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The purpose of this article is to present the idea of a T-spherical hesitant fuzzy set associated with probability and to develop an extended multi-attributive border approximation area comparison (MABAC) method under probabilistic T-spherical hesitant fuzzy (Pt-SHF) settings.

The authors define some basic operational laws for Pt-SHF sets (Pt-SHFSs) and a comparison method of two probabilistic T-spherical hesitant fuzzy numbers (Pt-SHFNs) is proposed. Moreover, some Pt-SHF aggregation operators and the multi-attributive border approximation area comparison (MABAC) method are established under Pt-SHF scenario to solve group decision making problems.

The developed Pt-SHF MABAC method for multi-attribute group decision making (MAGDM) can overcome the drawbacks of conventional MABAC method and limitations for decision makers, which they face while providing their assessment concerning any object.

Clearly, this paper is devoted to MABAC method, MAGDM and probabilistic T-spherical hesitant fuzzy set theory.

The approach established can be used in a variety of scenarios, including decision making, engineering, and economics. An explanatory example is illustrated which shows the superiority and effectiveness of our proposed technique.

If a T-spherical fuzzy MAGDM problem under the probabilistic scenario needs to be evaluated, the involvement of probabilities in fuzzy system will be lost because of no information. This work fills a gap in literature by establishing the notion of probabilistic t-spherical hesitant fuzzy set to deal with the ambiguity, uncertainty in decision making problems.

Since conducting agile strategies provides sustainable passenger satisfaction and revenue by replacing applied policies with more profitable ones rapidly, the focus of this study is to evaluate agile attributes for managing low-cost carriers (LCCs) operations by means of resources and competences based on dynamic capabilities built on resource-based view (RBV) theory and to achieve sustainable competitive advantage in a volatile and dynamic air transport environment. LCCs in Turkey are also evaluated in this study since the competition among LCCs is high to gain market share and they can adapt quickly to all kinds of circumstances.

Two well-known Multi-Criteria Decision-Making Methods (MCDM) named as the Stepwise Weight Assessment Ratio Analysis (SWARA) and multi-attributive border approximation area comparison (MABAC) methods by employing Picture fuzzy sets (PiFS) are employed to determine weight of agile attributes and superiority of LCCs based on agile attributes in the market, respectively. To check the consistency and robustness of the results for the proposed approach, comparative and sensitivity analysis are performed at the end of the study.

While the ranking orders of agile attributes are Strategic Responsiveness (AG1), Financial Management (AG4), Quality (AG2), Digital integration (AG3) and Reliability (AG5), respectively, LCC2 is selected as the best agile airline company in Turkey with respect to agile attributes. SWARA and MABAC method based on PiFS is appropriate and effective method to evaluate agile attributes that has important reference value for the airline companies in aviation industry.

The findings of this study will support managers in the airline industry to conduct airline operations more flexibly and effectively to take sustainable competitive advantage in unexpected and dynamic environment.

To the author' best knowledge, this study is the first developed to identify the attributes necessary to increase agility in LCCs. Thus, as a systematic tool, a framework is developed for the implementation of agile attributes to achieve sustainable competitive advantage in the airline industry and presented a roadmap for airline managers to deal with crises and challenging situations by satisfying customer and increasing competitiveness.

Platform-based enterprises, as micro-entities in the platform economy, have the potential to effectively promote the low-carbon development of both supply and demand sides in the supply chain. Therefore, this paper aims to provide a multi-criteria decision-making method in a probabilistic hesitant fuzzy environment to assist platform-type companies in selecting cooperative suppliers for carbon reduction in green supply chains.

This paper combines the advantages of probabilistic hesitant fuzzy sets (PHFS) to address uncertainty issues and proposes an improved multi-criteria decision-making method called PHFS-DNMEREC-MABAC for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Within this decision-making method, we enhance the standardization process of both the DNMEREC and MABAC methods by directly standardizing probabilistic hesitant fuzzy elements. Additionally, a probability splitting algorithm is introduced to handle probabilistic hesitant fuzzy elements of varying lengths, mitigating information bias that traditional approaches tend to introduce when adding values based on risk preferences.

In this paper, we apply the proposed method to a case study involving the selection of carbon emission reduction collaboration suppliers for Tmall Mart and compare it with the latest existing decision-making methods. The results demonstrate the applicability of the proposed method and the effectiveness of the introduced probability splitting algorithm in avoiding information bias.

Firstly, this paper proposes a new multi-criteria decision making method for aiding platform-based enterprises in selecting carbon emission reduction collaboration suppliers in green supply chains. Secondly, in this method, we provided a new standard method to process probability hesitant fuzzy decision making information. Finally, the probability splitting algorithm was introduced to avoid information bias in the process of dealing with inconsistent lengths of probabilistic hesitant fuzzy elements.

