Ranking Of Alternatives Research Articles

The Effect of CoCoSo Method on the Ranks of Alternatives: A Case Study of Copper Electrical Wire Selection

When using MCDM (Multi-Criteria Decision-Making) methods to rank alternatives, decision makers’ opinions have a huge influence on the ranking results. The decision makers’ opinions can vary depending on the chosen MCDM method, data normalization method, and weighting method. For some MCDM methods, during the application process, users also need to choose the value of a certain coefficient (called the user coefficient). Obviously, the value of the user coefficient depends on users’ opinions, and of course, these opinions can affect the ranking of the alternatives. In this article, the effects of users’ opinions on the ranks of the alternatives when using the CoCoSo (Combined Compromise Solution) method are investigated. Users’ opinions (including the weighting criteria method and the user coefficient) are considered the input of the investigation process. Organizing the investigation of the effects of these two parameters on the ranks of alternatives was applied to the case of copper electrical wire selection. The results show that the users’ opinions have little effect on the ranks of alternatives. This result confirms CoCoSo's outstanding advantage.

Preference learning based on adaptive graph neural network for multi-criteria decision support

Intelligent decision-making assists decision-makers (DMs) in making choices through data analysis, model prediction, and automated processes. Central to this field are two key concepts: multi-criteria decision making (MCDM) and preference learning (PL). While MCDM and PL both aim to develop decision models that rank alternatives based on observed or revealed preferences, they diverge in focus. MCDM concentrates on the DMs' perspectives, whereas PL emphasizes model-driven approaches. This divergence presents significant challenges in integrating these methodologies, particularly in ensuring the integrated method remains scalable and interpretable amidst the complexity of decision scenarios. To bridge this gap, our study introduces the use of graph structures to frame decision problems and proposes a novel PL method employing graph neural network (GNN) for multi-criteria decision support. This method is anchored in the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) technique and combines an adaptive GNN model with a weight determination model. The GNN model updates embeddings from the alternative's criteria and category features, utilizing an attention mechanism to adaptively assess their importance. Concurrently, the weight determination model contains a weight neural network module to set objective criterion weights and a game theory-based module for calculating combined criterion weights. The method not only inherits the interpretability and intuitive appeal of decision models but also leverages the computational efficiency and high accuracy of machine learning. In experiments conducted on benchmark datasets, our method exhibits significant performance improvements, especially in ranking-related evaluation metrics, outperforming the best baseline by 5.78%.

Ranking Analysis of Tribological, Mechanical, and Thermal Properties of Nano Hydroxyapatite Filled Dental Restorative Composite Materials Using the R‐Method

ABSTRACTR‐method as multi criteria decision‐making method was proposed to determine the ranking of alternatives and performance criteria or attributes of dental composite materials. Five alternatives (DHM0, DHM2, DHM4, DHM6, and DHM8: nomenclatures of micro‐nano ceramic particulate filled dental restoration composites) and 14 attributes (performance criteria) were considered in this research. The decision‐maker determined the relevance of each rank and then attributed it to the performance criterion. In a similar manner, the options were ranked in relation to each performance criterion according to the associated performance criteria. Therefore, the order of the ranks of dental composites is DHM8 (1st rank) > DHM4 (2nd rank) > DHM6 (3rd rank) > DHM0 (4th rank) > DHM2 (5th rank). The suggested technique is showed simpler and advance compared to the other generally employed decision‐making techniques.

Fuzzy set-based decision support system for hydrogen sulfide removal technology selection in natural gas processing: a sustainability and efficiency perspective.

