Failure prioritization and control using the neutrosophic best and worst method

Groundwater potential assessment based on GIS-based Best-Worst Method (BWM) and Step-Wise Weight Assessment Ratio Analysis (SWARA) Method

In this study, the most suitable areas in terms of groundwater potential within the borders of the adjacent area of Sivas Municipality (Sivas/Turkey) were determined with the help of Geographic Information System (GIS)-based Best-Worst Method (BWM) and Step-Wise Weight Assessment Ratio Analysis (SWARA) methods. Slope, drainage density, Topographic Position Index (TPI), lineament density, lithology, soil types, land use, geomorphology, and rainfall criteria were selected to determine groundwater potential areas. These criteria were weighted with the help of BWM, SWARA, and BWM-SWARA methods and the Groundwater Potential Index (GPI) was calculated according to the weighted linear combination method. According to the calculated GPI values, the groundwater potential of the study area was represented as "excellent," "very good," "good," "moderately good," "low," and "very low." According to all three methods, areas in the "excellent" class constituted 10.99%, 8.40%, and 11.16% of the study area, respectively, while areas in the "very low" class covered 8.33%, 7.98%, and 9.04% of the study area, respectively. The linear correlation coefficient (R²) values of the BWM, SWARA, and BWM-SWARA methods were calculated as 0.80, 0.82, and 0.75, respectively, while the area under the curve (AUC) values were determined as 0.83, 0.79, and 0.81, respectively. These results showed that the accuracy of the model was "very good" overall. As a result, groundwater potential mapping created for the study area will contribute to better development of groundwater resources and water management planning.

COVID-19 safe campus evaluation for universities by a hybrid interval type-2 fuzzy decision-making model

The fight against the COVID-19 pandemic, which has affected the whole world in recent years and has had devastating effects on all segments of society, has been one of the most important priorities. The Turkish Standards Institution has determined a checklist to contribute to developing safe and clean environments in higher education institutions in Turkey and to follow-up on infection control measures. However, this study is only a checklist that makes it necessary for decision-makers to make a subjective evaluation during the evaluation process, while the need to develop a more effective, systematic framework that takes into account the importance levels of multiple criteria has emerged. Therefore, this study applies the best-worst method under interval type-2 fuzzy set concept (IT2F-BWM) to determine the importance levels of criteria affecting the "COVID-19 safe campus" evaluation of universities in the context of global pandemic. A three-level hierarchy consisting of three main criteria, 11 sub-criteria, and 58 sub-criteria has been created for this aim. Considering the hierarchy, the most important sub-criterion was determined as periodic disinfection. The high contribution of the interval-valued type-2 fuzzy sets in expressing the uncertainty in the decision-makers' evaluations and the fact that BWM provides criterion weights with a mathematical optimization model that produces less pairwise comparisons and higher consistency are the main factors in choosing this approach. Simple additive weighting (SAW) has also been injected into the IT2F-BWM to determine the safety level of any university campus regarding COVID-19. Thus, decision-makers will be better prepared for the devastating effects of the pandemic by first improving the factors that are relatively important in the fight against the pandemic. In addition, a threshold value will be determined by considering all criteria, and it will prepare the ground for a road map for campuses. A case study is employed to apply the proposed model, and a comparison study is also presented with the Bayesian BWM to validate the results of the criteria weights.

Sustainable Manufacturing Evaluation Based on Enterprise Industry 4.0 Technologies

