in silico workflow to rank these EQ TPs according to their genotoxic potential and their occurrence data in Atlantic salmon feed and fillet. Ames genotoxicity predictions were obtained applying a suite of five (quantitative) structure-activity relationship ((Q)SAR) tools, namely VEGA, TEST, LAZAR, Derek Nexus and Sarah Nexus. (Q)SAR Ames genotoxicity predictions were aggregated using fuzzy analytic hierarchy process (fAHP) multicriteria decision-making (MCDM). A priority ranking of EQ TPs was performed based on combining both fAHP ranked (Q)SAR predictions and analytical occurrence data. The applied workflow prioritized four newly identified EQ TPs for further investigation of genotoxicity. The fAHP-based prioritization strategy described here, can easily be applied to other toxicity endpoints and groups of chemicals for priority ranking of compounds of most concern for subsequent experimental and mechanistic toxicology analyses.

An intuitionistic fuzzy entropy approach for supplier selection

Due to apparent flexibility of Intuitionistic Fuzzy Set (IFS) concepts in dealing with the imprecision or uncertainty, these are proving to be quite useful in many application areas for a more human consistent reasoning under imperfectly defined facts and imprecise knowledge. In this paper, we apply notions of entropy and intuitionistic fuzzy sets to present a new fuzzy decision-making approach called intuitionistic fuzzy entropy measure for selection and ranking the suppliers with respect to the attributes. An entropy-based model is formulated and applied to a real case study aiming to examine the rankings of suppliers. Furthermore, the weights for each alternative, with respect to the criteria, are calculated using intuitionistic fuzzy entropy measure. The supplier with the highest weight is selected as the best alternative. This proposed model helps the decision-makers in better understanding of the weight of each criterion without relying on the mere expertise.

An assessment of site suitability for marina construction in Istanbul, Turkey, using GIS and AHP multicriteria decision analysis

Marinas play a key role in sea transportation and tourism. The problem of an insufficient marina capacity has revealed in terms of sea traffic due to the demographic structure and increasing tourism potential of Istanbul which is the biggest metropolitan city of Turkey and has around 600-km-long coastline. Therefore, the study area is mainly focused on the Marmara Sea shoreline of Istanbul. Rather than traditional methods, a rapid and cost-effective solution which considers natural and urban environment conditions is essential to satisfy the need for a marina site selection. Thanks to the latest improvements in geographic information systems, it is convenient to perform location selection analysis of marinas taking advantages of geology, land use, demography and accessibility data sets. The goal of this study is to define the areas that are appropriate for building marinas, with the use of topographic and demographic data in a present shoreline applying analytical hierarchy process multicriteria decision-making method. In this study, erosion, landslide, tsunami, land use, geologically hazardous areas, transfer lines, sea traffic data, neighbourhood scale population, age patterns and house income data have been used. Analytical hierarchy process method is used to give a weight to each data set, and a grading system has been developed for the area selection of marinas. The result maps of the analysis that show study area as classified into four categories from good to not suitable are presented. It is possible to create a decision support system for upper scale plans that enable authorities to perform analysis accurately, cost and time effectively using the proposed methodology that integrates multiple data sets with different scales and types.

Multicriteria Evaluation of Classical Swine Fever Control Strategies Using the Choquet Integral

An outbreak of the highly contagious animal disease classical swine fever (CSF) requires the selection of an optimal control strategy. The choice of a control strategy is a decision process depending on different aspects. Besides epidemiology, economic and ethical/social aspects must be taken into account. In this study, multicriteria decision-making (MCDM) was used to evaluate six control strategies for two regions with different farm densities. A strategy including only the minimum EU control measures and the traditional control strategy based on preventive culling were compared to alternative control strategies using emergency vaccination and/or rapid PCR testing ('emergency vaccination', 'test to slaughter', 'test to control' and 'vaccination in conjunction with rapid testing'). The MACBETH approach was used in order to assess the three main criteria (epidemiology, economics and ethical/social aspects). Subcriteria with both quantitative and qualitative performance levels were translated into a normalized scale. The Choquet integral approach was adopted to obtain a ranking of the six CSF control strategies based on the three main criteria, taking interactions into account. Three different rankings of the importance of the main criteria, which were to reflect the potential perceptions of stakeholders, were examined. Both the region under investigation and the ranking of the main criteria had an influence on the 'best' choice. Alternative control strategies were favourable to the minimum EU control and the traditional control measures independent of the farm density. Because the choice of the 'best' control strategy does not solely depend on the epidemiological efficiency, MCDM can help to find the best solution. Both MACBETH and the Choquet integral approach are feasible MCDM approaches. MACBETH only needs a qualitative evaluation and is therefore a comparatively intuitive approach. The Choquet integral does not only take the importance of the criteria into account but also the interaction between them.