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The Circular Economy of the Automotive Industry's Sustainable Supply Chain in the Case of Lithium-Ion Batteries is pioneering in environmental protection and ecological resource utilization. In addition to solving environmental problems, this method provides economic benefits by reducing the need for raw materials and lowering manufacturing costs. However, introducing a circular economy approach in the lithium-ion battery supply chain has numerous risks and challenges. This study addresses these challenges by crafting a framework that encapsulates the risks involved. It identifies the risks that evolving circular economy strategies might bring to the lithium-ion battery supply chain through an integrated Gray Delphi–DEMATEL–ANP method. Furthermore, this work introduces the Gray Degree of Possibility to unveil worst-case scenarios in risk analysis and extends it into zero-sum Game Theory. The study then formulates an improved zero-sum game model to determine optimal strategies for mitigating these risks. The numerical analysis reveals that, according to the proposed methodology, Environmental Pollution Risk emerges as the most critical, with a definite weight of 0.1525. This is followed by the Support Program Deficiency Risk at 0.1452 and the Improper Waste Management Risk at 0.1372. [ABSTRACT FROM AUTHOR]

Circular economy and sustainable growth are closely linked as they both aim to reconcile economic development with environmental considerations. The circular economy provides a framework and set of principles for achieving sustainable growth. By adopting circular practices, such as resource efficiency, recycling, and product life extension, economic activities can become more sustainable and contribute to long-term growth. In today's world, consumers have high expectations for companies to be accountable for the environmental and social impact of their products and services. The implementation of Total Quality Management (TQM) in manufacturing can contribute to sustainable growth by enhancing quality, reducing waste, and increasing efficiency. This study proposes a novel methodological framework to establish a comprehensive association between Juran's ten quality principles using a mixed-method sequential approach with an integrated Machine Learning Group Decision-Making (MLGDM) and Interpretive Structural Modeling (ISM)-DEMATEL approach. The framework involves using the MLGDM approach to select the optimal number of experts to develop contextual relationships among the principles. This framework is designed to address the challenge of determining the appropriate number of experts to involve in the decision-making process. Involving too few experts can limit the generalizability of the results, while having too many experts can lead to a high degree of inconsistency and make it challenging to reach a consensus. The MLGDM portion of our framework provides a systematic approach to overcome this challenge and helps supply chain managers and academicians implement quality practices in their organizations. Moreover, although several studies have explored the implementation of TQM practices, there is still a lack of a systematic framework that can fully incorporate Juran's quality principles. To fill this gap, the ISM-DEMATEL approach was then used to explore the causal relationships between these principles. Practitioners from the industry were asked to identify contextual associations among variables, which facilitated a better understanding of these principles. Our results suggest that "Build awareness," "Set goals for improvement," and "Provide training" are strategic requirements for successful TQM implementation, while "Carry out projects to solve problems," "Organize to reach the goals," and "Keep score" are tactical requirements. Furthermore, "Communicate results," "Report progress," "Give recognition," and "Maintain momentum" are operational necessities for TQM implementation. The present study represents a significant step forward in giving a new direction to Juran's ten quality principles and providing a holistic picture to decision-makers. [ABSTRACT FROM AUTHOR]

Due to intensified sensitivity towards environmental protection and social responsibility, the concept of sustainable development has been widely mentioned in various industries. Circular economy has received great attention as it meets environmental and social requirements. It contributes to better resource efficiency and a more sustainable economic development by means of its principles to gain strategic advantages. Choosing suitable supplier in view of the circular and sustainability aspects are of great importance for all firms. Thus, the purpose of this study is to integrate the Pythagorean fuzzy information-based fairly aggregation operators, the CRiteria Importance Through Intercriteria Correlation (CRITIC), the PIvot Pairwise RElative Criteria Importance Assessment (PIPRECIA) and the Measurement of Alternatives and Ranking based on COmpromise Solution (MARCOS) methods to assess and rank the sustainable suppliers in circular supply chains. The proposed study classified into four phases. First, some fairly aggregation operators for Pythagorean fuzzy numbers (PFNs) are introduced that include the concept of proportional distribution to achieve a fair treatment to the membership and non-membership degrees of PFNs. Second, a combination of PF-CRITIC and PF-PIPRECIA-based weight-determining formula is introduced to compute the relative significances of the considered sustainability indicators. Third, the PF-MARCOS based on proposed PF-fairly aggregation operator and combined weight-determining tool is proposed to rank the sustainable circular suppliers (SCSs). Further, an illustrative case study of SCS selection is discussed to prove the practicability and effectiveness of the PF-MARCOS method. For this purpose, this study considers an inclusive set of four key criteria and 25 sub-criteria in the related literature concerning SCS selection process. The assessment criteria are categorized into economic, circular, environmental and social dimensions based on the expert's opinions. The result shows the significance degrees of economic, circular, environmental and social dimensions are 0.1964, 0.277, 0.3237 and 0.203, respectively and the SCS-4 (M4) should be chosen as the most suitable choice among others for the given data. The reliability and robustness of the presented methodology are examined by means of the comparative and sensitivity analyses. The findings of this study make a significant contribution to the SCS process by providing a novel decision support system from uncertainty perspective. [ABSTRACT FROM AUTHOR]

This paper aims to provide a comparative evaluation of public transport options for two cities of different scales. The study utilizes the Best Worst Method (BWM) based on the TOPSIS and also conventional economic assessment techniques. This comprehensive evaluation takes into account both monetary and nonmonetary impacts of public transport. The results of the study indicate that in terms of conventional economic assessment, Bus Rapid Transit (BRT) and tram systems are profitable for Kskisebir (small & medium-sized city), while all systems are profitable for Istanbul (metropolis city). These results suggest that public transport options for small & medium-sized cities are limited and the wrong public transport system selection can lead to significant economic losses. Additionally, the study emphasizes the importance of public transport for metropolis cities, as each system has diverse and crucial economic effects. According to the findings of BWM, the research highlights relationship between capacity, technology and economy-budget. Also, the study shows the positive impact of rail systems on the urban image-prestige, as many governments prefer rail systems due to their positive association with urban development. Conversely, BRT systems are negatively affected in terms of urban image due to their indirect relationship with buses. The vehicle cost comparison between rail system vehicles and BRT vehicles further supports this observation. Ranking and the best option were listed as BRT, metro, LRT, monorail and tram for two city-sized, according to Best Worst-based TOPSIS results. In conclusion, this study provides valuable insights into the comparative evaluation of public transport systems for two cities of different scales. It emphasizes the need for careful consideration of monetary and non-monetary impacts when selecting the most suitable public transport option. The findings can assist policymakers and urban planners in making informed decisions regarding public transport infrastructure. [ABSTRACT FROM AUTHOR]