Most studies in recent decades focused on transforming linear economics into circular through recovering and remanufacturing the products. Circular Economies (CE) aim to minimize the usage of resources by utilizing the waste in production as new or raw materials. Interconnectivity between parties in the industrial system provides decision-makers with rich information and anticipation of failure. Industry 4.0 technologies (I4.0) allow for handling such issues, protecting the environment by utilizing resources efficiently, and restructuring the industry to be smarter as well. This paper contributes to achieving cleaner production (CP), CE, and social for manufacturers through the linkage between 6R methodology with new technologies of I4.0 such as Blockchain technology (BCT) and big data analytical technology (BDA). In this paper, the authors proposed a Multi-criteria decision-making (MCDM) decision framework based on the best-worst method (BWM), Decision-Making trial and evaluation laboratory (DEMATEL), Technique for order of preference by similarity to ideal solution (TOPSIS), and Complex Proportional Assessment (COPRAS). The authors contributed to addressing the weaknesses and problems of these subjective MCDM methods through the cooperation of the neutrosophic theory with the usage of MCDM methods in this work. In the first stage, all criteria that influence sustainable manufacturer selection are specified using literature research on this topic. BWM-based neutrosophic theory was combined to get the criteria’s weights with the aid of DEMATEL-based neutrosophic to obtain the least and best criteria used in BWM in the second stage. The optimal sustainable manufacturer was selected based on TOPSIS and COPRAS under neutrosophic theory in the third and fourth stages, respectively. Furthermore, a case study performed indicated manufacturer 2 (A2) is an optimal sustainable manufacturer in two ranking methods otherwise, manufacturer 4 (A4) is the worst sustainable manufacturer. The contribution of this work is to propose a hybrid MCDM with an uncertainty theory of neutrosophic for sustainable manufacturer selection based BDA-BCT with 6R. Sensitivity analyses were carried out to show the decision’s flexibility in various scenarios. Finally, the consequences for management viewpoints were considered.

A holistic FMEA approach by fuzzy-based Bayesian network and best–worst method

Failure mode and effect analysis (FMEA) is a risk analysis tool widely used in the manufacturing industry. However, traditional FMEA has limitations such as the inability to deal with uncertain failure data including subjective evaluations of experts, the absence of weight values of risk parameters, and not considering the conditionality between failure events. In this paper, we propose a holistic FMEA to overcome these limitations. The proposed approach uses the fuzzy best–worst (FBWM) method in weighting three risk parameters of FMEA, which are severity (S), occurrence (O), and detection (D), and to find the preference values of the failure modes according to parameters S and D. On the other side, it uses the fuzzy Bayesian network (FBN) to determine occurrence probabilities of the failure modes. Experts use a procedure using linguistic variables whose corresponding values are expressed in trapezoidal fuzzy numbers, and determine the preference values of the failure modes according to parameter O in the constructed BN. Thus, the FBN including expert judgments and fuzzy set theory addresses uncertainty in failure data and includes a robust probabilistic risk analysis logic to capture the dependence between failure events. As a demonstration of the approach, a case study was conducted in an industrial kitchen equipment manufacturing facility. The results of the approach have also been compared with existed methods demonstrating its robustness.

An Integrated Approach of Best-Worst Method (BWM) and Triangular Fuzzy Sets for Evaluating Driver Behavior Factors Related to Road Safety

Driver behavior plays a major role in road safety because it is considered as a significant argument in traffic accident avoidance. Drivers mostly face various risky driving factors which lead to fatal accidents or serious injury. This study aims to evaluate and prioritize the significant driver behavior factors related to road safety. In this regard, we integrated a decision-making model of the Best-Worst Method (BWM) with the triangular fuzzy sets as a solution for optimizing our complex decision-making problem, which is associated with uncertainty and ambiguity. Driving characteristics are different in different driving situations which indicate the ambiguous and complex attitude of individuals, and decision-makers (DMs) need to improve the reliability of the decision. Since the crisp values of factors may be inadequate to model the real-world problem considering the vagueness and the ambiguity, and providing the pairwise comparisons with the requirement of less compared data, the BWM integrated with triangular fuzzy sets is used in the study to evaluate risky driver behavior factors for a designed three-level hierarchical structure. The model results provide the most significant driver behavior factors that influence road safety for each level based on evaluator responses on the Driver Behavior Questionnaire (DBQ). Moreover, the model generates a more consistent decision process by the new consistency ratio of F-BWM. An adaptable application process from the model is also generated for future attempts.