Removing hydrogen sulfide (H2S) toxic and corrosive gas from the natural gas processing and utilization industry is a challenging problem for managers of these industries. This problem involves different economic, environmental, and health issues. Various technologies have been employed to remove the H2S gas from these industries, and choosing appropriate H2S removal technologies is a complex multi-criteria decision-making (MCDM) problem. In this regard, the present research work aims at developing a novel decision support system (DSS) by integrating MCDM techniques based upon the (best worst method) BWM and (alternative ranking order method accounting for two-step normalization) AROMAN methods under the decomposed fuzzy set theory. The DSS is used to assess technologies for removing H2S gas and then choose the best scenario for implementing among these technologies. Fourteen criteria in four environmental, social, economic, and efficiency aspects and five technologies of fixed-bed activated carbon technology (A_h2s1), biotrickling filter technology (A_h2s2), scavenger technology (A_h2s3), chemical oxidation scrubber technology (A_h2s4), and liquid redox technology (A_h2s5) have been chosen for evaluation. The final value of each technology is obtained as A_h2s1 (0.5550), A_h2s2 (0.5262), A_h2s3 (0.5809), A_h2s4 (0.6357), and A_h2s5 (0.5790). The results reveal that the chemical oxidation scrubber technology (A_h2s4) and biotrickling filter technology (A_h2s2) with the values of 0.6357 and 0.5262 are respectively the best and worst scenario for the removal of H2S produced by the natural gas processing and utilization plants of Iran. Therefore, the DSS is feasible and applicable and gives reliable and robust findings for acquiring the optimal H2S removal technology.

Student diversity, university rankings and the positioning of Russell Group universities

ABSTRACT This paper presents insights into English universities’ student diversity profiles, based on data from the Times and Sunday Times 2024 (TST2024) Social Inclusion Index. Typically excluded from league tables and rankings, TST2024 supplemented its Good University Guide with an additional ranking based on undergraduate socio-economic and socio-demographic student characteristics, dropout rates and the Black attainment gap. Analyses demonstrate a negative correlation between the TST2024 general league table score and its specific social inclusion score, indicating that highly ranked universities tend to score lower on social inclusion. However, not all social inclusion indicators correlate with the league table score to the same strength. Additionally, nuances in student diversity profiles between Russell Group members exist. Several non-Russell Group institutions score lower on social inclusion in comparison with some Russell Group members. This paper also contributes to ongoing debates on the role of university league tables, especially considering the rapid expansion of alternative rankings.

BWM-MARCOS: A new hybrid MCDM approach for mineral potential modelling

Here we describe the application of a novel hybrid multi-criteria decision-making (MCDM) approach termed “Best-Worst-Method-Measurement of Alternatives and Ranking according to COmpromise Solution” (BWM-MARCOS) to mineral potential modelling (MPM). The newly proposed BWM-MARCOS technique combines two mathematical frameworks in which the BWM approach is utilised to weight decision criteria (i.e., predictor maps) whilst the MARCOS approach is applied to rank alternatives. A BWM-MARCOS model of orogenic gold mineral systems in the well-endowed Granites-Tanami Orogen (GTO) in Australia's Northern Territory was generated utilising the same study area and set of competent spatial proxies as previously developed and described by Roshanravan et al. (2020), facilitating the benchmarking of the new results against those obtained from previous models. We found the BWM-MARCOS approach to MPM performed better than any of the knowledge-driven (i.e., fuzzy inference system), continuous (i.e., data-driven index overlay, geometric average and fuzzy gamma) and data-driven (i.e., feed-forward deep neural network and ‘original’ random forest) mineral prospectivity models previously developed for the GTO by Roshanravan et al. (2020, 2023b). To further constrain the BWM-MARCOS outputs, the modeled gold potential zones were delimited utilising a concentration-area fractal approach. The areas covered by the prioritised 1st order (top priority) and 2nd order (high priority) targets represent significant reductions of the search space (i.e., >303 times or two orders of magnitude, and >93 times or one order of magnitude, respectively). Any order of magnitude, or greater, reduction of the search space, as achieved in this study, can be considered a hallmark of a well-performing, practically useful targeting approach.