Type 2 intuitionistic fuzzy sets (T2IFS) appear to be an attractive and potentially useful tool for coping with uncertainty and inaccurate data in today's uncertain and ambiguous world. Consequently, it is frequently employed in decision-making (DM) issues. In the DM procedure, it is essential to aggregate the information provided by the experts. The Aggregation operators (AOs) are effective information combining tools. Therefore, this study aims on developing geometric and interactive geometric AOs on T2IFSs. To demonstrate the efficacy of the proposed operator, the optimal e-learning tool selection MCDM problem is solved. The results demonstrate that the proposed interactive AO outperforms existing arithmetic AOs and is applicable to all DM problems. [ABSTRACT FROM AUTHOR]

Purpose: Sustainable building design relies heavily on building parts, with crucial consideration for climate and environmental impact. Due to numerous criteria and diverse alternatives, employing multi-criteria decision-making (MCDM) to choose the best alternative is essential. Yet, relevant criteria and suitable MCDM methods for life cycle-based building planning still need to be determined. This study highlights prevalent environmental criteria and offers guidance on MCDM approaches for sustainable building parts. Methods: This study introduces an innovative approach by integrating life cycle assessment and MCDM. This provides comprehensive decision support for planners. A systematic literature review identifies environmental criteria for building parts and is validated in expert workshops. Thus, the relevance of criteria across the building life cycle is established. Furthermore, the study analyzes MCDM approaches in the built environment. From this, the study employs and evaluates the Analytical Network Process (ANP) and Analytical Hierarchy Process (AHP) in a case study. Thereby, it offers insights into effective decision-making methodologies for sustainable building practices. Results: This research categorizes environmental criteria for building parts and buildings into emissions, energy, resources, and circularity. Among 26 building part-related criteria, the global warming potential is highlighted. While the AHP is widely used in MCDM, a standardized method in planning processes is yet to emerge. Applying the ANP and AHP reveals similar rankings for the best and worst alternatives in a case study focused on selecting the optimal ceiling structure. Ribbed or box slab ceiling constructions are favored over reinforced concrete and composite timber-concrete constructions. Conclusions: This study presents a novel method for life cycle-based MCDM challenges, identifying key environmental criteria. While material correlations exist, evaluating building parts demands simultaneous consideration of multiple criteria. Future research aims to compare further MCDM methods regarding their applicability, transparency, and ranking to enhance decision-making in sustainable construction. These investigations are essential for refining decision-making processes in the built environment, ensuring effective and transparent sustainability planning approaches. [ABSTRACT FROM AUTHOR]

The unpredictable nature of renewable energy sources, such as solar and wind, raises concerns about consistent power supply based on a single type. Green hydrogen presents a promising solution to mitigate the environmental effects of fossil fuels and control the unpredictability of renewable energy sources. Yet, developing green hydrogen systems mandates immense financial commitments, vitally stressing the importance of determining the most appropriate locations before constructing them. As such, the primary purpose of this paper was to propose an initial spatial analysis and prioritize the most suitable locations for installing solar-based green hydrogen systems in Tunisia. For this reason, an exhaustive literature survey was conducted to develop a GIS-based MCDM approach, taking into account technical, topography, environmental, and accessibility criteria. Then, EDAS, ARAS, MOORA, and COPRAS techniques were used to rank the most promising sites resulting from the previous stage. The study revealed that sites of high suitability, primarily in the southeastern and southwestern parts of Tunisia, spanned a total area of 1591 km2 (0.78%). Among such regions, Sfax, Monastir, and Sousse appeared to hold the top ranking as the most suitable locations for solar-based hydrogen. The "most suitable" sites had a hydrogen potential capacity of 9910 Mt per year, with Sfax, Monastir, and Sousse accounting for 14.5% of that capacity. By tapping into this immense potential and embracing the green hydrogen industry, Tunisia has the opportunity to lead in renewable energy adoption. This move could help ensure energy security, reduce dependence on imports, stimulate economic growth, and steer the nation toward a more sustainable and prosperous future. [ABSTRACT FROM AUTHOR]

Despite the variety of technologies available for the valorization of biowaste, this fraction of municipal solid waste is usually disposed of in landfills and dumpsites in undeveloped and developing countries. The selection of technological strategies compatible with the reality of each location represents an essential role in the success and continuity of the operation of the facilities. The best strategy will result from combining different environmental, economic, social, and technical dimensions, confronted with the interests of the stakeholders and the legal and policy contexts. Due to the complexity of different criteria, alternatives, and interests, a Multi-criteria Decision-making (MCDM) methodology is recommended to ensure the quality of the decision-making process. This study presents a systematic review of publications that have applied multi-criteria analysis as a decision support tool for the selection of biowaste management strategies. The different stages of management are discussed separately, highlighting the change in paradigms over time. It was found that less than a hundred studies have addressed it so far, with Asia contributing to most publications. However, the timeline shows that the number of publications is increasing, with multi-criteria models representing great potential to support decisions at any individual management stage, from preventing food waste to using by-products. Integrated multi-criteria approach that combines all stages of biowaste management, however, is still scarce in the literature. [ABSTRACT FROM AUTHOR]

Because of its excellent mechanical qualities and weldability, titanium alloy is used in many different biomedical applications. Wire electrical discharge machining may be used to machine materials with such greater strengths and intricate forms. Using Taguchi-data envelopment analysis-based ranking (DEAR) approach and zinc-diffused coated brass wire electrode to improve Titanium alloy machining was the goal of this research project. The quality metrics that were taken into consideration were surface roughness, kerf width, and material removal rate. Among the selected factors, with an error of 2.7%, the optimal configuration of input factors was determined to be 130 µs (Ton), 40 µs (Toff), 50 V (SV), 6 A (IP), and 8 kg (WT). Due to its relevance in the process of deionization, the Ton is the highest influential parameter for creating quality measurements. [ABSTRACT FROM AUTHOR]

Lung disorders involve swelling, inflammation, and muscle tightening around the airways, with symptoms such as coughing, wheezing, shortness of breath, and abnormal fluid build-up. The global prevalence of these conditions is rising, highlighting the need for extensive research to alleviate their severity and symptoms. Due to the chronic nature and recurrence of these disorders, the human body often develops immunity and side effects to certain medications. Therefore, developing novel and appropriate drug combinations is crucial. This study analyzes a dataset of lung disorder drugs, characterized by various topological indices. The structures of 16 drugs used to treat lung disorders are correlated with their physical properties using degree-based graph invariants. When considering specific attributes, the VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje) method provides an optimal ranking for each drug. The QSPR results highlight the effectiveness of this approach in drug prioritization, offering valuable insights for clinical decision-making and drug development. This methodology can enhance the strategic selection of treatments for lung disorders, leading to improved patient care and better resource allocation. [ABSTRACT FROM AUTHOR]

