Multi-criteria Decision-making Methodology Research Articles

Deciding Technosignature Search Strategies: Multi-Criteria Fuzzy Logic to Find Extraterrestrial Intelligence

This study presents the implementation of Multi-Criteria Decision-Making (MCDM) methodologies, particularly the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS), in prioritizing technosignatures (TSs) for the search for extraterrestrial intelligence (SETI). By incorporating expert opinions and weighted criteria based on the established Axes of Merit, our analysis offers insights into the relative importance of various TSs. Notably, radio and optical communications are emphasized, in contrast to dark side illumination and starshades in transit. We introduce a new axis, Scale Sensitivity, designed to assess the variability of TS metrics. A sensitivity analysis confirms the robustness of our approach. Our findings, especially the highlighted significance of artifacts orbiting Earth, the Moon, or the Sun, indicate a need to broaden evaluative criteria within SETI research. This suggests an enhancement of the Axes of Merit, with a focus on addressing the plausibility of TSs. As the quest to resolve the profound question of our solitude in the cosmos continues, SETI efforts would benefit from exploring innovative prioritization methodologies that effectively quantify TS search strategies.

Effect of fused deposition modeling process parameter in influence of mechanical property of acrylonitrile butadiene styrene polymer

The objective of this study is to investigate how the mechanical properties of components produced using acrylonitrile butadiene styrene (ABS) on a Creality Ender-3 3D printer are affected by various fused deposition modeling (FDM) printing parameters. The impact of various factors, including infill density, printing speed, platform temperature, extruder temperature, and so on, was assessed in terms of their influence on the ultimate tensile strength, yield strength, and elastic modulus of the manufactured components. The impact of each parameter was assessed using a Multi-criteria decision-making (MCDM) methodology. Finally, the second set of parameters, including a 35% infill thickness, 0.25 mm layer level, 40 mm/s printing speed, 75 °C platform temperature, 210 °C extruder temperature, and 75 mm/s travel speed, was discovered to be the most suitable for ABS filament used to make impellers.

Assessment of Selecting the Most Appropriate Oilseed for Biodiesel Production Using the COPRAS Method

Diesel resources are valuable both industrially and economically around the world. However, considerations including the diminishing supply of fossil energies and the rise in greenhouse gas emissions have accelerated research into greener alternative fuels globally. Presently, emerging nations are having difficulty supplying their expanding energy needs due to the depletion of traditional energy sources. It is essential to increase the variety of energy supplies and lessen reliance on fossil fuels by utilising substitutes to address this challenge. One of these alternate sources is biomass, which may be used to produce fluid biofuels like "bioethanol and biodiesel"."The fuel quality, engine performance characteristics, and emission outcomes of biodiesel" are the main criteria that have changed as a result of variations in the physicochemical features of the oilseeds ("soybean, cottonseed, rapeseed, and camelina"). To choose the best energy crop, these parameters were assessed using "multi-criteria decision-making (MCDM) methodologies". “COPRAS” has been used to determine the importance of each parameter and the order of the studied alternatives for this objective."Rapeseed, soybean, cottonseed, and camelina biodiesel" are respectively the top four oilseed substitutes for the manufacturing of biodiesel, according to the analysis's findings.According to the findings; "rapeseed" is the most suited oilseed for growth as an energy commodity, whereas "camelina" is the least favored option for making biodiesel.

Development of a new tool for seismic risk assessment and multi-criteria decision making

The present paper introduces a new risk-informed based tool that can support disaster management authorities in making decisions in response to earthquakes at local and regional scales by using estimated physical and socio-economic losses. The risk-informed based tool is building upon existing risk assessment methods, and multi-criteria decision-making (MCDM) methodology. The integrated risk assessment methods are adapted from the earthquake loss estimation tool SELENA. The integrated MCDM methodology, allowing selection of optimal mitigation measures by taking into account the priorities and preferences of disaster management authorities, was developed within the EU Horizon 2020 TURNkey project [1]. The new tool presented in this study also allows easy integration and incorporation of existing database and models that can be openly available. The tool is characterized with user-friendly interface and flexible format of the input and output data, that can be easily understood by different actors in disaster risk management.

