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Several studies using Friction Stir Welding (FSW) on dissimilar Aluminium and Copper weld configurations and the mechanical and microstructure characteristics, as well as the identification of intermetallic phases that appear in the weld region, have been conducted. Indeed, earlier several studies had been conducted in both lap and butt FSW joint designs. Even so, Al–Cu lap welding has received further attention than butt–welding, which has attracted only a few studies thus far. The studies ended with varying findings and were unable to reach high strengths despite the fact that very few studies addressed tool placement factors. The present investigation starts with butt–welding of AA1050 and pure Cu sheet by employing the FSW procedure. Experimental trails were designed using the L16 array (Taguchi’s technique) with distinct combinations of tool spinning speed, tool offset, welding speed, and plunge depth. Weld parameters such as compressive strength (CS), ultimate tensile strength (UTS), and average hardness at nugget zone (AHNZ) were evaluated using three different optimization methodologies, i.e. the WASPAS method, Alibaba and the Forty Thieves algorithm, and the Taguchi–WASPAS method, to find out the best weld constraint arrangement for maximizing the welded joint mechanical characteristics. ANOVA test was also conducted, which revealed that welding speed is the most significant and influential FSW factor for Al–Cu butt–welding using FSW. Finally, confirmatory tests were piloted to check the consistency of the predicted FSW parameters. [ABSTRACT FROM AUTHOR]

Hazardous waste treatment facilities are modified and specialized facilities constructed to safely deal with hazardous waste resources, which pose important risks to human health and the situation due to their physical, chemical, and biological properties. In this manuscript, we compute the novel technique of propositional picture fuzzy 2-tuple linguistic (PPF2-TL) set, where the grades in PPF2-TL sets are linked with each other with the help of parameters. Further, some algebraic operational laws and Aczel-Alsina operational laws for PPF2-TL values are presented. Moreover, we describe the technique of PPF2-TL Aczel-Alsina power averaging (PPF2-TLAAPA) operator, PPF2-TL Aczel-Alsina power weighted averaging (PPF2-TLAAPWA) operator, PPF2-TL Aczel-Alsina power geometric (PPF2-TLAAPG) operator, PPF2-TL Aczel-Alsina power weighted geometric (PPF2-TLAAPWG) operator, and their basic properties. Additionally, we evaluate the model of technique for order of preference by similarity to the ideal solution (TOPSIS) method and weighted aggregates sum product assessment (WASPAS) method by using the initiated operators and measures. Further, we resolve the problem of hazardous waste treatment facilities by using the technique of multi-attribute decision-making (MADM) method to enhance the worth of the proposed theory. Finally, we illustrate some examples for comparing the initiated ranking values with some existing ranking techniques to show the supremacy and flexibility of the proposed ranking techniques. [ABSTRACT FROM AUTHOR]

One of the key service elements of travel companies operating in the road transportation sector are passenger buses. Deciding to purchase a new bus requires managers to consider various aspects, complicating the decision-making process. In this context, implementing Multi-Criteria Decision Making (MCDM) methodologies in such decision problems can provide more accurate and effective solutions. The aim of this study is to investigate the new bus purchase decision processes of travel companies operating in Turkey with MCDM methods and to determine the most suitable bus model. Ten major bus models were analyzed in the study using four different MCDM approaches based on four major criteria and 16 sub-criteria. The weights of the criteria used in the evaluation of the relevant decision problem were determined by the Entropy method. Moreover, WASPAS, EDAS, and Gray Relational Analysis were utilized separately to discover the best suitable alternative. Furthermore, the rankings generated by these several MCDM approaches were compared using Spearman Rank Correlation Analysis in the study, and they were found to be highly consistent. The study provides several key recommendations for academic research and industry practice moving forward. [ABSTRACT FROM AUTHOR]

Young adult literature (YAL) is a relatively new and developing field both as an art form and as an educational tool. Although it has grown in popularity and recognition over the past 50 years, YAL still struggles for academic legitimacy. To raise its status, scholars argue that research should focus more on understanding student learning outcomes rather than textual analysis. By examining how YAL affects student engagement and understanding within the classroom, educators and researchers can demonstrate its value as a meaningful subject within educational research. Additionally, as the body of high-quality YAL develops, there is significant potential for professional development for educators, allowing them to connect with adolescent experiences and concerns. This article encourages schools to implement book review programs that use YAL to foster conversation around important topics such as body image, relational aggression, and sexual identity. Such discussions deepen teachers' empathy and insight into the challenges facing today's adolescents and foster an environment where students feel understood and supported. By embracing YAL not only as literature, but as a tool for understanding adolescent development, academic institutions can further legitimize its place in education and in the classroom. YAL has evolved to include contemporary themes such as technology, social media and online interactions that resonate with today's teenagers. Since the 1990s, YAL has increasingly incorporated computers, the Internet and digital communication into its storylines, reflecting the changing ways young people communicate and connect. Interestingly, many of these novels mix digital themes with the supernatural, including ghosts and otherworldly figures, underscoring the complexity of online relationships. The "ghost" in cyberspace often symbolizes the twin desires for both freedoms from physical limitations and the need for real, embodied connections. These supernatural elements are seen metaphorically, capturing the tension between distorted digital communication and the desire for real human connections. Although dystopian undertones sometimes accompany these stories, YAL is often optimistic, portraying technology as a powerful tool for inclusiveness, tolerance, and fostering community. This optimistic outlook on technology, despite its challenges, holds that digital communication has the potential to create positive relationships and includes spaces that reflect and embrace the diversity of modern adolescent experiences. [ABSTRACT FROM AUTHOR]