The concept of a hydrogen economy has received increasing attention worldwide. Hydrogen is a promising energy carrier that contributes to the development of a sustainable and reliable energy sector. Selection of the most sustainable hydrogen production technology (HPT) plays a significant role in promoting a hydrogen economy. However, it is regarded as a multi-criteria decision-making (MCDM) problem with several incompatible evaluation criteria. In this study, we develop an MCDM approach based on Fuzzy Analytic Hierarchy Process (FAHP) for prioritizing HPT alternatives, focusing on Egypt as a case study. Our contributions are twofold. First, to combat the issue of a small number of subject matter experts, a bootstrap method is employed to validate the main criteria weights in FAHP. Second, we conduct a comparative FAHP analysis from a broad perspective of the national experts from a developing country and the international experts from developed countries. Our results show that PV electrolysis is the most preferable technology option from the perspectives of both the national and international experts. Since the FAHP approach proposed is a flexible framework that can be applied to countries other than Egypt, it will support global policymakers and industry stakeholders in their decision making toward a sustainable and efficient hydrogen economy strategy. [ABSTRACT FROM AUTHOR]

Digital tools are revolutionizing patient care by enhancing the quality of care and facilitating the development of patient-oriented solutions that improve patients' well-being. Evaluating and comparing these digital tools requires the consideration of several essential key criteria, which turns it into a multicriteria decision-making (MCDM) problem. This study introduces a decision-making framework that integrates the additive ratio assessment (ARAS) method with an extension of the fuzzy weighted zero inconsistency based on the Pythagorean fuzzy environment (PyFWZIC). The framework is used to evaluate and benchmark genetic services tools used by patients based on a decision matrix formed of eight key attributes and 69 existing digital tools as alternatives grouped in five main categories. PyFWZIC is used to compute the weights of the criteria based on the opinion of three domain experts. The ARAS method is used to benchmark the digital tools and rank them for effectiveness. The findings of the study reveal that PyFWZIC effectively weighs the key criteria required for the development and benchmarking of genetic services digital tools while considering the vagueness and ambiguities of subjective opinions and eliminating inconsistencies that arise thereof. The robustness and reliability of the result is evaluated using sensitivity analysis and comparative analysis. [ABSTRACT FROM AUTHOR]

The present study introduces an innovative approach to multi-criteria decision making (MCDM) aimed at handling decision analysis involving p,qrung orthopair fuzzy (p,qROF) data, where the criteria weights are completely unknown. To achieve this objective, we formulate generalized operational rules referred to as Frank operational rules, tailored for p,qROF numbers (p,qROFNs) utilizing the Frank t-norm and t-conorm. With these newly devised operations as a foundation, we create a variety of p,qROF aggregation operators (AOs) to effectively aggregate p,qROF information. Furthermore, we examine specific instances of these operators and rigorously establish their desirable properties, including idempotency, monotonicity, boundedness, and symmetry. Subsequently, we adapt the SWARA technique to the realm of p,qROF fuzzy data and this adapted technique becomes instrumental in determining criteria weights within the proposed MCDM framework centered around proposed AOs. We furnish a descriptive example to exemplify the practicality of the developed approach. Lastly, the effectiveness and soundness of our approach are underscored through both parameter analysis and a comparative evaluation. [ABSTRACT FROM AUTHOR]

The urgent global challenges of climate change, energy security, and environmental degradation highlight the need for sustainable energy solutions. Renewable energy sources (RES) present a viable pathway towards sustainability by mitigating greenhouse gas emissions, reducing reliance on fossil fuels, and fostering economic resilience. Purpose: The purpose of this paper is to propose an advanced Multi-Criteria Decision-Making (MCDM) approach to evaluate various RES by integrating environmental, economic, technological, social acceptance, and resource availability criteria, to identify the most suitable RES for sustainable energy solutions. Methodology: The study employs a hybrid method combining Type-2 Neutrosophic Numbers (T2NN) with LOPCOW (Logarithmic Percentage Change Operator Weighting) and MAIRCA (Multi-Attributive Ideal-Real Comparative Assessment) to rank the suitability of different RES, including solar, wind, hydropower, and geothermal energy. Findings: The case study results reveal wind energy as the top-ranked alternative, supported by consistent findings across comparative methods such as COPRAS, MABAC, EDAS, and TOPSIS. Sensitivity analysis further confirms the stability of the proposed model under various scenarios. Originality: The originality of this study lies in the integration of T2NN, LOPCOW, and MAIRCA to address the limitations of traditional MCDM approaches in handling uncertainty and imprecision in data. The study demonstrates the efficacy of the proposed framework in providing a robust evaluation of RES, and its value lies in its potential to inform decision-making in the field of sustainable energy solutions. [ABSTRACT FROM AUTHOR]

Purpose: The objective of this study is to investigate the concept of "earthquake resistance" in high-rise buildings and assess the current level of structural strength in the areas where these structures are situated. The study aims to identify and implement necessary measures to enhance resilience in these areas. A comprehensive literature review was conducted to develop a conceptual framework focusing on earthquake resistance's meanings, stages and physical elements to achieve these goals. Design/methodology/approach: This study focuses on Istanbul, a city known for its high earthquake risk, specifically targeting the Atasehir district. The research utilizes the DEMATEL (Decision-Making Trial and Evaluation Laboratory) method to evaluate urban resilience parameters. Additionally, the Fuzzy TOPSIS (Preference Ranking Technique by Similarity) method is employed to analyze the location of five buildings in Atasehir, using criterion weights derived from this methodology. Findings: The findings indicate that resilience varies depending on the distance of the buffers. Moreover, the amount and quality of urban equipment in the study areas have a significant impact on the earthquake resistance level of the surrounding areas where high-rise buildings are situated. Building upon this analysis, the study suggests the implementation of measures aimed at augmenting the quantity and quality of urban facilities in the study areas, consequently enhancing urban resilience. Originality/value: The originality and value of this study lie in its examination of seismic resilience within the context of high-rise buildings and the identification of necessary measures to increase resilience in areas where these structures are prevalent. By focusing on Istanbul, a city with a high earthquake risk, and specifically selecting the Atasehir district as the study area, this research provides a comprehensive conceptual framework for understanding urban resilience and its physical components. Moreover, the study offers a fresh perspective on urban resilience by highlighting the influence of tall buildings on the surrounding areas. Ultimately, it provides practical recommendations for architects, urban planners and other stakeholders to improve regional earthquake resilience. [ABSTRACT FROM AUTHOR]