Assessment Criteria of Solar Hydrogen Production Plant Based on Fuzzy Multi-Criteria Decision-Making Methodology

This study presents a comprehensive solar hydrogen production plant assessment, focusing on evaluating its technological efficiency, economic viability, environmental impact, and operational reliability. Leveraging solar photovoltaic technology and electrolysis processes, the plant converts abundant solar energy into hydrogen, offering a sustainable pathway towards clean energy production. The assessment encompasses a detailed analysis of solar panel efficiency, electrolyze performance, cost-effectiveness, lifecycle emissions, and operational considerations. Findings reveal that while technological advancements drive enhanced efficiency and reliability, economic viability remains challenging due to high initial capital costs. However, the environmental benefits, including reduced carbon footprint and water usage, underscore the plant's potential contribution to a low-carbon future. Operational assessments highlight the need for scalable, grid-integrated systems with robust safety measures. Overall, the evaluation provides critical insights into the opportunities and challenges of solar-driven hydrogen production, offering recommendations to optimize plant efficiency, reduce costs, and promote its widespread adoption. We used the multi-criteria decision-making (MCDM) concept for various criteria. The SWARA method is used to compute the weights of criteria. The SWARA method is integrated with fuzzy numbers to overcome the uncertainty data. The main results show that solar production has the highest rank.

A Combined Compromise Solution (CoCoSo) of MCDM Problems for Selection of Medical Best Bearing Ring

Bearings offers a variety of products designed to meet the rigorous requirements of the medical sector for patient safety, such as diagnostic and laboratory equipment, surgical and dental instruments, ventilators, heart pumps, etc. In the field of medical instruments, the bearing rings compromise from superior quality metals to perform surgical operations safely. The selection of the best bearing ring materials for medical applications is a critical task that involves considering various factors to ensure optimal performance, reliability, and safety. The study explores challenges and criteria in selecting the ideal bearing ring for medical devices and equipment. The criteria discussed encompass material compatibility, biocompatibility, lubrication requirements, precision, cleanliness, durability, noise and vibration control, size and dimension, load capacity, temperature and environmental conditions, regulatory compliance, manufacturing considerations, compatibility with adjacent components, maintenance, and serviceability. The challenges mainly include biocompatibility validation, material selection, performance requirements, regulatory compliance, size limitations, cost considerations, supply chain reliability, long-term stability, maintenance, expertise, and collaboration. To address the current challenges of medical bearing the study needs to understand the medical requirements, and engagement with trusted suppliers and regulatory bodies. The study presents a multi-criteria decision-making methodology (MCDM) problem with various criteria and alternatives with uncertainty conditions. The decision maker’s perspectives evaluate the importance of criteria and alternatives. The neutrosophic set is used to deal with uncertainty conditions. The study illustrates a solution to select the best bearing ring to ensure the success and safety of medical applications. The MCDM provides solutions to choose the best bearing ring. A Combined Compromised Solution (CoCoSo) method is used as an MCDM method with neutrosophic theory to select the best alternatives. A numerical case study is presented to show the applicability and availability of the proposed methodology.

Supplier Selection in the Age of Industry 4.0: A Review on MCDM Applications and Trends

The advent of Industry 4.0 has triggered a profound transformation in manufacturing and supply chain management, necessitating the adaptation of supplier selection processes to this evolving technological landscape. This comprehensive review paper places a significant focus on the role of multi-criteria decision-making (MCDM) methodologies and emerging trends in the context of supplier selection within Industry 4.0. It underscores the critical importance of effective supplier selection in achieving operational excellence and bolstering supply chain resilience in this new era, exploring the challenges and opportunities presented by technological advances such as the Internet of Things, big data analytics, artificial intelligence, and advanced manufacturing techniques. The paper looks into the theoretical underpinnings of MCDM techniques, highlighting their suitability for addressing the complex, multi-dimensional criteria associated with supplier selection. As a valuable resource, this study caters to researchers, practitioners, and decision-makers navigating the intricate landscape of supplier selection within the dynamic area of Industry 4.0, emphasizing the pivotal role of MCDM methodologies and the ever-evolving dynamics shaping supplier selection processes in the digital age.