AI-Driven LOPCOW-AROMAN Framework and Topological Data Analysis Using Circular Intuitionistic Fuzzy Information: Healthcare Supply Chain Innovation

Artificial intelligence (AI) stands out as a significant technological innovation, driving progress in diverse areas such as big data analysis, supply chain management, energy efficiency, sustainable development, etc. The present study investigates how AI could contribute to the sustainability of the healthcare supply chain (HSC) and managing medical needs. Medical organizations can boost the logistics of their tasks, reduce pharmaceutical trash, and strengthen revenue projections through the adoption of AI tools. This study aims to provide a structured evaluation of AI-driven solutions for enhancing healthcare supply chain robustness, especially under conditions of uncertainty and complex logistics demands. To determine the investment value of AI applications in HSC management, the current research adopted a revolutionary multi-criteria decision-making (MCDM) methodology tailored to the healthcare sector’s unique demands, including six critical factors. In light of these criteria, six highly technologically advanced AI-based solutions are examined. The implementation of a circular intuitionistic fuzzy set (CIFS) in the instance discussed provides a versatile and expressive way to describe vague and uncertain information. This study leverages the CIF topology to address data complexities and uncover the underlying structural features of a large dataset. At the outset, we adopted the LOPCOW approach, which includes logarithmic variation to assign weights to criteria, whereas the AROMAN method utilizes a powerful two-step normalization technique to rank alternatives, hence guaranteeing a trustworthy and accurate appraisal. A substantial degree of robustness was confirmed by the technique following a comparison of the operators as well as sensitivity testing.

Optimum selection of nature-inspired texture pattern for cutting tool surface using an integrated multi-criteria decision-making approach: a comparative analysis

Purpose This paper aims to focus on the selection of an appropriate nature-inspired texture pattern for cutting tool tribological surface. The selection process uses the recognized skin textures of different snakes scrolling on highly rough and projected surface conditions to analyze suitability of texture based on the texture geometry and machining conditions. The work also aims to propose a texture pattern selection process to incorporate on cutting tool tribological surface. Design/methodology/approach The selection of alternative nature-inspired texture patterns based on the texture pattern geometry and machining properties leads to a multi-criteria decision-making problem. Thirteen criteria are considered for selecting an appropriate texture pattern among 14 alternatives, i.e. nature-inspired texture patterns. In the present work, an integrated analytical hierarchy process (AHP)-TOPSIS, AHP-multi-objective optimization on the basis of ratio analysis (MOORA) and AHP-Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) approaches have been proposed for the selection of an appropriate nature-inspired texture pattern. AHP is used for the formulation of decision-making matrix and criteria weight calculations and ranking of alternatives is done by three methods. Spearman’s correlation compared and found positive relations between rank assigned by methods. Experimental validation is done in Lathe for selected texture effects. Findings The texture parameters C-1 (Width of texture) and C-2 (Depth of texture) are found significant, while T-2 (Blended Krait) and T-6 (Banded Racer-1) texture is found optimal to generate on cutting tool surface. Research limitations/implications Only some nature-inspired texture patterns have been recognized before the selection; an infinite number of textures are available in nature. The size of the texture pattern is difficult to identify by the selection process because each texture pattern may have different effects on tribological surfaces. Practical implications The proposed selection methodology of nature-inspired texture patterns will help identify optimal texture geometry for specific tribological applications. The nature-inspired texture patterned tool has a significant impact on the cutting force and temperature due to its tribological effect on the cutting tool surface; it decreases the power required for machining. The machining characteristics like roughness are found to decrease by using nature-inspired texture patterned tools. Social implications Various nature-inspire texture studies to generate specific effects on the tribological surfaces may be started study for the surface of aircraft, ships, bearings, etc. Small and big fabrication industries may benefit by decreasing the cost of machining using nature-inspired texture-patterned tools. Research society will pay attention to nature’s inspiration. Originality/value Novel snake-skin-inspired texture patterns are recognized and hybrid MCDM methods are proposed to select optimal texture pattern. Proposed method used single time normalization to effectively rank the alternatives. The insights gained from this research can be extrapolated to address similar challenges in selecting nature-inspired textures for various applications. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2024-0163/

Grey Relation System Applications in Multi-Attribute Decision-Making and Forecasting in Financial Management