In the era of Industry X.0, manufacturers are increasingly adopting digital technologies such as robotics, big data, mobile communications, and information technology to enhance productivity and profitability. However, navigating this digital revolution presents significant challenges, particularly in selecting the most suitable technologies amidst uncertainties and the lack of precise quantitative data. Fuzzy logic, initially introduced to manage vagueness and imprecision, has proven to be an essential tool in various domains for handling these uncertainties. However, as systems and environments become more complex, traditional fuzzy logic exhibits limitations in effectively representing and managing deeper levels of uncertainty. This realization has driven the development of several extensions to fuzzy logic, designed to address challenges associated with uncertainty representation. Yet, a central question remains: Which fuzzy logic extension is the most relevant for better dealing with uncertainty? This study introduces a hybrid-fuzzy decision-making framework for technology selection, which allows for more than one representation of fuzzy information. Using a multi-expert, multi-criteria decision-making (MCDM) strategy, the study seeks to support organizations in selecting the most suitable digital technology for their operations, particularly when precise quantitative data is unavailable, and decisions must rely on expert opinions. The suggested framework is designed to handle decision-makers' hesitancy, subjectivity, and ambiguity in the decision-making process. To showcase the practical application of the proposed methodology, a case study was conducted to select appropriate technologies in the Moroccan automotive sector. The results demonstrate that, regardless of the fuzzy logic extension used, digital automation and automatic identification consistently rank among the top technologies, underscoring their importance according to most experts. Our proposed approach is particularly effective in resolving conflicts between different fuzzy logic extensions, thereby facilitating the ranking of the remaining technologies. Notably, additive manufacturing ranked third, followed by the other technologies.Article highlights: This study proposes a more comprehensive and flexible decision-making framework for managing uncertainty in technology selection. A case study from Moroccan automotive sector is provided to show the practical aspect of the proposed methodology Digital automation, automatic identification, and additive manufacturing are identified as the leading digital technologies for adoption in the Moroccan automotive sector [ABSTRACT FROM AUTHOR]

In recent times, sustainable supply chain (SSC) management has received significant attention from academics and industries. Due to its complex nature, various risk sources are involved in SSCs. Thus, evaluation of risks in SSC becomes critical in enhancing the resilience and efficiency. Accordingly, this paper develops a framework for evaluating the most influential risks in an SSC using multi-criteria decision-making method. Using a data triangulation strategy, the study finalized 20 risks from the list of 25 risks. The finalized risks are evaluated and prioritized using multi-objective optimization ratio analysis technique. The findings show that the environmental supply chain is critical for SSC as it plays a key role in enhancing SSC performance. Based on the outcomes, the study suggests that the industries should prefer closed-loop supply chain practices rather than traditional linear supply chain practices. Also, the industries must incorporate 6R (Rethink, Refuse, Reduce, Reuse, Recycle, Repair) practices in the regular industrial activities. Furthermore, the findings help academics and industrial managers to achieve sustainability and circularity in supply chains. [ABSTRACT FROM AUTHOR]

This paper explores the evolution, applications, and prospective developments of a very popular multi-criteria decision-making (MCDM) method called Measurement of Alternatives and Ranking according to COmpromise Solution Method (MARCOS). Employing an extensive bibliometric analysis, the study examines 115 pertinent papers sourced from the Scopus database spanning over the years from 2020 to 2024. This study also provides an evaluation of the methodological significance and outlines potential future directions of MARCOS method. The outcomes indicate "Sustainable supplier selection in healthcare industries using a new MCDM method: Measurement of alternatives and ranking according to COmpromise solution (MARCOS)" by Stević et al. (2020) as the most cited paper. Journals such as "Sustainability (Switzerland)", "Mathematics" and "Expert Systems with Applications" stand out among the most cited journals. "University of East Sarajevo" is an institution distinguished for its prolific research in this field. "Stević Ž." Has been identified as the most cited and published author. The most frequently used keywords are "MARCOS", "MARCOS method", and "MCDM". CRiteria Importance Through Intercriteria. Correlation (CRITIC) method is a weighting model often integrated with MARCOS method. The results of the study provide researchers and practitioners in the field of MCDM with an important insight into the current state of the MARCOS methodology, highlighted studies and potential future developments. It also provides a comprehensive overview of the importance of this method in the multi-criteria decision-making literature, shedding light on future research directions. [ABSTRACT FROM AUTHOR]

Titanium carbo-nitride (TiCN) comes under the metal nitride group which can provide a combined property of titanium carbide (TiC) and titanium nitride (TiN) coating. TiCN Coating possess an excellent mechanical and tribological, properties which leads its application in many industries particularly in the Automotive and aerospace industries. TiCN films can be very hard and strong based on the composition and synthesis process. Physical Vapor Deposition (PVD) technique is widely used for the preparation of thin film coating because of its ability to precisely control the composition and thickness of the coatings. The properties of the developed coating significantly affect by the PVD process parameters and their levels. Multi-Criteria Decision Making (MCDM) methods are widely used in many sectors for the selection of process parameters based upon some specific criteria. In the present work TiCN coatings were synthesized by using one of the most widely used PVD method called magnetron sputtering. L16 orthogonal array was used as a design of experiment for the experimental work by varying the sputtering process parameters like bias voltage, N2 flow rate and substrate to target distance. After the synthesis, the developed films were tested using atomic force microscopy (AFM), Scanning electron microscopy (SEM), and Nanoindentation. From the characterization three response parameters such as hardness (H), Modulus of elasticity (E) and surface roughness (Ra) of the coating has been considered. A hybrid method based on improved removal effects of criteria- technique for order performance by similarity to ideal solution (MEREC-TOPSIS) approach has been considered for the for the process parameters selection. The results have also been compared with other weight calculation techniques. In all the cases it is observed that experiment no 16 and 15 have got the 1st and second rank respectively. However, experiment no 1 is the last rank in all the cases. From the correlation analysis it is found a strong positive correlation between MEREC-TOPSIS and other methods. The method provides a significant advancement in the selection of optimal parameters, ensuring enhanced coating properties crucial for industrial applications [ABSTRACT FROM AUTHOR]

Identifying soil erosion-prone zones in an ungauged river basin is crucial for devising and implementing timely soil protection measures to mitigate soil degradation and protect soil quality. Soil erosion damages the fragile ecosystem, decreases soil fertility, and reduces reservoir water storage, thereby impacting food production. The prime motive of the current research work is to assess and categorize on the basis of priority the sub-watersheds (SWs) susceptible to substantial soil erosion in the Ponnaniyar River basin (an ungauged river basin) based on the morphometric parameters that impact soil erosion. To achieve this research objective, four multi-criteria decision-making (MCDM) approaches based on the outranking approach and synthesis method are adopted to facilitate the decision-making process by considering an integrated and balanced assessment of multiple complex parameters for devising effective soil conservation measures to minimize soil erosion. Cartosat-1 digital elevation model (DEM) is employed to extract eighteen morphometric parameters under linear, shape, areal, relief and hypsometric aspects. The priority of SWs obtained by different MCDM techniques is evaluated using percentage of variation and intensity of variation. The outcomes show that the MABAC method is effective in prioritizing SWs with the least percentage of variation (59.61%) and intensity of variation (4.397). It is also found to be the best method for integration with the RSS method for determining SW priority with a root sum of squares value of 43. SW1 is identified to be highly vulnerable to soil erosion with a grade average value of 1.00 followed by SW2 (3.00), SW3 (3.25) and SW13 (5.00), requiring immediate implementation of watershed planning and management measures to control the extent of soil erosion and safeguard soil resources. [ABSTRACT FROM AUTHOR]