Ontological Augmentation and Analytical Paradigms for Elevating Security in Healthcare Web Applications

The 4th and 5th industrial revelations provided huge access to a high volume of information in healthcare sectors. However, the vast majority of web applications are under attack constantly. Due to the huge volume of attack vectors in recent years, technical breaches to the web applications of healthcare sectors are becoming a common issue. Hence, it is essential to develop an effective framework that would help experts and healthcare practitioners in web application security management. In this research, we proposed an ontology-based technique for developing secure online web applications in healthcare sectors. This study presents a conceptual framework with 5 stages namely: idea understanding, requirement identification, design and code, threat classification, and facilitate. The proposed methodology involves several various advantage features such as providing a unified path for future professional applications. Also, the proposed solution provides a clear pathway for implementing the easy-to-use secure web application development in short term in the healthcare sector. Finally, the study used a multi-criteria decision-making methodology to undertake a performance simulation assessment. Out of the evaluated process, Fuzzy-AHP has shown better performance for scheduling each step of the final developed security system better. In the future, the proposed method will be evaluated using more databases in the healthcare sector to improve the applications of the proposed method.

A Study and Application Analysis Exploring Pythagorean Fuzzy Set Distance Metrics in Decision Making

The ever-increasing demand for high-quality solutions drives research toward more sophisticated decision-making solutions. In the field of decision making, the ability to solve complex real-world problems is of paramount importance. To this end, fuzzy sets are used, which offer the possibility of incorporating uncertainty into the values describing decision options. This study focuses on Pythagorean fuzzy sets, an extension of classical fuzzy sets, providing even more tools for modeling real-world problems by presenting a distance measure for these specific sets. A verification of the characteristics of the proposed distance measure has been carried out, proving its validity. The proposed measure is characterized by a more straightforward formula and thus simplifies the calculations. Furthermore, to confirm its usability, a multi-criteria decision-making methodology is presented, the results of which are compared with two multi-criteria decision-making methods, namely, PF-TOPSIS and PF-VIKOR, and another distance measure previously presented in the literature. The comparative analysis highlights lower variability in terms of preference values calculated using the proposed distance measure, which confirms the stability and reliability of the newly proposed distance measure while maintaining low computational complexity. Moreover, a high correlation with rankings calculated using PF-TOPSIS ensures its utility in terms of decision making.

A rigorous evaluation of earthquake management strategies in Morocco's Al Haouz province: a Multi-Criteria Decision-Making methodology

A rigorous evaluation of earthquake management strategies in Morocco's Al Haouz province: a Multi-Criteria Decision-Making methodology

Information Fusion from Multimodal Clinical Sensors for Effective Supplier Decision-Making in Healthcare

Effective procurement of clinical devices in healthcare demands a sophisticated decision-making approach integrating diverse data sources from multiple devices, brands, and suppliers, particularly within the context of information fusion. This study addresses this challenge by proposing an improved best-worst method harmonized with information fusion techniques and multi-criteria decision-making methodologies. The background emphasizes the dynamic nature of healthcare procurement, necessitating systematic strategies for navigating the complexities of device selection and integration. Recognizing the intricacies inherent in this challenge, the problem statement revolves around enhancing the best-worst method to amalgamate data from clinical devices while concurrently evaluating brands and suppliers. This aims to optimize performance and minimize costs within the information fusion paradigm. Our proposed methodology introduces an augmented best-worst approach, encompassing weighted criteria assessment for clinical devices, brands, and suppliers, providing a more adaptable and nuanced decision-making framework tailored to the information fusion landscape. The results showcase a structured evaluation matrix derived from refined weighted criteria, elucidating the relative performance and strengths across various entities within the healthcare procurement ecosystem. Emphasizing reliability, compatibility, innovation, and quality assurance, this process highlights pivotal factors influencing procurement decisions within the realm of information fusion.

Choice Optimal Fuel Alternative in Thermal Power Station Using Neutrosophic Set and MCDM Methodology

In a power plant, the fuel choice directly impacts the efficiency, cost, and ecological impact of generating electricity. For power plants to produce electricity effectively and affordably to fulfill the needs of consumers in homes, companies, and communities, they need a fuel supply that is constant, dependable, and inexpensive. In this study, we used the concept of multi-criteria decision-making (MCDM) to deal with the various criteria of fuel alternatives. We used the EDAS method as an MCDM methodology to rank the fuel alternatives and select the best one. The EDAS method is employed with the interval-valued neutrosophic sets (IVNSs) to deal with the uncertainty information in the evaluation process. We compute the weights of the criteria of thermodynamic parameters. We used ten thermodynamic parameters such as temperature, mass, energy, etc. Then, the principal results show that temperature is the best criterion, and the work interaction is the worst criterion in all criteria. The EDAS method ranked twenty alternatives. The results show that alternative 20 are the best and alternative 14 is the worst of all alternatives. We employed the sensitivity analysis to show the rank of alternatives under ten cases. The results show the 20 alternative is the best in all cases. The results are stable.