This study investigates how AI technologies can improve the candidate selection process. It will also use the WASPAS method to analyze the criteria used during the selection stages, aiming to optimize the effectiveness of the process. The article employed a two-phase methodological approach to enhance candidate selection analysis. The CRITIC Method assigned weights to criteria, while the WASPAS Method ranked alternatives based on these weights. A sensitivity analysis, specifically for the WASPAS method, further assessed its robustness and consistency, providing a comprehensive view of the decision-making process. Twelve experienced professionals from various sectors, all decision-makers, participated in the research. As a result of the CRITIC method C5 (31.45%) 'Data Confidentiality' is the most important criteria, followed by C1 (25.92%) 'Justice'. Furthermore, in the selection process using AI with the WASPAS method, statements A14 and A15 stand out in the final stage, automating the identification of the most suitable candidates for the position and carefully evaluating them. Meanwhile, A9, in the cognitive and behavioral testing stage, is crucial for identifying promising candidates based on objective results, ensuring an accurate fit between candidate and position and increasing the efficiency of the selection process. [ABSTRACT FROM AUTHOR]

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In this paper, a type of variable step size LMS named weighted zero attracting variable step size (WZAVSSLMS) is equipped with the control algorithm for governing DSTATCOM. In WZAVSSLMS, the ability of zero attracting nature to a variable step size (VSSLMS) for improving convergence rate is observed. WZAVSSLMS algorithm-based fundamental extractor is developed to identify the fundamental quantity of the load current for a three-phase four-wire DSTATCOM with non-linear load in this work. This type of variable step size LMS has provided a concession for quick convergence rate and steady-state error. Precision and convergence of responses are accomplished by the learning rate control. As a result of these features over conventional LMS, the authors choose WZAVSSLMS based approach in well-known DSTATCOM system to compensate power quality issues. The fundamental extraction from the distorted load current constitutes the main divergences in the proposed study. Further, the Finite control set model predictive control (FCS MPC) of the distribution static compensator (DSTATCOM) using Weighted Aggregated Sum Product Assessment Solution (WASPAS) method is implemented. [ABSTRACT FROM AUTHOR]

The study aimed to assess the performance of electric buses that are used in public transportation, and produced and sold in Türkiye. To achieve this, multi-criteria decision-making techniques, the Entropy and WASPAS methods, were employed. The Entropy method was used to calculate the weights of the criteria, and the WASPAS method was used to rank the performance of the electric buses. During the analysis, the most important performance criteria were identified to be maximum torque and battery capacity, while the lowest performance criterion was found to be maximum speed. According to the results, the Otokar Kent Electra was the highest performing electric bus in terms of entropy weights, and the Temsa MD9 ElectriCITY was the best performer according to equal weights. On the other hand, the Otokar Doruk Electra was found to be the worst performing bus in both weight calculations. [ABSTRACT FROM AUTHOR]

The purpose of this study is to compare financial performance of the Islamic banks in the COVID-19 Era. The performance evaluation was conducted on seven Islamic countries between 2017 and 2021. Entropy Method was utilized to choose the best performance evaluation criteria affecting the Islamic banks’ performance and Waspas Method was used to determine the best performing Islamic country. The results indicate that ‘liquid assets to short-term liabilities’ is the best performance criteria for Islamic banks during the ‘whole period’, ‘no pandemic period’ and ‘pandemic period’. Additionally, while the Islamic country with the best performance was Bangladesh during ‘no pandemic period’, Turkey was the leading Islamic country for both ‘whole period’ and ‘pandemic period’.

Due to the manufacturing sector's severe negative impacts on sustainable development, sustainable manufacturing is gaining more momentum than ever. Despite the advantages of sustainable manufacturing, academic literature resources report that practitioners still face several challenges while implementing sustainable manufacturing. To eliminate such challenges, numerous mitigation strategies have been proposed, including those that identify Industry 4.0 technologies as a key factor. However, current studies are generally more focused on the application of Industry 4.0 technologies/smart manufacturing in sustainable manufacturing; most fail to provide an in-depth understanding of how these technologies might mitigate the existing adoption challenges of sustainable manufacturing. In this study, the key challenges of sustainable manufacturing are identified through literature review and analyzed with MCDM tools such as the Best-Worst Method and WASPAS method. The results suggest that governmental challenge demonstrates the greatest weight in the final ranking, followed by technological and organizational challenges. Among the sub-challenges, "lack of support from the government in the form of regulations / policies" and "absence of subsidies and incentives" display the most weight. Further, a framework has been proposed to map the collected challenges with relevant mitigating smart manufacturing technologies to bridge the gap remaining from existing studies. Finally, this study contributes to the new field of approaching smart manufacturing as a mitigating strategy for sustainable manufacturing implementation through highlighting the implications and recommendations. [ABSTRACT FROM AUTHOR]