Blockchain networks continue to gain attraction in cutting‐edge applications and mining within these networks has become increasingly popular. To get rewards, miners solve cryptographic puzzles and add new blocks to blockchain networks using the proof‐of‐work (PoW) consensus mechanism. Numerous miners opt to participate in mining pools due to the challenges of solo mining. However, selecting reputed miners for pool mining poses a significant challenge, given the decentralized nature of the blockchain system. This paper addresses this challenge by introducing a new ranking model that evaluates miners' performance and reputation through trust scores. It provides a method for optimizing pool mining performance by identifying highly reputed miners within mining pools, enhancing overall pool profitability. This endeavor necessitates the development of ranking algorithms tailored to the unique dynamics of mining pools. The research offers a meticulously designed ranking model that identifies reputed miners. We extensively evaluate the proposed model using the hyperledger blockchain framework, guaranteeing strong performance across vital metrics like block authorization time, Processing time, block creation time, validation time, and confirmation time. [ABSTRACT FROM AUTHOR]

Natural aquifers used for human consumption are among the most important resources in the world. The Locumba basin faces significant challenges due to its limited water availability for the local population. In this way, the search for possible aquifer recharge zones is crucial work for urban development in areas that have water scarcity. To evaluate this problem, this research proposes the use of the hybrid Fuzzy AHP methodology in conjunction with the TOPSIS algorithm to obtain a potential aquifer recharge map. Ten factors that influence productivity and capacity in an aquifer were implemented, which were subjected to Fuzzy AHP to obtain their weighting. Using the TOPSIS algorithm, the delineation of the most favorable areas with high recharge potential was established. The result shows that the most influential factors for recharge are precipitation, permeability, and slopes, which obtained the highest weights of 0.22, 0.19, and 0.17, respectively. In parallel, the TOPSIS result highlights the potential recharge zones distributed in the Locumba basin, which were classified into five categories: very high (13%), high (28%), moderate (15%), low (28%), and very low (16%). The adapted methodology in this research seeks to be the first step toward effective water resource management in the study area. [ABSTRACT FROM AUTHOR]

Octahedron sets, which extend beyond the previously defined fuzzy set and soft set concepts to address uncertainty, represent a hybrid set theory that incorporates three distinct systems: interval-valued fuzzy sets, intuitionistic fuzzy sets, and traditional fuzzy set components. This comprehensive set theory is designed to express all information provided by decision makers as interval-valued intuitionistic fuzzy decision matrices, addressing a broader range of demands than conventional fuzzy decision-making methods. Multi-criteria decision-making (MCDM) methods are essential tools for analyzing and evaluating alternatives across multiple dimensions, enabling informed decision making aligned with strategic objectives. In this study, we applied MCDM methods to octahedron sets for the first time, optimizing decision results by considering various constraints and preferences. By employing an MCDM algorithm, this study demonstrated how the integration of MCDM into octahedron sets can significantly enhance decision-making processes. The algorithm allowed for the systematic evaluation of alternatives, showcasing the practical utility and effectiveness of octahedron sets in real-world scenarios. This approach was validated through influential examples, underscoring the value of algorithms in leveraging the full potential of octahedron sets. Furthermore, the application of MCDM to octahedron sets revealed that this hybrid structure could handle a wider range of decision-making problems more effectively than traditional fuzzy set approaches. This study not only highlights the theoretical advancements brought by octahedron sets but also provides practical evidence of their application, proving their importance and usefulness in complex decision-making environments. Overall, the integration of octahedron sets and MCDM methods marks a significant step forward in decision science, offering a robust framework for addressing uncertainty and optimizing decision outcomes. This research paves the way for future studies to explore the full capabilities of octahedron sets, potentially transforming decision-making practices across various fields. [ABSTRACT FROM AUTHOR]

This experimental study investigates the effect of the cutting parameters of the Electrical Discharge Machining (EDM) process of Ti-6Al-4V alloy material on surface roughness (Ra), cutting time (t) and Material Removal Rate (MRR) then solve the Multiple Objective Optimization Problem using separate Multi-Criteria Decision-Making (MCDM) methods namely Entropy-Weighted TOPSIS (E-TOPSIS), MOORA, SAW, VIKOR, and WPM. Focusing on nine 3-levels variants including Operating Voltage (OV), Pulse-On Time (Ton), Pulse-Off Time (Toff), Short-Circuit Off Time (AFF), Secondary Voltage (SV), Feed Rate (WF), Tension (WT), Water Pressure (WL) and Material Cutting Speed (F). Due to the large number of variants studied, the Taguchi L27 experimental design was chosen to reduce the number of experiments while still ensuring reliability in assessing the impact of technological parameters on responses in the study. The optimization results from the different methods indicated two distinct optimal outcomes. According to the E-TOPSIS, MOORA, and SAW, the optimal result is a Ra of 3.27 μm, a t of 7.37 min, and an MRR of 7.45 mm3/min. This result suggests a balanced and harmonious optimization among all criteria. On the other hand, the figures VIKOR and WPM methods are 2.87 μm, 9.57 min, and 5.74 mm3/min, respectively. These results indicate a higher priority for certain criteria, reflected in the lower Ra, longer cutting time, and a smaller MRR in comparison to the figures for the remaining MCDM methods. The different optimal results achieved by various methods highlight that each method is suited to and excels with different sets of values. Therefore, in each specific research or production process, comparing and choosing results calculated by different methods provides a comprehensive view, aiding in making appropriate decisions. [ABSTRACT FROM AUTHOR]

This study presented a decision-making methodology to evaluate leadership management strategies in healthcare systems. This study used the multi-criteria decision-making (MCDM) methodology to deal with conflicting criteria in the decision-making process. The MCDM methodology is integrated with neutrosophic sets to deal with uncertainty and vague information in decision-making. The neutrosophic set has three memberships: truth, indeterminacy, and falsity. The type-2 neutrosophic set is a subset of the neutrosophic set with nine membership degrees. The neutrosophic set integrated with the COPRAS method to rank the alternatives. fourteen criteria and eleven alternatives are used to evaluate leadership management in healthcare systems. The sensitivity analysis was conducted to show the stability of the rank. The sensitivity analysis was performed with 15 cases to change the criteria weights and rank the alternatives under different weights. The results show that the rank of other options is stable in various instances. [ABSTRACT FROM AUTHOR]