Objective: The objective of the paper is to verify the Grey relation analysis methods and compare them with known models in financial management. The first part is devoted to MADM (multi-attribute decision methods) and the second one to grey forecasting methods. Method: Localization GRA and globalization GRA multi-criteria models are described and compared with the TOPSIS method. The forecasting GRA(1,1) model based on cumulative data is described, derived and compared with the linear growth model. The models are verified on the data set. Results and Discussion: The globalisation model based on a pair-wise comparison of alternatives is different, and the ranking of alternatives is fully different in comparison with the TOPSIS model. It was found that the localisation GRA model and TOPSIS, both constructed on similarity measures, present similar and comparable results. So, the grey relation approach considers the uncertainty of input data. The forecasting model GRA(1,1) is computed and compared with the growth model. Results show that the growth model is the trendy model, and the non-trendy series is overestimated or underestimated, so the GRA model looks more robust. Research Implications: The problem of imprecise data is investigated. And it is a realistic condition for practical decision-making. The problem of lack of data is often a phenomenon in decision-making. Traditional statistical methods cannot be applied. Therefore, grey relation methodology is one approach to overcoming the problem of imprecise data and lack of data. Uncertainty and lack of data problems are topics important for research and the managerial community. Originality/Value: Sometimes, in financial decision-making, high-quality data or long-term time series is not at our disposal, and on the other hand, decisions have to be made. This problem is often neglected and solved by not-relevant traditional statistical models. Therefore, methods that allow the solving of such problems are useful. It was shown that a grey relation methodology for multicriteria financial performance evaluation and forecasting shows more robust and stable results and could be more relevant for imprecise data or short time series data in financial decision-making problems.

Probabilistic linguistic multi-attribute group decision making method considering the important degrees of experts and attributes

The probabilistic linguistic term set (PLTS) uses multiple linguistic terms to express experts’ opinions and reflects the preference degree of experts for different linguistic terms, which can be flexibly applied to multi-attribute group decision making (MAGDM). First, considering the weights of the ordered positions and importance of each probabilistic linguistic element (PLE), the PLE hybrid weighted averaging (PLEHWA) operator is defined, which can transform a PLTS into a linguistic term with the position-associated weights. Then, the new operations of PLTSs are proposed, which can guarantee that the operation results of the normalized PLTSs retain the complete PLTS. Then the probabilistic linguistic induced ordered weighted averaging (PLIOWA) operator is extended, and its properties are discussed. Subsequently, the probabilistic linguistic MAGDM method considering the important degrees of experts and attributes is proposed. In the probabilistic linguistic MAGDM method, a linear programming model is established based on the PLEHWA operator to determine the important degrees of attributes on each expert. The important degrees of experts are derived based on the differences between the weighted linguistic ratings of different experts. Using the PLIOWA operator, the collective ratings of alternatives are obtained to rank alternatives. Finally, two practical cases are shown in order to express the effectiveness of the proposed method.

A Methodology for Alternatives Ranking by Estimations Forming based on Values from Criteria Decomposition into Options

A Methodology for Alternatives Ranking by Estimations Forming based on Values from Criteria Decomposition into Options

Application of Multi-criteria Decision-making Methods for Forensic Analysis of Mechanical Parts in Vehicle Accidents using q-Rung Orthopair Fuzzy Numbers

This study explores the application of a Multi-Criteria Decision-Making (MCDM) approach based on q-rung orthopair fuzzy numbers to identify the origin of mechanical parts found at vehicle accident scenes. The primary objective is to determine the most likely vehicle to which these parts belong by evaluating key criteria such as compatibility, damage level, serial number matching, and color compatibility. Q-rung orthopair fuzzy numbers offer an advanced method to handle the inherent uncertainty and vagueness associated with forensic evaluations, particularly in scenarios where data is incomplete or imprecise. The proposed methodology involves defining the criteria, assigning membership and non-membership degrees using q-rung orthopair fuzzy sets, and applying an aggregation process to effectively rank alternatives. This approach facilitates flexible decision-making by accommodating different levels of confidence and uncertainty, making it particularly suitable for forensic applications where evidence is often ambiguous. The findings demonstrate that integrating q-rung orthopair fuzzy numbers into the MCDM framework significantly enhances the accuracy and reliability of identifying vehicle components involved in accidents. The proposed methodology provides a systematic tool to support forensic investigations, aiding in the determination of liability and contributing to more robust outcomes in mechanical evidence analysis.