The purpose of this study is to compare financial performance of the Islamic banks in the COVID-19 Era. The performance evaluation was conducted on seven Islamic countries between 2017 and 2021. Entropy Method was utilized to choose the best performance evaluation criteria affecting the Islamic banks' performance and Waspas Method was used to determine the best performing Islamic country. The results indicate that 'liquid assets to short-term liabilities' is the best performance criteria for Islamic banks during the 'whole period', 'no pandemic period' and 'pandemic period'. Additionally, while the Islamic country with the best performance was Bangladesh during 'no pandemic period', Turkey was the leading Islamic country for both 'whole period' and 'pandemic period'. [ABSTRACT FROM AUTHOR]

Globally, industries are focusing on green habits, with world leaders demanding net zero carbon; clean energy is considered an attractive and viable option. The Internet of things (IoT) is an emerging technology with potential opportunities in the clean energy domain for quality improvement in production and management. Earlier studies on IoTs show evidence that direct adoption of such digital technology is an ordeal and incurs adoption barriers that must be prioritized for effective management. Motivated by the claim, in this paper, the authors attempt to prioritize the diverse adoption barriers with the support of the newly proposed Fermatean fuzzy-based decision framework. Initially, qualitative rating information is collected via questionnaires on barriers and criteria from the circular economy (CE). Later, these are converted to Fermatean fuzzy numbers used by integrated approaches for decision processes. A regret scheme is put forward for determining CE criteria importance, and the barriers are prioritized by using a novel ranking algorithm that incorporates the WASPAS formulation and experts' personal choices during rank estimation. The applicability of the developed framework is testified via a case example. Sensitivity analysis and comparison reveal the merits and limitations of the developed decision model. Results show that labor/workforce skill insufficiency, an ineffective framework for performance, a technology divide, insufficient legislation and control, and lack of time for training and skill practice are the top five barriers that hinder IoT adoption, based on the rating data. Additionally, the criteria such as cost cutting via a reuse scheme, resource circularity, emission control, and scaling profit with green habits are the top four criteria for their relative importance values. From these inferences, the respective authorities in the clean energy sector could effectively plan their strategies for addressing these barriers to promote IoT adoption in the clean energy sector. [ABSTRACT FROM AUTHOR]

Aczel-Alsina t-norm and t-conorm are a valuable and feasible technique to manage ambiguous and inconsistent information because of their dominant characteristics of broad parameter values. The main theme of this analysis is to explore Aczel-Alsina operational laws in the presence of the complex interval-valued intuitionistic fuzzy (CIVIF) set theory. Furthermore, we derive the theory of aggregation frameworks based on Aczel-Alsina operational laws for managing the theory of CIVIF information. The CIVIF Aczel-Alsina weighted averaging (CIVIFAAWA), CIVIF Aczel-Alsina ordered weighted averaging (CIVIFAAOWA), CIVIF Aczel-Alsina hybrid averaging (CIVIFAAHA), CIVIF Aczel-Alsina weighted geometric (CIVIFAAWG), CIVIF Aczel-Alsina ordered weighted geometric (CIVIFAAOWG) and CIVIF Aczel-Alsina hybrid geometric (CIVIFAAHG) operators are proposed, and their well-known properties and particular cases are also detailly derived. Further, we derive the theory of the WASPAS method for CIVIF information and evaluate their positive and negative aspects. Additionally, we demonstrate the multi-attribute decision-making (MADM) strategy under the invented works. Finally, we express the supremacy and dominancy of the invented methods with the help of sensitive analysis and geometrical shown of the explored works.

Artificial intelligence (AI) is a well-known and reliable technology that enables a machine to simulate human behavior. While the major theme of AI is to make a smart computer system that thinks like a human to solve awkward problems, machine learning allows a machine to automatically learn from past information without the need for explicit programming. In this analysis, we aim to derive the idea of Aczel–Alsina aggregation operators based on an intuitionistic fuzzy soft set. The initial stage was the discovery of the primary and critical Aczel–Alsina operational laws for intuitionistic fuzzy soft sets. Subsequently, we pioneer a range of applicable theories (set out below) and identify their essential characteristics and key results: intuitionistic fuzzy soft Aczel–Alsina weighted averaging; intuitionistic fuzzy soft Aczel–Alsina ordered weighted averaging; intuitionistic fuzzy soft Aczel–Alsina weighted geometric operators; and intuitionistic fuzzy soft Aczel–Alsina ordered weighted geometric operators. Additionally, by utilizing certain key information, including intuitionistic fuzzy soft Aczel–Alsina weighted averaging and intuitionistic fuzzy soft Aczel–Alsina weighted geometric operators, we also introduce the theory of the weighted aggregates sum product assessment method for intuitionistic fuzzy soft information. This paper also introduces a multi-attribute decision-making method, which is based on derived operators for intuitionistic fuzzy soft numbers and seeks to assess specific industrial problems using artificial intelligence or machine learning. Finally, to underline the value and reasonableness of the information described herein, we compare our obtained results with some pre-existing information in the field. This comparison is supported by a range of numerical examples to demonstrate the practicality of the invented theory. [ABSTRACT FROM AUTHOR]