Nowadays, to achieve sustainable development and avoid devastating impacts on the environment, India is making rapid and broad changes to green energy technology. In order to provide long-term energy security with lower emissions, renewable energy sources are essential. It is well known that renewable energy technologies, or RETs, have the capacity to significantly meet the demand for electricity while lowering pollution levels. The nation has set up an ecologically friendly energy route in recent years. In this paper, we apply multi-criteria decision making (MCDM) models to determine the best renewable energy technology for India. We use the MULTIMOORA model to identify the best technology under the pythagorean neutrosophic fuzzy set (PNFS) and the FUCOM to obtain the criteria weights. In this study, we investigate sources of renewable energy using the newly developed idea of the PNFS and recommend the best renewable energy source for India. [ABSTRACT FROM AUTHOR]

This study aims to assist public bus operators in locating electric bus charging station (EBCS) facilities from a smart and sustainable view. The selection of the most suitable EBCSs from various possible candidates involves a sophisticated decision-making procedure in terms of several contradictory criteria with imprecise information. The novelty of the study resides in exploring the EBCS site selection problem with spherical fuzzy sets (SFSs), which have shown remarkable effectiveness in limiting information loss by seizing ambiguous, and uncertain data. In this regard, a novel best–worst method (BWM) incorporating Multi-objective optimization via full multiplicative form ratio analysis (MULTIMOORA) methodology in the spherical fuzzy context is proposed to choose the optimal locations for EBCSs. The integrated framework combines the adaptability of the spherical fuzzy BWM (SF-BWM) for determining the criteria weights with the convenience of spherical fuzzy MULTIMOORA (SF-MULTIMOORA) approach for ranking the alternatives. A case study for Istanbul is provided to substantiate the propounded technique and to confirm its viability and efficiency. In the course of making a decision, a four-level hierarchical structure consisting of five main and 22 sub-criteria is built and the comparison matrices are reviewed by a panel of seven experts. A sensitivity analysis is executed, and the results demonstrate that the propositioned approach produces outcomes that are quite robust and consistent. Hence, the findings of this research can benefit public bus operators in choosing the ideal sites for electric charging stations. Finally, the formulated generic methodology is also easily applicable to diverse and complex multiple-criteria problems in the spherical fuzzy domain. [ABSTRACT FROM AUTHOR]

Fuzzy ranking plays a vital role in decision-making problems and various fuzzy applications. There are plenty of ranking methods that are used to rank fuzzy numbers. However, they fail to give satisfactory results in certain situations due to the complexity of the problem. In this present study, an attempt has been made to introduce four types of ranking methods in the field of intuitionistic dense fuzzy (IDF) depending on centroid and graded mean ranking. Also, arithmetic operations based on λ 1 , λ 2 -cuts, fuzzy numbers, and extension principles are defined for IDF environment. A model is framed to rank MCDM problems, which are aggregated using a weighted aggregation operator and ordered using the proposed and extended ranking methods. To illustrate the proposed MCDM model under the field of IDF, the problem of robot selection is taken for war fighter robots and exoskeleton robots to help and replace humans in war and help assistive walking patients with spinal cord injuries. The result reveals that the Ripsaw and Rewalk emerge as preferable options for substituting humans in the contexts of war fighters and exoskeleton robots, respectively. To analyze the effectiveness of the ranking results, comparative and sensitivity analyses are examined. Thus, the results provide a satisfactory output. [ABSTRACT FROM AUTHOR]

The circular economy is important in implementing EU Green Deal targets, climate change mitigation commitments, and pursuing sustainable development. A circular economy allows the reduction of waste and environmental damages linked to waste to save natural resources and mitigate climate change. The transition from linear to circular economy models provides many economic and environmental benefits and is a priority for most countries, especially developed ones. There is a need to measure and monitor the circular economy's progress. In this context, indicators can be useful tools to assess progress achieved by countries in pursuing circular economy development. The paper developed an indicators framework to measure circular economy development progress in the EU and applies the developed framework for Visegrad countries. The Visegrad Group (V4) includes four Central European countries, Poland, the Czech Republic, Slovakia, and Hungary, linked by similar geographical and geopolitical situations and common history and culture. MCDM tool ARAS was applied to ranking four Visegrad countries in 2010 and 2021 based on the progress achieved in circular economy development. [ABSTRACT FROM AUTHOR]

Due to many unexpected events, international enterprises are facing an increasing supply and demand uncertainty. From the perspective of logistics demands, selecting the appropriate logistics service providers is an important issue. From the perspective of logistics service providers, satisfying customers’ needs in competitive markets is vital. This study focuses on three multinational logistics companies providing services in China, Hong Kong, and Taiwan as case studies for investigating the selection criteria in the context of logistics service providers and for evaluating their performance in an uncertain market. The criteria with a higher total influence and net influence should receive a higher weight than other criteria. However, the existing modified DANP-mV model that integrates the DEMATEL (Decision-Making Trial and Evaluation Laboratory) method, the ANP (Analytic Network Process) method, and the mVIKOR (modified Vlsekriterijumska Optimizacija I Kompromisno Resenje) method often does not follow this logic. This study proposes a new model called rDANP-U (revised DANP with mean group Utility) with the purpose of generating more proper weights for the selection criteria with a higher total influence and net influence. Further on, the mean group utility component derived from the VIKOR method is employed for evaluating the performance of the three chosen logistics service providers operating in an uncertain environment and for providing improvement strategies for them. [ABSTRACT FROM AUTHOR]