Assessment of Hazard in Firefighting Job Using Triangular Neutrosophic Sets and Hybrid Multi-criteria Decision Making

Traditional approaches to recognizing hazards and evaluating their risks have several shortcomings, including data confusion and unpredictability, an inability to accurately reflect human thought processes, a failure to give weight to factors, the use of established data and tables, and the influence of the evaluator on the final risk evaluation outcomes. Thus, refining current techniques and creating new ways with more precision and sensitivity is essential. We proposed a framework for risk assessment of firefighting. Firefighting has various criteria, so the concept of multi-criteria decision-making (MCDM) deals with these criteria, such as life safety, resource allocation, incident duration, weather conditions, access, etc. We collected ten risk criteria and 25 alternatives. The proposed framework has two main stages. First, we apply the average method to ten risk criteria to show the weights and the importance of the criteria. Then, in the second stage, we used the grey rational analysis (GRA) method to assess the firefighting risks. The GRA method is an MCDM methodology used to rank the alternatives. The GRA method is integrated with the triangular neutrosophic sets (TNSs) to deal with vague and uncertain information. Then, the principal results show that life safety is the highest weight, and the incident duration is the lowest. The outcome of the GRA method shows that risk 25 is the highest and risk 17 is the lowest. We applied the sensitivity analysis to show the stability of the results. We offer the model is adequate, and the results are stable.

Integrated Neutrosophic methodology and Machine Learning Models for Cybersecurity Risk Assessment: An exploratory study

Information technology security, or Cybersecurity, guards against hostile cyberattacks on computers, mobile devices, servers, electronic systems, and networks. Cybersecurity risks have been a significant concern for any vital digital infrastructure in recent years, and different online cyberattacks are also becoming a significant problem for society. Consequently, it's critical to adopt technology created to provide cybersecurity. However, one should consider the associated hazards while selecting among Cybersecurity systems. We have developed a multi-criteria decision-making (MCDM) approach based on a single-valued neutrosophic set (SVNS). This allows specialists more latitude in assessing the criteria and alternatives using language and overcoming uncertain information. The VIKOR is an MCDM methodology used to rank the other options. The VIKOR method is integrated with the neutrosophic set. There are 18 criteria, and 10 alternatives are used in this study. The sensitivity analysis and comparative analysis are conducted in this study. The sensitivity analysis results show the alternatives' rank is stable under different cases. The comparative analysis compares the suggested method with other MCDM methods. The comparative analysis shows the suggested method was effective compared with other MCDM methods. Machine learning methods predict the type of attack in Cybersecurity. This study uses Three machine learning methods: decision tree, random forest, and support vector machine.

A Probabilistic Neutrosophic Hesitant Fuzzy Set for Waste Water Treatment Plants

Decision-makers at wastewater treatment plants must increase process efficiency and circularity while preserving economic performance. They must comply with increasing requirements about lowering emissions, sustainability, and human health safety. To operate and choose technologies to fulfil these expectations leads to complicated multi-objective issues. As a consequence, the water industry has developed several decision support systems. Multi-criteria decision-making (MCDM) is used to deal with various criteria in the evaluation process. The MCDM methodology integrated with the probabilistic neutrosophic hesitant fuzzy set (PNHFS) to deal with vague and incomplete information. The PNHFS used the VIKOR method to rank the alternatives and used the optimal wastewater treatment plants. The criteria weights are computed. The results show that safety is of the highest importance—the sensitivity analysis was conducted to show the different ranks under different cases. The main results show the different ranks are stable, and the suggested MCDM methodology is robust compared with other MCDM methods.

Single Valued Neutrosophic Sets Approach for Assessment Wind Power Plant

Full exploitation of offshore wind resources still needs to be completed despite their significant potential to reduce the impacts of climate change via the production of renewable power. Planning strategies that include wind resources, safety, economic, social, and government impacts are essential for advancing offshore wind generation projects. This study aims to evaluate the criteria for wind power plants and select the best turbine. This process has various conflict criteria, so the multi-criteria decision-making (MCDM) methodology deals with multiple criteria. The ARAS method is an MCDM method used to rank the alternatives. The ARAS method uses the single-valued neutrosophic set to deal with uncertain information. We gathered eleven criteria and fifteen alternatives. The results show the turbine resource is the best and the economic criterion is the worst. The sensitivity analysis is conducted to ensure the proposed model's results and show the strength of the proposed method. The results show the proposed model is suitable for selecting the best wind power plant.