Using a multi-criteria decision support method (WASPAS) to analyze and rank alternatives, this article proposes a method to assist in the selection of helicopter models that are the most suitable for police air activity in the State of Rio de Janeiro. A robust technical basis for defining the essential requirements of an aircraft is established, and solutions that can ensure the effective and safe execution of missions are indicated. Helicopter models were evaluated by considering predefined criteria, and the weights of these criteria were attributed using a questionnaire that was administered to pilots and aerostatic operators of Public Air Units (UAP) in several states of the federation. As a result of the evaluation of the 15 helicopter models used by police services in the State of Rio de Janeiro, the modeling with the WASPAS method ranked the Sikorsky UH-60 (Black Hawk) model in first place, the Leonardo AW 139 model in second place, and the Bell 412 model in third place. Based on the available data, we suggest that a comparative study integrating the Entropy and CRITIC methods be conducted to measure the weights of the criteria associated with the application of other multi-criteria techniques, such as COMET, MACAB, SPOTIS, VIKOR, SAPEVO, and PROMETHEE. [ABSTRACT FROM AUTHOR]

Globally, industries are focusing on green habits, with world leaders demanding net zero carbon; clean energy is considered an attractive and viable option. The Internet of things (IoT) is an emerging technology with potential opportunities in the clean energy domain for quality improvement in production and management. Earlier studies on IoTs show evidence that direct adoption of such digital technology is an ordeal and incurs adoption barriers that must be prioritized for effective management. Motivated by the claim, in this paper, the authors attempt to prioritize the diverse adoption barriers with the support of the newly proposed Fermatean fuzzy-based decision framework. Initially, qualitative rating information is collected via questionnaires on barriers and criteria from the circular economy (CE). Later, these are converted to Fermatean fuzzy numbers used by integrated approaches for decision processes. A regret scheme is put forward for determining CE criteria importance, and the barriers are prioritized by using a novel ranking algorithm that incorporates the WASPAS formulation and experts’ personal choices during rank estimation. The applicability of the developed framework is testified via a case example. Sensitivity analysis and comparison reveal the merits and limitations of the developed decision model. Results show that labor/workforce skill insufficiency, an ineffective framework for performance, a technology divide, insufficient legislation and control, and lack of time for training and skill practice are the top five barriers that hinder IoT adoption, based on the rating data. Additionally, the criteria such as cost cutting via a reuse scheme, resource circularity, emission control, and scaling profit with green habits are the top four criteria for their relative importance values. From these inferences, the respective authorities in the clean energy sector could effectively plan their strategies for addressing these barriers to promote IoT adoption in the clean energy sector.

Artificial intelligence (AI) is a well-known and reliable technology that enables a machine to simulate human behavior. While the major theme of AI is to make a smart computer system that thinks like a human to solve awkward problems, machine learning allows a machine to automatically learn from past information without the need for explicit programming. In this analysis, we aim to derive the idea of Aczel–Alsina aggregation operators based on an intuitionistic fuzzy soft set. The initial stage was the discovery of the primary and critical Aczel–Alsina operational laws for intuitionistic fuzzy soft sets. Subsequently, we pioneer a range of applicable theories (set out below) and identify their essential characteristics and key results: intuitionistic fuzzy soft Aczel–Alsina weighted averaging; intuitionistic fuzzy soft Aczel–Alsina ordered weighted averaging; intuitionistic fuzzy soft Aczel–Alsina weighted geometric operators; and intuitionistic fuzzy soft Aczel–Alsina ordered weighted geometric operators. Additionally, by utilizing certain key information, including intuitionistic fuzzy soft Aczel–Alsina weighted averaging and intuitionistic fuzzy soft Aczel–Alsina weighted geometric operators, we also introduce the theory of the weighted aggregates sum product assessment method for intuitionistic fuzzy soft information. This paper also introduces a multi-attribute decision-making method, which is based on derived operators for intuitionistic fuzzy soft numbers and seeks to assess specific industrial problems using artificial intelligence or machine learning. Finally, to underline the value and reasonableness of the information described herein, we compare our obtained results with some pre-existing information in the field. This comparison is supported by a range of numerical examples to demonstrate the practicality of the invented theory.