Machining processes are crucial in the production of various products across different industries. The accuracy, lifespan, and cost of these products significantly depend on the machining processes. This research introduces a novel method for selecting the optimal solution for machining processes. The proposed method, named PSI-R-PIV, is a hybrid of three methods preference selection index (PSI), ranking of the attributes and alternatives (R), and proximity indexed value (PIV). PSI, R, and PIV are all techniques used to rank options to determine the best among the available choices. Moreover, PSI and R have an additional function of calculating weights for the criteria. Therefore, using PSI-R-PIV to rank options for each machining process results in four sets of rankings: one by PSI, one by R, and two by PIV. In the PIV method, the weights for the criteria are calculated using the PSI and R methods. The ranking method using PIV with weights calculated by the PSI and R methods is named the PSI-PIV and R-PIV methods respectively. The four methods in the PSI-R-PIV combination include PSI, R, PSI-PIV, and R-PIV, and have been utilized to rank options in various machining processes. The results indicate that the PSI-PIV method offers high accuracy and is recommended for selecting the best option among the available choices in machining processes. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Financial Politic & Economic Reviews / Finans Politik & Ekonomik Yorumlar is the property of Journal of Financial Politic & Economic Reviews / Finans Politik & Ekomomik Yorumlar and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Real-world multi-criteria problems can be solved using multi-criteria decision-making (MCDM) techniques that allow a variety of qualitative and quantitative aspects to be taken into account simultaneously. There have been an increasing number of theories and approaches created for solving distinct MCDM situations with proportionate and conflicting aspects. Judgments about alternatives considered in MCDM methods may change according to the circumstances and conditions that may occur in the future. Many existing decision-making approaches do not take this situation into account, which may cause wrong decisions. Therefore, it is necessary to create adaptable decision models that make use of both current and future information in order to handle a dynamic decision system. The novelty of this study is to develop a novel MCDM method and its intuitionistic fuzzy set extension, Multi-Period Dynamic ASsessment (MP-DAS) and Intuitionistic Fuzzy Multi-Period Dynamic ASsessment (IF MP-DAS), to provide a dynamic decision approach based on present and future information simultaneously. The objective is to present a dynamic-based MCDM method with as many future time points as experts prefer and a stronger multi-measurement system. By concentrating on medium- and long-term judgments that take into account potential variations of the addressed components, the established method enables more informed decisions to be made for complicated problems. In the study, a multi-measurement system is developed by using both Euclidean distance and distance from average solution. The proposed dynamic decision method can be applied by managers and decision-makers as a decision support system. To show the efficiency and applicability of the developed method, a renewable energy source selection problem is handled, where the assessments include possible variations for both developed methods. A comprehensive sensitivity analysis is conducted to confirm the effectiveness and stability of the method for the developed methods. The results of the comparison analysis with distance-based MCDM and intuitionistic fuzzy distance-based MCDM methods showed that the developed method is superior to other MCDM methods. [ABSTRACT FROM AUTHOR]

Effective management of flood risks requires the prioritization of appropriate flood control solutions. This study aims to prioritize structural flood control options using multi‐criteria decision‐making (MCDM) methods. Four MCDM methods, namely analytic hierarchy process, technique for order preference by similarity to ideal solution, multi‐criteria optimization and compromise solution, and Fuzzy‐VIKOR are employed to assess and rank the flood control options based on multiple criteria. Field surveys, interviews with local authorities and experts, and on‐site assessments of existing flood control structures constituted the primary data collection methods. The findings demonstrate the effectiveness of reservoir dams, retention basins, and levees as viable solutions. Conversely, flood control gates and the no‐project options were assigned lower priorities. The findings highlight the importance of considering multiple MCDM methods to account for variations in rankings. The study provides valuable insights into the decision‐making process for prioritizing flood control options in the study area. These findings can assist policymakers and stakeholders in effectively allocating resources and implementing appropriate structural flood control measures to mitigate flood risks. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study presents a novel approach for ranking problems, named BWM-MOORA-N, which consists of a hybrid MCDM/A method based on BWM and MOORA. The proposed method is utilized to analyze a set of investment funds, considering the preferences of a financial analyst. The BWM-MOORA-N method utilizes BWM to determine the criteria weights, MOORA to order the alternatives, and an additional normalization procedure to ensure that the results are evaluated on a 0–1 scale. The final normalization prevents the presence of negative global evaluations, which makes it easier to interpret and visualize the results. In the application, 42 investment funds are ordered based on a set of criteria derived from real data provided by an investment advisory company. The decision-makers preferences, according to a financial expert, were modeled using an unstructured process of interviews conducted by a decision analyst. The results of the study include comparisons to risk-return metrics and a ranking obtained using a reference point approach that incorporates the Tchebycheff distance concept. Additionally, experiments were conducted to vary possible BWM input comparisons, and statistics from over 1000 simulations are provided. The proposed method produces coherent results, including a ranking of funds that reflects the significant importance given by the decision-maker to profitability and risk criteria. Furthermore, the additional normalization proves to be suitable for practical purposes. Finally, the simulation results provide detailed information for each alternative, including the mean and standard deviations of the global evaluation and the mode of the ranking position. [ABSTRACT FROM AUTHOR]

Background: Supply chain companies have merits and demerits regarding operational and economic transactional policies. The effectiveness of supply chain companies corresponds to a cumulative score on a multi-criteria and perspectives-based evaluation. In this paper, we analyse the performances and challenges of several celebrated e-commerce companies to perceive their overall impression of supply chain management. Method: A mathematical model is framed as a multi-criteria decision-making (MCDM) problem with challenges as criteria and companies as alternatives. The criteria importance through inter-criteria correlation (CRITIC) method is used in this paper to adjust weights representing the available data. The ranking of e-commerce companies is evaluated using multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) method. Results: This model investigates the most dependent criteria and sub-criteria for the adaptation challenges of supply chain companies (SCCs). Furthermore, the SCCs are prioritized based on various conflicting criteria. Conclusion: Various challenges of SCCs, like logistics constraints, disruptions in supply chains, issues with technology, ethical sourcing and inconsistency between the products' availability and the pace of consumption, are considered and analysed. We amassed the difficulties as criteria and sub-criteria in a numerical process using the MCDM approach. Additionally, the sensitivity and comparative of several optimal phenomena are analysed based on distinctive combinations of challenges in the ranking arena. [ABSTRACT FROM AUTHOR]

This study tackles the challenge of identifying optimal investment sectors amid the growing importance of environmental, social, and governance (ESG) factors, which are often complex and conflicting. This research aims to effectively evaluate and prioritize ten investment sectors based on twelve ESG criteria by integrating expert evaluations with two advanced multi-criteria decision-making (MCDM) methods. Three expert teams assessed each sector's performance based on these criteria using fuzzy logic to manage uncertainties in expert judgments. The MEREC (MEthod based on the Removal Effects of Criteria) identified biodiversity and land use as the most critical factor, while transparency and disclosure was least significant. The AROMAN (Alternative Ranking Order Method Accounting for two-step Normalization) method was further used to rank the ten alternative sectors, with impact investing funds emerging as the top choice, followed by renewable energy and sustainable responsible investment funds. Conversely, ESG-compliant stocks, ESG-focused exchange-traded funds, and ESG-focused real estate investment trusts ranked the lowest. The study's findings were validated through comparisons with other MCDM tools and sensitivity analysis, confirming the robustness of the proposed model. This research offers a valuable framework for investors looking to incorporate ESG considerations into their decision-making, promoting sustainable and responsible investing practices. [ABSTRACT FROM AUTHOR]