The purpose of this paper is to identify critical equipment by dynamically ranking them in interval-valued intuitionistic fuzzy (IVIF) circumstances. Accordingly, the main drawbacks of the conventional failure mode and effects analysis (FMEA) are eliminated. To this end, the authors have presented the interval-valued intuitionistic fuzzy condition-based dynamic weighing method (IVIF-CBDW).

To realize the objective, the authors used the IVIF power weight Heronian aggregation operator to integrate the data extracted from the experts’ opinions. Moreover, the multi-attributive border approximation area comparison (MABAC) method is applied to rank the choices and the IVIF-CBDW method to create dynamic weights appropriate to the conditions of each equipment/failure mode. The authors proposed a robust FMEA model where the main drawbacks of the conventional risk prioritization number were eliminated.

To prove its applicability, this model was used in a case study to rank the equipment of a HL5000 crane barge. Finally, the results are compared with the traditional FMEA methods. It is indicated that the proposed model is much more flexible and provides more rational results.

In this paper, the authors have improved and used the IVIF power weight Heronian aggregation operator to integrate information. Furthermore, to dynamically weigh each equipment (failure mode), they presented the IVIF-CBDW method to determine the weight of each equipment (failure mode) based on its equipment conditions in the O, S and D criteria and provide the basis for the calculation. IVIF-CBDW method is presented in this study for the first time. Moreover, the MABAC method has been performed, to rank the equipment and failure mode. To analyze the information, the authors encoded the model presented in the robust MATLAB software and used it in a real sample of the HL5000 crane barge. Finally, to evaluate the reliability of the model presented in the risk ranking and its rationality, this model was compared with the conventional FMEA, fuzzy TOPSIS method, the method of Liu and the modified method of Liu.

Due to several unique characteristics, such as high tensile strength, low extensibility, high frictional resistance, biodegradability, eco-friendliness and cheapness, Jute ranks second just after cotton with respect to its worldwide consumption and production. To overcome the difficulties of the existing Jute grading procedure, this paper aims to focus on the application of decision-making trial and evaluation laboratory (DEMATEL) and multi-attributive border approximation area comparison (MABAC) methods for evaluation of 10 Tossa Jute fiber lots based on strength, defects, root content, color, fineness and bulk density properties.

The DEMATEL method divides all the six physical properties of Jute fiber into cause and effect groups. The most influencing property is also identified. On the other hand, the considered Jute fiber lots are ranked using MABAC method along with the identification of the strengths and weaknesses of each of them.

This combined approach would provide a more scientific and realistic way of Jute grading and evaluation based on various properties of the considered Jute fiber lots. The positions of the superior and the inferior Jute lots perfectly match with those as identified by the earlier researchers.

It is concluded that the adopted combined decision-making tool can be effectively applied for grading and evaluation of other natural fibers with diverse heterogeneous physical properties.

Selecting the most appropriate healthcare waste treatment technology (HCWTT) is an uncertain and complex decision-making problem because there exist more than one alternative and many conflicting qualitative and quantitative criteria. However, the use of fuzzy and comparative values, instead of specific crisp values, provides more accurate results, so that the alternatives may be evaluated in accordance with hesitant human nature. The purpose of this paper is to select the best HCWTT using a hesitant fuzzy linguistic term set (HFLTS).

Five main criteria were identified for HCWTT selection, such as economic, social, environmental, technical and ergonomic criteria. In total, 19 sub-criteria were examined, and the hierarchy of the criteria was formed. The criteria weights were determined using the multi-criteria hesitant fuzzy linguistic term set (MC-HFLTS). The selection processes of incineration (A₁), steam sterilization (A₂), microwave (A₃) and landfill (A₄) alternatives were carried out using the multi-attributive ideal-real comparative analysis (MAIRCA) and multi-attributive border approximation area comparison (MABAC) methods. In the comparative analyses, Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and technique for order preference by similarity to an ideal solution (TOPSIS) methods were used.

The comparison of the results of the MABAC and MAIRCA methods with the results of VIKOR and TOPSIS methods indicated that A₂ (steam sterilization) alternative was the best one and produced the same ranking of the technology alternatives (A₂ > A₃ > A₁ > A₄). As a result, the study concluded that these methods can be successfully used for HCWTT selection problems.

To the best of the authors’ knowledge, MC-HFLTS has not been used to select HCWTT in the existing literature. For the first time, MC-HFLTS&MAIRCA and MC-HFLTS&MABAC approaches were used in order to choose the best treatment method for healthcare waste under the effect of multiple conflicting hierarchical criteria. It has been provided that MABAC and MAIRCA select alternative choices by taking into consideration the hierarchical criteria. Unlike other studies, this study also considered ergonomic criteria that are important for people working during the process of using the treatment technology.

During pre-vaccine era, pharmaceutical supplies [self-care essentials (SCEs)] have been proved to be a major deflector, protector and safety guard against novel coronavirus disease (COVID-19). Hence, the objective of the study is to provide a comprehensive socio-technological decision-making framework based on multiple criteria for selecting the suppliers of pharmaceuticals, such as SCEs, by multi-brand enterprises (distributors) in the pandemic environment.