Single Valued Neutrosophic Sets for Analysis Opinions of Customer in Waste Management

This study presents an analysis of consumer opinions on waste medicine management. The study explores consumers' concerns, preferences, and suggestions regarding correctly disposing unused or expired medications. The analysis shows the key points that emerged from consumer opinions, including environmental impact, public health and safety, accessibility and affordability, education and awareness, pharmaceutical industry responsibility, convenience and ease of disposal, privacy and confidentiality, community engagement, alternatives to disposal, extended producer responsibility, international collaboration, technology solutions, environmental stewardship, and government regulation and support. This study shows the importance of understanding consumer perspectives in developing effective waste medicine management strategies prioritizing environmental sustainability, public health, and consumer satisfaction. We used the multi-criteria decision-making (MCDM) methodology to deal with these criteria. We gathered 15 criteria concerned with waste medicine management. We used the DEMATEL method to show the criteria weights and relationships between criteria. The DEMATEL method is integrated with the single-valued neutrosophic set to deal with uncertain data. The results show the environmental impact has the most significant weight.

Single Valued Neutrosophic HyperSoft Set based on VIKOR Method for 5G Architecture Selection

This work introduces the framework for selecting architecture in 5G networks, considering various technological, performance, economic, and operational factors. With the emergence of 5G technology, the architecture selection process has become pivotal in meeting diverse requirements for ultra-high-speed connectivity, low latency, scalability, and diverse service demands. The evaluation comprehensively analyses different architecture options, including centralized, distributed, cloud-based, and virtualized architectures. Factors such as network performance, scalability, cost-effectiveness, security, and compatibility are considered within a multi-criteria decision-making framework. Findings reveal each architecture option's diverse strengths and limitations, emphasizing the importance of aligning architectural choices with specific use cases, deployment scenarios, and business objectives. We used the concept of multi-criteria decision-making (MCDM) methodology to control the various criteria. We used the single-valued neutrosophic set (SVNS) to deal with vague and inconsistent data. The SVNS is integrated with the hyperSoft set. The proposed methodology uses a single-valued neutrosophic hyperSoft set (SVNHSS) to select the best 5G architecture. We used the VIKOR method as an MCDM method to rank the alternatives. The proposed framework provides stakeholders with a structured methodology to evaluate and prioritize architecture options, facilitating informed decision-making in the complex landscape of 5G network deployments.

Analysis of the countries’ business attraction with the ELECTRE-III method

Attracting foreign investment is essential for the competitiveness and prosperity of nations. When deciding where to invest, an investor may be interested in considering specific criteria for investing or doing business and preferences for those criteria. In the same way, when evaluating the situation of a country to attract investment, consider different aspects to determine its ease of doing business. The MultiCriteria Decision-Making (MCDM) methodology is suitable for evaluating nations according to their ease of doing business due to the multifactorial elements of each nation. This work applied the ELECTRE-III method; it evaluated 190 nations based on the decision-maker?s preferences, giving different importance to the ten criteria considered in the World Bank?s Doing Business 2020 study. The results with this methodology show better-positioned nations in the ranking compared to the report presented by the World Bank Group.

Sustainability in Servitization: A Review of Assessment Methodologies for the Steel Sector

Due to increasingly stringent environmental regulations, many sectors, including the steel industry, have to think about new strategies to improve their sustainability performance. In other sectors, the adoption of servitized business models has proven to be an effective practice in this respect. For this reason, the steel sector is increasingly looking at this paradigm. However, as there is a lack of clear methodologies for assessing the sustainability of servitized business models, this article aims to identify the most suitable ones through a literature review. In order to provide guidelines within the steel sector then, the work is completed by matching the identified methodologies with a set of criteria useful for assessing servitized business models in this specific sector. The results show how the adoption of Multi Criteria Decision-Making methodologies is more suitable for qualitative criteria, while methodologies such as Life Cycle Cost or Life Cycle Assessment are more suitable for quantitative criteria, although attention must be paid to the identification of the functional unit, or the determination of the system boundaries. Finally, the article also shows which criteria are most relevant to assess the sustainability of servitized business models within the steel sector.