Determining the optimal intermix of various input parameters for an end milling process is an important task to explore its fullest performance along with the achievement of the desired responses. Several multi-criteria decision-making techniques, like technique for order preference by similarity to ideal solution (TOPSIS), grey relational analysis and multiobjective optimisation on the basis of ratio analysis (MOORA), have been applied for solving parametric optimisation problems of milling processes while assigning equal importance to the considered responses. In this paper, the optimal combinations of cutting speed, feed rate and depth of cut of an end milling process during the machining of Al 1070 alloy are determined using the weighted sum model (WSM), weighted product model (WPM), weighted aggregated sum product assessment (WASPAS), MOORA, TOPSIS, evaluation based on distance from average solution (EDAS), additive ratio assessment (ARAS) and complex proportional assessment (COPRAS) methods with the allocation of unequal weights to the responses determined by standard deviation and entropy techniques. The derived solutions are also compared when the same importance is assigned to the responses. From the results, a parametric combination of 255 rpm cutting speed, 82 mm/min feed rate and 0.75 depth of cut is identified by WSM, WPM, WASPAS, COPRAS, TOPSIS and ARAS methods to achieve the optimal values of material removal rate (MRR) and average surface roughness (Ra) as 595.23 mm3/min and 13.79 µm, respectively. It is also noticed that both feed rate and depth of cut significantly influence MRR and Ra during the machining of Al 1070 alloy. [ABSTRACT FROM AUTHOR]

Titanium alloys are frequently utilised in applications that call for high strength at high temperatures as well as excellent mechanical resistance. This alloy has excellent mechanical properties that enable improved performance from titanium alloys. In the current research, Taguchi L25 orthogonal array of experiments were conducted first, and MCDM techniques and ANNOVA were utilised to forecast the influencing machining factors. From MCDM three methods (SAW, VIKOR & TOPSIS) prediction algorithms which rank one for experiment no. 2. Experiment results shows that experiment no. 02 gives optimum/best results in terms of Tool life and surface roughness. While the process parameter has values cutting speed = 80 rpm, Feed rate = 018 rev/mm, Depth of cut = 0.12 mm, and Rake angle = 14 degrees. From ANOVA outcome for surface finish it is noticed that the process parameter rake angle is having Rank 01 for all levels. Thus rake angle is major influencing parameter to achieve a better quality of surface roughness. While in case of flank wear it is crystal clear that the process parameter DOC is having Rank 01 for all levels. Thus DOC is most influencing parameter to achieve lesser flank wear and better quality of tool life. [ABSTRACT FROM AUTHOR]

Abstract Plastic recycling has become more important than ever as the globe struggles with growing environmental issues. This research explores the significant environmental impact of recycling plastic and its growing relevance. The pervasive material known as plastic presents a complex risk to both human health and ecosystems in contemporary life. It exacerbates problems including marine pollution, habitat damage, and wildlife entanglement because of its persistence in landfills and seas, which leads to serious ecological deterioration. In addition, producing plastic uses a lot of energy and produces a lot of greenhouse gas emissions, which exacerbate climate change. Through the use of multi-criteria decision making (MCDM), this study emphasizes how vital it is to support recycling activities in order to protect the environment and promote a sustainable future. The elimination and choice ex-pressing reality (ELECTRE) approach is used to rank the alternatives in this proposed research study that employs bipolar dual hesitant fuzzy sets (BDHFs). The most efficient and versatile outranking method for making decisions is the BDHF-ELECTRE approach. The weights of environment, economic, social, technical, and finally safety is computed using the entropy distance metric. The economic factor received the highest score of 0.2945 among the other factors since economic considerations are crucial in choosing the most efficient plastic recycling method, as they ensure sustainability, cost-effectiveness, resource allocation, and overall feasibility in managing plastic waste. The decision-makers determined that the mechanical recycling approach ought to be prioritized over all others for the efficient recycling of plastic waste. The robustness of the system is examined in the sensitive and comparative analyses. The proposed MCDM technique thus presents a viable solution, mitigating the adverse effects of plastic waste by conserving resources, reducing energy consumption, and curbing pollution.

This article introduces a comprehensive analysis of 20 leading companies, scrutinized through their financial metrics across various sectors. By deploying multi-criteria decision-making (MCDM) techniques, we aim to offer investors a clear and objective perspective on which companies stand out as the best investment options. Among the MCDM techniques, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is utilized, renowned for its efficiency in handling complex decision-making scenarios which is conducted by two clauses. 1) Implementing TOPSIS with assigning equal weights and same share to every chosen metrics as criteria and 2) employ BWM (Best Worst Method) to calculate these weights base on their significance and relevancy to the prosses of ranking. According to the Result gained from the computation, ranks 1 to 5 belong to the similar companies with both assumptions which are Ford Motor Co, BP plc, Tesla Inc, General Motors Co and Exxon Mobil Corp. The consistency in rankings across two different weighting assumptions highlights the robustness of the criteria used, ensuring stable and reliable outcomes. This enhances the credibility of the findings, making them more trustworthy and citable for those who seek reliable and robust methodologies for informed investment decisions.

Abstract In the era of Industry X.0, manufacturers are increasingly adopting digital technologies such as robotics, big data, mobile communications, and information technology to enhance productivity and profitability. However, navigating this digital revolution presents significant challenges, particularly in selecting the most suitable technologies amidst uncertainties and the lack of precise quantitative data. Fuzzy logic, initially introduced to manage vagueness and imprecision, has proven to be an essential tool in various domains for handling these uncertainties. However, as systems and environments become more complex, traditional fuzzy logic exhibits limitations in effectively representing and managing deeper levels of uncertainty. This realization has driven the development of several extensions to fuzzy logic, designed to address challenges associated with uncertainty representation. Yet, a central question remains: Which fuzzy logic extension is the most relevant for better dealing with uncertainty? This study introduces a hybrid-fuzzy decision-making framework for technology selection, which allows for more than one representation of fuzzy information. Using a multi-expert, multi-criteria decision-making (MCDM) strategy, the study seeks to support organizations in selecting the most suitable digital technology for their operations, particularly when precise quantitative data is unavailable, and decisions must rely on expert opinions. The suggested framework is designed to handle decision-makers' hesitancy, subjectivity, and ambiguity in the decision-making process. To showcase the practical application of the proposed methodology, a case study was conducted to select appropriate technologies in the Moroccan automotive sector. The results demonstrate that, regardless of the fuzzy logic extension used, digital automation and automatic identification consistently rank among the top technologies, underscoring their importance according to most experts. Our proposed approach is particularly effective in resolving conflicts between different fuzzy logic extensions, thereby facilitating the ranking of the remaining technologies. Notably, additive manufacturing ranked third, followed by the other technologies.

Because of its excellent mechanical qualities and weldability, titanium alloy is used in many different biomedical applications. Wire electrical discharge machining may be used to machine materials with such greater strengths and intricate forms. Using Taguchi-Data Envelopment Analysis-based Ranking (DEAR) approach and zinc-diffused coated brass wire electrode to improve Titanium alloy machining was the goal of this research project. The quality metrics that were taken into consideration were sur-face roughness, kerf width, and material removal rate. Among the selected factors, with an error of 2.7%, the optimal configuration of input factors was determined to be 130 µs (Ton), 40 µs (Toff), 50 V (SV), 6 A (IP), and 8 Kg (WT). Due to its relevance in the process of deionization, the Ton is the high-est influential parameter for creating quality measurements.