Evaluating municipal solid waste management with a confidence level-based decision-making approach in q-rung orthopair picture fuzzy environment

Municipal solid waste (MSW) management is a critical aspect of urban planning and public health. As societies strive towards environmental sustainability and socio-economic development, robust techniques to transform waste into energy become paramount. Assessment of waste-to-energy (WTE) techniques is based on a spectrum of criteria that are often vague and imprecise. The current study addresses this multi-criteria group decision-making problem of assessing and evaluating WTE methods for MSW management using q-rung orthopair picture fuzzy (qRPF) numbers. The study proposes an innovative combination of the Defining Interrelationships Between Ranked-criteria (DIBR) and Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) methods. The criteria are assessed using the recently developed DIBR method, while the alternatives are assessed using a popular distance-based method, namely CRADIS. Moreover, new confidence level-based aggregation operators for qRPF numbers are proposed and used to aggregate fuzzy data, while a novel triangular divergence-based distance measure is proposed and used to modify the existing CRADIS method. The results show that anaerobic digestion and pyrolysis are the two most preferred WTE methods for MSW management. An extensive comparative analysis demonstrates the applicability of the proposed methodology, while an exhaustive sensitivity analysis confirms the proposed method’s stability. The results of Spearman’s correlation coefficient validate the model’s practicality. The findings of this research yield significant insights beneficial to policymakers, industry stakeholders, and researchers alike. By implementing sustainable waste management strategies, municipalities can improve recycling rates, minimize landfill use, and promote a cleaner, healthier environment for urban populations.

Optimisation Best Sales Assessment Using aCombination of Rank Order Centroid and Additive Ratio Asessment Algorithms

Employee assessment in a company is one of the tricks used to improve employee performance, so that it can be profitable for the company. One of the methods used to determine the best employees is to use a decision support system (DSS). By using DSS, decision making becomes faster, more accurate, efficient and effective. This study was used to determine the best sales at PT Surya Asia Abadi. By combining the Rank Order Centriod (ROC) Algorithm and Additive Ratio Assessment (ARAS) in the DSS, it can produce a better level of prediction accuracy and decision making based on the processed data. Where in this study using 6 (six) variables and 7 (seven) alternatives. The ROC algorithm is used to find the weight of each variable, and the weight obtained from the ROC is then processed using the ARAS algorithm to obtain alternative rankings. The purpose of this study is to make it easier for the Company to determine the best employees with more accurate results. The research results showed that the highest ranking was achieved by alternative AL3 with a value of 0.884 and the lowest was obtained by alternative AL1 with a value of 0.684.

Wind Power Plant Location Selection with Fuzzy Logic and Multi-Criteria Decision-Making Methods

This study aims to examine the effectiveness of the fuzzy multi-criteria decision-making methods, especially Fuzzy Weight by Envelope and Slope (F-WENSLO) and Fuzzy Ranking Alternatives with Weights of Criterion (F-RAWEC) for evaluating the potential locations for wind power plants. The study's objective is to provide a solid framework for deciding which locations are most suitable for wind energy projects, taking into account various criteria and expert opinion. The study includes the evaluation of five potential places including Gürün, Kangal, Divriği, Ulaş and Zara in Sivas province of Turkey. The evaluation criteria include wind speed and direction, altitude, land use, environmental impacts, infrastructure proximity, social acceptance, economic costs, security and risk factors, climatic conditions and legal and permit requirements. Scores from experts from various fields and weights of criteria were determined. The analysis revealed that Ulaş and Kangal got the highest point for the wind power plant installation. As Ulaş gets the highest point due to favorable wind conditions, favorable altitude and advantageous land use; Kangal stood out as a strong candidate because of its acceptable wind speed, positive social acceptance and low economic costs. The study highlights the importance of integrating multiple criteria and expert assessments in the decision-making process. The findings suggest that fuzzy multi-criteria decision-making methods can be effectively supportive of wind power plant site selection. The study provides valuable information for project managers and policy makers, emphasizing the importance of criteria such as security in the choice of location, legal requirements and social acceptance. Future research may expand these findings to examine the integration of additional criteria, alternative locations, and other multi-criteria decision-making techniques.