Using a multi-criteria decision support method (WASPAS) to analyze and rank alternatives, this article proposes a method to assist in the selection of helicopter models that are the most suitable for police air activity in the State of Rio de Janeiro. A robust technical basis for defining the essential requirements of an aircraft is established, and solutions that can ensure the effective and safe execution of missions are indicated. Helicopter models were evaluated by considering predefined criteria, and the weights of these criteria were attributed using a questionnaire that was administered to pilots and aerostatic operators of Public Air Units (UAP) in several states of the federation. As a result of the evaluation of the 15 helicopter models used by police services in the State of Rio de Janeiro, the modeling with the WASPAS method ranked the Sikorsky UH-60 (Black Hawk) model in first place, the Leonardo AW 139 model in second place, and the Bell 412 model in third place. Based on the available data, we suggest that a comparative study integrating the Entropy and CRITIC methods be conducted to measure the weights of the criteria associated with the application of other multi-criteria techniques, such as COMET, MACAB, SPOTIS, VIKOR, SAPEVO, and PROMETHEE.

Multiple attribute decision-making (MADM) methods are commonly employed to assist decisions for selecting the best alternative according to conflicting criteria in complex decision situations. Although MADM methods have proven to be very useful, in some dynamic decision cases they may cause faulty results due to the "rank reversals" problem. Numerous MADM methods such as Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), and several others well-known methods proven to have rank reversal problems. In this paper, we study the rank reversal problem for a recent MADM method that is known as Weighted Aggregated Sum Product Assessment (WASPAS). As far as we know, there is no study that considers the rank reversal problem in the WASPAS. In this paper, rank reversal problems in WASPAS are analyzed empirically by considering different types of rank reversals. After detailed computational experiments, we show that rank reversal problems also exist in WASPAS when classical normalization techniques are utilized. We also show through extensive computational experiments by using different problem instances that rank reversal problems can be avoided in WASPAS when modified Max and Max–Min normalization techniques are used. [ABSTRACT FROM AUTHOR]

Supply chain management emerged as the ultimate management strategy to ensure the competitive advantages of companies in their markets. Suppliers are considered as inevitable sources of external risks in modern supply chains. In this respect, resonance is essential for the ability to adapt in resonance to disturbances and to restore in choosing suppliers. As suppliers of critical resources are vulnerable, choosing better suppliers to create resilience, and thereby reducing the risks in the supply chain as a whole. In recent years, emphasis has been placed on supply chain resilience and resilient suppliers, but few studies have been conducted on the evaluation and selection of resilient suppliers with multi-criteria decision making models. The main purposes of this study are a broad review of the literature on the resilient factor, factorization, efficiency of key factors in the reliance of suppliers and the ranking of resilient suppliers using the combined approach of SWARA and WASPAS. For this purpose, after a comprehensive review of Literature interview with the experts of petrochemical upstream industry, six key factors and overall resilience of suppliers were identified in eighteen factors. Then the weight of the dimensions was determined by using the SWARA method. The output of the method showed that supplier accountability and key performance factors were the most important factors in assessing the resilience of suppliers. Using the supporting method, five resilient suppliers were evaluated based on six dimensions and the final ranking of suppliers was determined. With this ranking, the industry will be a major step towards improving supply chain and increasing suppliers' resilience to address disruptions and risks, improve supply and achieve competitive advantage and satisfy the consumers' needs. [ABSTRACT FROM AUTHOR]

Bu çalışmanın amacı Türk bankacılık sektöründe faaliyette bulunan mevduat bankalarının performansını SD ve WASPAS yöntemleri kullanarak değerlendirmektir. Bu amaçla analiz için aktif büyüklüğü açısından özel sermayeli mevduat bankaları arasında ilk dört sırada yer alan bankalar seçilmiştir. Çalışmada 2014-2018 yıllarını kapsayan dönemde özel sermayeli bankalara ait sekiz finansal gösterge (sermaye yeterlilik oranı, kredinin mevduata dönüş oranı, donuk alacaklar, likidite oranı, ortalama aktif karlılığı, ortalama özkaynak karlılığı, faiz dışı gelirler ve personel giderleri) karar kriteri olarak kullanılmıştır. SD yöntemi kullanılarak gerçekleştirilen analiz neticesinde seçilen özel bankaların en önemli performans kriterleri 2014 ve 2016 yıllarında ortalama aktif karlılığı, 2015 yılında sermaye yeterlilik oranı, 2017 yılında ortalama özkaynak karlılığı ve 2018 yılında ise donuk alacaklardır. WASPAS performans sıralamasına ilişkin bulgulara göre ise analize dahil edilen tüm yıllarda en iyi performans gösteren özel sermayeli bankanın Akbank T.A.Ş. olduğu tespit edilmiştir. Bununla beraber, 2014, 2016 ve 2017 (2015 ve 2016) yıllarında finansal açıdan performansı en düşük olan bankanın ise Türk Ekonomi Bankası A.Ş (Yapı ve Kredi Bankası A.Ş.) olduğu tespit edilmiştir.