A hybrid methodology of Bayesian best worst method (BWM) and multi-attributive border approximation area comparison (MABAC) method has been applied for carrying out the study. Bayesian BWM has been applied for computing the importance of criteria identified for the selection of SCEs' suppliers during pandemic environment and MABAC method evaluated the suppliers of the SCEs.

In the study, the authors have identified eight criteria such as disinfection and sanitization of vehicles, social conscience of suppliers, brand (Technological recognition) of SCEs and logistics and distribution network, among others, which are critical to the selection of a supplier for the supply of SCEs. The application of the proposed hybrid model revealed that lead time and quality of SCEs are of utmost concern for pharmacies in a pandemic environment. Among the ten suppliers, results showed that Suppliers 2, 4 and 5 have been ranked first for supplying hand wash, hand sanitizer and face mask, respectively.

The proposed model has helped the multi-brand distributors of pharmaceuticals in selecting suppliers during the ongoing crisis of COVID-19. In addition to that, in future the outcomes of the study would be helpful for multi-brand distributors as well as pharmacies and hospitals in selecting the best suppliers. Policy makers will be able to make and revise the policies immediately with the help of the proposed decision-making framework.

The paper makes a novel contribution towards theory with the criteria identified for selecting best suppliers during the pandemic COVID-19. Additionally, the proposed hybrid model helps multi-brand distributors of pharmaceuticals in making decisions that lead to a huge social and economic success in pandemic time.

Traditional risk assessment (RA) methodologies cannot model vagueness in risk and cannot prioritize corrective-preventive measures (CPMs) by considering effectiveness of those on risk types (RTs). These cannot combine and reflect accurately different subjective opinions and cannot be used in a linguistic manner. Risk factors (RFs) are assumed to have the same importance and interrelations between RFs are not considered. This study aims to overcome these disadvantages by combining fuzzy logic with multi-criteria decision-making in a dynamic manner.

This study proposes a novel three-stage fuzzy risk matrix-based RA integrating fuzzy decision-making trial and evaluation laboratory (F-DEMATEL) and fuzzy multi-attributive border approximation area comparison (F-MABAC). At the first stage, importance weights of RFs are computed by F-DEMATEL. At the second stage, risk degrees of RTs are computed via using fuzzy risk matrix. At the third stage, CPMs are ranked by F-MABAC. Finally, a numerical example for RA in a warehouse is given.

Results show that developing instructions for material loading or unloading is the most important CPM and severity is the most important RF for the warehouse.

This study has originality in terms of having fuzzy dynamic structure. At first, RFs are assumed to be criteria sets then, RTs are assumed to be criteria set considering their risk degrees to rank CPMs in a fuzzy manner. Risk degrees of RTs are used for weights of RTs and effectiveness of CPMs are used for performance values of CPMs.

The implementation of circular economy strategies is one of the central objectives of several governments seeking a transition toward a sustainable development. Circular economy in agriculture deals with the production of agricultural commodities making an efficient use of resources and avoiding unnecessary waste and carbon emission generation. Disruptions in the production and supply of critical agricultural products can have serious negative repercussions for firms and consumers of the food supply chain. In recent decades, disruptions generated by natural disasters such as hurricanes, thunderstorms and floods have greatly impacted social communities and industrial sectors. Supply chain risks approaches are seen to contribute key elements to address the impacts of natural disaster toward the implementation of circular economy in agriculture, helping to prevent collapses in the production and supply of food. The purpose of this paper is to study and identify flood risk drivers and their effects on the sustainability of an agriculture supply chain in connection with a circular economy strategy. By using an extended Step-wise Weight Assessment Ratio Analysis method combined with a multi-criteria decision analysis, the most essential flood drivers with a degree of importance are reported here. Then, the authors propose an Evaluation of Data based on average ASsessment method, to rank different agricultural projects that pretend to mitigate the flood risks and its impacts on crop areas. The application of this research lies within the framework of a real agricultural project founded by the European Commission Scientific Section, called RUC-APS.

The authors use management science-based tools to address circular economy in agriculture. The authors propose a multi-criteria-based methodology to assess the risks of flooding in crops areas. To validate the proposed methodology, a case example from Spain is discussed to rank different agricultural projects that pretend to mitigate the flood risks and its impacts on crop areas.

The proposed multi-criteria methodology confirmed a successful application to rank different agricultural projects that pretend to mitigate the flood risks and its impacts on crop areas. Organizations and firms in the agricultural business can use the methodology to identify risks drivers and to detect the best projects to mitigate the highest impacts of flooding risks in crops areas.

The authors use supply chain risks approaches to address the impacts of natural disaster on the implementation of circular economy in agriculture. The authors propose a robust multi-criteria-based methodology to assess the risks of flooding in crops areas and we used to determine the best mitigating projects to face flooding risks on crop areas.