Optimizing marine vehicles industry: a hybrid analytical hierarchy process and additive ratio assessment approach for evaluating and selecting IoT-based marine vehicles

Rapid developments in the Internet of Things (IoT) have opened the door for game-changing applications in numerous sectors, especially the vehicle industry. There is a rising demand for efficient assessment and decision-making methodologies to pinpoint the most promising choices for the vehicle sector with the introduction of IoT-based maritime vehicles. To overcome this issue, the integrated multi-criteria decision-making analysis (MCDA) paradigm proposed in this research combines the additive ratio assessment (ARAS) and analytic hierarchy process (AHP) approaches to evaluate and choose IoT-based maritime vehicles based on their performance- and authenticity-related criteria in the vehicle sector. The selection issue is hierarchically organized, and the assessment criteria are prioritized using the AHP approach. There are seven performance and authentication related criteria are selected that might aid in the selection procedure. Using the AHP, we are assigned these criteria proportionate weights that reflect their respective significance and interrelationships. AHP, however, falls short of offering a thorough analysis of the alternatives that exist. To overcome these restrictions, this research presents the integration of AHP with the ARAS approach for the ranking of alternatives according to how well they perform against the set criteria. By using the ARAS technique, it is possible to get over the restrictions of AHP and achieve a more thorough assessment of maritime IoT-based vehicles. The efficiency of the framework is proven using empirical data and professional judgment. The findings show that the hybrid method successfully encapsulates the intricate relationships between the factors being evaluated and objectively appraises the potential of IoT-based maritime vehicles for the automotive sector. This study extends to the area by providing an organized and thorough method for assessing and choosing IoT-based maritime vehicles. Considering several factors and their mutual dependence, the hybrid AHP and ARAS technique gives decision-makers a powerful tool for evaluating the potential of IoT-based maritime vehicles in the automotive sector. Smart decisions on the deployment of IoT-based marine vehicles and maximizing the potential they present may be made by beneficiaries in the automotive sector using the study’s results.

Agricultural sustainability assessment in Fars province of Iran through the lens of the elimination multi-criteria decision-making method

The main objective of the present research was to assess agricultural sustainability through the lens of the Elimination Method. Iranian wheat farmers in Fars province comprised the population of the study. Smapling table of Krejcie and Morgan was applied to estimate the statistically plausible sample size. A stratified sampling approach was used for selecting sampling randomly. The research instrument included a structured questionnaire. A panel of experts approved the face and content validities. Average Variance Extracted (AVE), Composite Reliability (CR), and items correlations were used to confirm the convergent validity of the constructs. The average shared squared variance (ASV) and the maximum shared squared variance (MSV) criteria were applied to examine the divergent validity of constructs. Employing the Elimination Method to assess agricultural sustainability in four counties of Fars revealed that Marvdasht is the most sustainable area for the development of wheat cultivation. Because it was able to fulfill 16 indicators out of 23 indicators of agricultural sustainability. The high level of productivity and efficiency of the wheat crop in this region is one of the main reasons for the relatively high sustainability of wheat production practices. Furthermore, the results demonstrated that Darab was the second most suitable region for developing wheat production. Based on the Elimination Method, the region was able to fulfill six indicators out of 23 agricultural sustainability assessment indicators. According to the results of ranking alternatives using the Elimination Method, the two counties of Kazeroun and Eghlid could not acceptably fulfill indicators of agricultural sustainability. In other words, it seems that these two regions are not suitable for the development of wheat production in Fars province. In the end, it was recommended that the Elimination Method can be a useful approach for urgent decision-making and agricultural managers and decision-makers in Fars province should focus more intensively on the development of wheat production in Marvdasht.