In the current era, various business process activities require the application of information technology. Decision making is one of the business process activities that can be helped by the existence of technology. Determining poor families is a complex problem because many criteria must be met to determine poor families and deserve assistance in dealing with poverty. Therefore, a decision support system is made using the Weighted Aggregated Sum Product Assessment (WASPAS) method with predetermined criteria and then implemented using Visual Basic .Net. The sample used was 89 residents of Cibangkong Village who were registered to apply for government assistance. The results of the study obtained 12 very poor households, 28 poor households, 38 poor vulnerable households and 11 non-poor households. With a decision support system, the WASPAS method in processing data will be more effective and efficient because of the process of evaluating and making quick reports. Employees only enter population data, criteria data and population assessment data, while the process of calculating and reporting is done by the system.

In this paper, concerning advantages of relative preference relation (RPR) and interval type-2 fuzzy sets (IT2FSs), a new IT2F-RPR based on multi-attributive border approximation area comparison (MABAC) method is introduced for determining the critical path of production projects under group decision-making process. IT2FSs are more capable than classic fuzzy sets in coping with uncertainty and providing more degree of freedom to model the uncertain conditions. Also, the RPR is better than defuzzification because it does not lose the fuzzy message, and the method avoids pairwise comparisons. In fact, the MABAC method is extended by the RPR for reducing the time complexity. Furthermore, an extended MABAC method is developed under IT2FSs to better address the uncertainty. Weights of decision makers (DMs) are computed by introducing a new IT2F-RPR-based MABAC concept in the proposed new decision model. Also, experts' opinions are aggregated by using weights of the DMs. Moreover, weights of criteria are determined based on a new extended weighted aggregated sum-product assessment (WASPAS) method by using the DMs or experts' opinions on the importance of criteria and weights of DMs. As a matter of fact, weights of DMs are determined by a new version of extended MABAC method, and weights of criteria are specified using a new version of WASPAS method for the first time in the literature. Finally, a case study about aircraft maintenance planning is solved to address the calculation process and applicability of the proposed method. Computational results of the presented model are accurate and suitable for real-life situations. [ABSTRACT FROM AUTHOR]

GSM operators serve a huge amount of customers and this causes difficulties in meeting the customer requirements. Selection of the GSM operator having the best quality of service is a multicriteria decision making problem under vague and imprecise evaluations rather than exact numerical evaluations. Pythagorean fuzzy sets (PFS) let experts assign membership and nonmembersip degrees in a wider area than it is in ordinary intuitionistic fuzzy sets. In this paper, we employ Pythagorean fuzzy sets for the extension of WASPAS method. WASPAS is a multicriteria method integrating simple additive weighting and weighted product methods. Single valued PFS are used in the proposed WASPAS method. We illustrate the application of the proposed Pythagorean fuzzy WASPAS through a selection problem among communication firms. [ABSTRACT FROM AUTHOR]

Health-care waste (HCW) contains numerous hazardous, infectious, and radioactive contents that may cause serious health issues for society and drastic environmental problems. Therefore, it is crucial to dispose of HCW safely and appropriately. Four commonly used HCW disposal methods are incineration, steam sterilization, microwave, and landfill disposal. To select an appropriate HCW disposal method, there are several conflicting criteria. Furthermore, when information is lacking due to uncertainty and criteria weighing conditions are also unknown, the decision to choose an appropriate HCW disposal method becomes more challenging. So, the objective of this paper is to introduce a novel integrated multi-criteria group decision-making (MCGDM) approach to evaluate HCW disposal methods in which incomplete information is quantified using intuitionistic fuzzy sets (IFSs), unknown criteria weights are evaluated using the maximizing deviation method, and the evaluation of HCW disposal methods is done by applying the Archimedean t -norm and t -conorm-based interactive intuitionistic fuzzy weighted aggregated sum product assessment (WASPAS) method. Herein, the Archimedean t -norm and t -conorm cover a wide range of t -norms and t -conorms, including the Algebraic, Einstein, Hamacher, and Frank, etc., by adopting prescribed combinations of additive generators. The proposed MCGDM approach has been applied to evaluate the above-listed four HCW disposal methods for a case study conducted by Mishra et al. (2020) in the context of India. A sensitivity analysis is performed to examine the impact of changes in the parameters involved in the proposed MCGDM approach. A comparative study is also performed with some well-known multi-criteria decision making(MCDM) and MCGDM methods to demonstrate the consistency and stability of the proposed approach. Findings suggest that the newly developed MCGDM approach is more general, flexible, and provides realistic results to evaluate HCW disposal methods under uncertainty and unknown criteria weight information. • The paper proposes an Archimedean t-norm and t-conorm based IF-WASPAS method with unknown criteria weights to evaluate HCW disposal techniques. • The method applies IFS to quantify uncertainty while unknown criteria weights are evaluated using the maximizing deviation method. • Method has been applied to evaluate HCW disposal alternatives in context of India. • Sensitivity and comparative analyses are also conducted to examine the consistency and stability of the method. • The proposed MCDM approach is general,flexible, and provides realistic results under uncertainty and unknown criteria weights. [ABSTRACT FROM AUTHOR]

Electric vehicle charging station location (EVCSL) is critical to promoting the electric vehicle industry and how it is evaluated is typically a MCDM issue. In this paper, an applicable multi-criteria group decision support model is developed to evaluate the electric vehicle charging station location. To do this, we used normal wiggly hesitant fuzzy set to describe the fuzzy decision-making information, which can further dig the potential uncertainty. Considering that the experts' preference can lead to significant changes in the results, k-means clustering method is used to group the experts in different fields. Moreover, the improved correlation coefficient and standard deviation (CCSD) method is used to determine the criterion weights, and considering that prospect theory can reflect the mental behavior of experts and the simplicity and efficiency of weighted aggregated sum product assessment (WASPAS) method, a combined ranking model is proposed to evaluate the alternatives comprehensively. The procedure of the model is given in detail, and the example of electric vehicle charging station location evaluation is employed to demonstrate our results. Comparative analyses and discussion show that the proposed model has powerful effectiveness, performs better than other existing methods, and largely extends the applicability of the method with theoretical and practical implications. [ABSTRACT FROM AUTHOR]

The assessment of Third-Party Logistics (3PL) provider becomes an important issue for enterprises trying to achieve operational efficiency and customer service improvement as well as capital expenditure and logistics costs reduction. It can be said that evaluation and selection of an appropriate 3PL provider is a kind of Multi-Criteria Decision-Making (MCDM) problem. Uncertainty is an unavoidable part of information in the decision-making process. Interval Type-2 Fuzzy Sets (IT2FSs) are very flexible to model the uncertainty of the MCDM problems. In this study, a new integrated approach based on the criteria Importance Through Inter-criteria Correlation (CRITIC) and Weighted Aggregated Sum Product assessment (WASPAS) methods is proposed to evaluate 3PL providers with IT2FSs. In the proposed approach, objective weights resulted from the CRITIC method are combined with subjective weights expressed by decision-makers (dms) to determine more realistic weights for criteria. A computational study is performed to illustrate the proposed approach and the applicability of it. In addition, a sensitivity analysis is carried out using different sets of criteria weights to demonstrate the stability of the proposed approach. The results show the stability of ranking results and prove the efficiency of the proposed approach to handle MCDM problems with IT2FSs.

The assessment of Third-Party Logistics (3PL) provider becomes an important issue for enterprises trying to achieve operational efficiency and customer service improvement as well as capital expenditure and logistics costs reduction. It can be said that evaluation and selection of an appropriate 3PL provider is a kind of Multi-Criteria Decision-Making (MCDM) problem. Uncertainty is an unavoidable part of information in the decision-making process. Interval Type-2 Fuzzy Sets (IT2FSs) are very flexible to model the uncertainty of the MCDM problems. In this study, a new integrated approach based on the CRiteria Importance Through Inter-criteria Correlation (CRITIC) and Weighted Aggregated Sum Product ASsessment (WASPAS) methods is proposed to evaluate 3PL providers with IT2FSs. In the proposed approach, objective weights resulted from the CRITIC method are combined with subjective weights expressed by decision-makers (DMs) to determine more realistic weights for criteria. A computational study is performed to illustrate the proposed approach and the applicability of it. In addition, a sensitivity analysis is carried out using different sets of criteria weights to demonstrate the stability of the proposed approach. The results show the stability of ranking results and prove the efficiency of the proposed approach to handle MCDM problems with IT2FSs. [ABSTRACT FROM AUTHOR]

• A nonlinear decision approach is put forward for CVS. • Attributes are transformed to polynomial space from the linear fuzzy space. • Weights of attributes are determined by using CRITIC in the non-linear space. • Personalized ranking is obtained with nonlinear WASPAS and rank fusion. • Usefulness is validated via a cloud vendor selection case example. Cloud vendor selection (CVS) is a complex decision-making problem, which actively adheres to human behavior/cognition. The complex nature of the problem is due to personal biases/hesitation, trade-offs among attributes, uncertainty in rating, and the nonlinear relationship among cloud vendors and associated attributes. In recent times, researchers started paying more attention to user/expert behavior, which led to non-linear decision-making. Most of the extant decision models for CVS considered the linear form of decision-making, which is not realistic due to expert opinions' complexity and dynamism. Motivated by the claim, in this paper, a non-linear decision approach is put forward for CVS. Likert scale rating is adopted for rating cloud vendors based on some attributes, which are transformed to polynomial space from the linear fuzzy space. After this, weights of attributes are determined by using CRITIC in the non-linear space. Following this, cloud vendors are ranked in a personalized fashion using the proposed algorithm that encompasses the WASPAS procedure and rank fusion schemes. Finally, a case study is exemplified to validate the usefulness of the decision approach. Comparison and sensitivity analysis showcases the efficacy and robustness of the developed approach. [ABSTRACT FROM AUTHOR]