Advancing Economical and Environmentally Conscious Electrification: A Comprehensive Framework for Microgrid Design in Off‐Grid Regions

AbstractThe design of renewable energy systems traditionally emphasizes life cycle costs, often focusing primarily on emissions rather than a comprehensive life cycle impact assessment. This research proposes a four‐tier methodology to balance cost‐effectiveness and sustainability in the electrification of remote areas. Tier 1 focuses on understanding the community context by analyzing electrical load profiles, meteorological data, and component specifications for microgrid design. Tier 2 evaluates the feasibility of various systems, optimizing them through cost analysis and Multi‐Criteria Decision‐Making (MCDM) to rank alternatives. Tier 3 assesses environmental impacts using life cycle assessment, ranking alternatives based on environmental criteria. Tier 4 integrates cost and environmental rankings to determine the most suitable energy configurations, followed by sensitivity analysis to ensure robust decision‐making. The methodology is validated through a case study of an unelectrified remote community, demonstrating that the PV‐Wind Turbine‐Biomass Generator‐Converter configuration is the most robust alternative, proving to be the optimal choice in 50% of the analyzed scenarios, achieving a Cost of Energy of 0.213 USD/kWh while minimizing environmental impact across all 18 criteria considered over a 25‐year life cycle. This novel framework offers a scalable approach to designing renewable energy systems, enhancing sustainable electrification efforts in developing regions.

Fuzzy heterogeneous multi-attribute group decision with dual interaction of attributes and alternatives

Heterogeneous preferences and the dual interaction of attributes and alternatives are two important features in real-world multi-attribute group decision-making (MAGDM). However, fewer MAGDM studies simultaneously consider heterogeneous preferences and dual interaction, reducing method efficacy and practicability. This study proposes a novel fuzzy heterogeneous MAGDM method considering the dual interaction of attributes and alternatives. First, a similarity-based optimization method is constructed to process fuzzy heterogeneous preferences with triangular fuzzy numbers (TFNs), intuitionistic fuzzy numbers (IFNs), and interval-valued intuitionistic fuzzy numbers (IVIFNs), decreasing the complexity and subjective fuzziness of processing. The dual interaction of attributes and alternatives is objectively measured by constructing the alternatives deviation optimization model and multi-attribute alternative interaction network, which is the inaugural achievement in MAGDM. Next, the novel Shapley-Choquet dual interaction superiority integral is defined to fuse non-additive preferences, overcoming the absolute superiority hypothesis of alternative ranking and improving ranking accuracy. Finally, an application study of hot dry rock siting, along with a detailed analysis and discussion, demonstrate the effectiveness, reliability and superiority of the proposed MAGDM method. The TDDV (total discrimination degree based on variance) respectively increased by 52.381%, 99.259%, and 99.647% in the three comparison cases, revealing the significant role of fuzzy heterogeneous preferences and dual interaction of attributes and alternatives in improving the accuracy and reliability of MAGDM and should be simultaneously considered in MAGDM modeling.

Intuitionistic Fuzzy Ordinal Priority Approach with Grey Relational Analysis

Multi-attribute decision-making (MADM) is a methodology for solving decision problems with a finite set of alternatives. The several methods of MADM require weights for the criteria and the alternatives to provide a solution. The Ordinal Priority Approach (OPA) is a recently proposed method for MADM that innovates; it does not require these inputs, just the rankings of criteria and alternatives. This article introduces a new hybrid method for MADM: the Intuitionistic Fuzzy Ordinal Priority Approach with Grey Relational Analysis (OPA-IF-GRA). OPA-IF-GRA combines GRA with OPA-IF, a newer extension of OPA that includes intuitionistic fuzzy sets to incorporate uncertainty into the decision-making process. The article presents an OPA-IF-GRA application for solving an electronics engineering problem, considering four criteria and six alternatives. The solution of OPA-IF-GRA is compared with the solutions obtained with three other MADM methods.