Aczel-Alsina t-norm and t-conorm are a valuable and feasible technique to manage ambiguous and inconsistent information because of their dominant characteristics of broad parameter values. The main theme of this analysis is to explore Aczel-Alsina operational laws in the presence of the complex interval-valued intuitionistic fuzzy (CIVIF) set theory. Furthermore, we derive the theory of aggregation frameworks based on Aczel-Alsina operational laws for managing the theory of CIVIF information. The CIVIF Aczel-Alsina weighted averaging (CIVIFAAWA), CIVIF Aczel-Alsina ordered weighted averaging (CIVIFAAOWA), CIVIF Aczel-Alsina hybrid averaging (CIVIFAAHA), CIVIF Aczel-Alsina weighted geometric (CIVIFAAWG), CIVIF Aczel-Alsina ordered weighted geometric (CIVIFAAOWG) and CIVIF Aczel-Alsina hybrid geometric (CIVIFAAHG) operators are proposed, and their well-known properties and particular cases are also detailly derived. Further, we derive the theory of the WASPAS method for CIVIF information and evaluate their positive and negative aspects. Additionally, we demonstrate the multi-attribute decision-making (MADM) strategy under the invented works. Finally, we express the supremacy and dominancy of the invented methods with the help of sensitive analysis and geometrical shown of the explored works., Competing Interests: The authors declare there are no competing interests., (©2023 Fang et al.)

The main goal of green supply chain management (GSCM) is to reduce the negative environmental impacts in all activities and stages of a supply chain. Evaluation of suppliers in a supply chain according to environmental criteria can help us to achieve this goal of GSCM. Since this evaluation usually comprises some alternatives and some criteria, green supplier selection (GSS) could be considered as a multi-criteria decision-making (MCDM) problem. To handle the uncertainty of information in an MCDM problem, the theory of fuzzy sets is an effective tool. Interval type-2 fuzzy sets (IT2FSs), which are characterized by an interval membership function, is very flexible to model the uncertainty of the MCDM problems. In this study, a new integrated approach based on Weighted Aggregated Sum Product Assessment (WASPAS) method, is proposed to deal with multi-criteria group decision-making problems with IT2FSs. This approach is based on the operators of IT2FSs, some modifications in the classical WASPAS method and a new procedure for calculation of criteria weights. In the procedure of calculation of criteria weights, we combine the subjective weights expressed by decision-makers with objective weights resulted from an entropy method to obtain more realistic weights. To show the applicability of the proposed approach in the real-world MCDM problems, a green supplier selection problem is used. We perform a sensitivity analysis with different weights of criteria and different values of method's parameters to show the stability of the proposed approach. This analysis shows that combining the subjective and objective weights can help to increase the stability of the proposed approach with different weights of criteria. A comparison is also made between the results of the proposed approach and some existing methods for validating the proposed approach. This analysis shows that the proposed approach is efficient and well consistent with the other methods. [ABSTRACT FROM AUTHOR]

Determination of laser cutting conditions for simultaneous improvement of different performance characteristics is of great practical importance. In this study an approach for multi-criteria assessment of process performance characteristics in CO2 laser cutting of mild steel was presented. Laser cutting experiment was conducted based on Taguchi's L9 experimental design by varying the laser power, cutting speed, assist gas pressure and focus position at three levels. Multi-criteria assessment was performed by using the weighted aggregated sum product assessment (WASPAS) method while considering kerf width, dross, surface roughness, material removal rate and assist gas consumption as assessment criteria. [ABSTRACT FROM AUTHOR]

• A new approach to selecting improvement projects (IPs) is described. • IPs are evaluated and prioritized taking into account a proposed set of criteria. • An extension of WASPAS method using the Ordered Fuzzy Numbers (OFNs) is proposed. • In this approach, the uncertainty and trends of project assessments are taken into account. This paper proposes a new approach to selecting improvement projects (IPs). It allows the evaluation and prioritization of projects taking into account a proposed set of criteria as a key criteria for processes continuous improvement. The novelty of the research is the proposition of a OFN-WASPAS method (the WASPAS method, which uses the Ordered Fuzzy Numbers (OFNs)), in the selection of improvement projects. The advantage of the proposed method is its ability to provide project assessments in the form of fuzzy numbers or qualitative assessments together with the description of likely direction of assessment change in the nearest future. This is possible due to the use of orientation in OFNs. The approach verification is presented on the basis of the case study considered five projects constituting the portfolio of IPs selected, using the 19 most important subcriteria proposed within the 9 main criteria. The group AHP method is used to calculate the criteria weights. The conducted research demonstrated that the proposed approach to IPs selection is, to a large extent, applicable in an enterprise that operates per the CI concept. The resulting process improvement requires the implementation of many improvement projects. This approach handles the key aspects of decision making in a consistent and rational way and can be especially useful for portfolio analysis with projects using the latest technologies and implemented in frequently changing environmental conditions. [ABSTRACT FROM AUTHOR]