Journal of Contemporary Decision Science
The Journal of Contemporary Decision Science (JCDS) is a peer-reviewed, international scholarly journal dedicated to advancing knowledge and practice in the field of decision science. The journal publishes high-quality original research that addresses contemporary decision-making challenges arising in complex, dynamic, and uncertain environments. JCDS serves as an interdisciplinary forum for researchers and practitioners working at the intersection of decision theory, decision support systems, management science, engineering, artificial intelligence, and sustainability. The journal emphasizes both theoretical development and applied research, with a strong focus on methodologies and frameworks that support effective and informed decision-making in real-world contexts. The journal welcomes contributions that develop or apply advanced decision-making models, including—but not limited to—multi-criteria decision analysis, intelligent and data-driven decision support systems, uncertainty modeling, optimization techniques, and behavioral decision approaches. Particular attention is given to studies demonstrating practical relevance, managerial implications, and societal impact. The Journal of Contemporary Decision Science adopts a rigorous double-blind peer-review process to ensure the scientific quality, originality, and relevance of published articles. The journal publishes research articles, review papers, methodological contributions, and applied case studies from a broad range of disciplines, including management, engineering, economics, computer science, environmental studies, and public policy. By fostering interdisciplinary collaboration and methodological innovation, JCDS aims to contribute to the development of robust decision frameworks that support sustainable development, organizational performance, technological advancement, and evidence-based policy-making.
Association for Optimization, Risk and Business Intelligence in Transformation
An Interval-Valued Fermatean Fuzzy SWOT-AHP-Based Model for Assessing Artificial Intelligence Adoption for Sustainable Development Goal 4
This study adopted an interval-valued Fermatean fuzzy analytical hierarchy process (IVFF-AHP)-based strategic approach to assess the strengths, weaknesses, opportunities, and threats (SWOT) related to the role of artificial intelligence (AI) in achieving quality education under Sustainable Development Goal 4 (SDG 4). First, 14 SWOT factors were identified based on a literature review. Data were then collected from three domain experts, and the IVFF-AHP approach was applied to determine the weights of the SWOT factors. The findings reveal that AI-driven personalized learning content (S2) and intelligent tutoring systems for personalized and special-needs education (O2) serve as key enablers of quality education. In contrast, insufficient training in the use of educational technology (W3) and inequality in digital and AI access (T2) constitute major barriers to achieving this goal. These findings provide a structured basis for prioritizing investments in personalized learning technologies, teacher training, and equitable digital infrastructure. The proposed framework also demonstrates the usefulness of IVFF-AHP for evaluating educational strategies when expert judgments involve uncertainty and hesitation. Moreover, the framework can be adapted to assess AI-supported education policies in different institutional and national contexts. The study makes a meaningful contribution to the decision sciences and management literature by offering practical insights for policymakers seeking to achieve quality education and concludes by outlining clear avenues for future research.
Copyright (c) 2026 Mouhamed Bayane Bouraimar (Author) https://creativecommons.org/licenses/by/4.0
Evaluating E-Commerce Websites under Uncertainty Using a Hybrid Fuzzy Knowledge–Entropy–TODIM Framework
Assessing the reliability of e-commerce platforms is a challenging task due to the large number of interrelated evaluation criteria and the uncertainty inherent in decision-making data. To address this issue, this study proposes a novel decision-support framework that integrates Triangular Fuzzy Numbers (TFNs), the entropy weighting method, and the TODIM approach. TFNs are employed to model uncertainty in expert assessments, the entropy method determines the objective importance of the evaluation criteria, and TODIM is used to rank the e-commerce platforms. A case study involving five international e-commerce platforms evaluated against eleven criteria demonstrates the applicability of the proposed framework. Its performance is further validated through comparisons with several well-established multi-criteria decision-making (MCDM) methods. In addition, sensitivity analysis using two different approaches confirms the robustness and reliability of the obtained rankings. The results demonstrate that the proposed framework provides consistent and reliable rankings, making it an effective decision-support tool for evaluating the reliability of e-commerce platforms.
Copyright (c) 2026 . Aanchal, . Sonia, Satish Kumar (Author) https://creativecommons.org/licenses/by/4.0
Digital readiness assessment of Western Balkan countries: A MCDM framework for WB DESI evaluation
Digitalisation has become a major driver of economic growth, competitiveness, and societal development, making the assessment of countries' digital readiness an increasingly important research area. The Digital Economy and Society Index (DESI) is widely recognised as a comprehensive framework for monitoring and comparing digital development. This study evaluates the digital readiness of the Western Balkan countries using the Western Balkans Digital Economy and Society Index (WB DESI). An entropy-weighted TOPSIS model serves as the primary evaluation method, while several established multi-criteria decision-making (MCDM) techniques are employed to examine the robustness, reliability, and stability of the results. The analysis identifies Serbia as the highest-ranked country in terms of digital readiness, followed by Montenegro, Albania, Bosnia and Herzegovina, and North Macedonia. Robustness analysis demonstrates a high degree of consistency across the applied MCDM methods, indicating that the obtained rankings are stable and largely independent of the selected evaluation technique. By addressing both geographical and methodological gaps in the existing literature, this study provides a comprehensive decision-support framework that can assist policymakers in monitoring digital convergence and designing more effective digital transformation strategies across the Western Balkans.
Copyright (c) 2026 Aleksandra Radić, Sanela Arsić (Author) https://creativecommons.org/licenses/by/4.0
Weighted Tree Seed Teaching–Learning-Based Optimization (WTSTLBO) Algorithm for the Static Economic Load Dispatch Problem
This study proposes a novel Weighted Tree-Seed Teaching–Learning-Based Optimization (WTSTLBO) algorithm to solve the Static Economic Load Dispatch (SELD) problem. The proposed algorithm integrates the exploration capability of the Tree-Seed Algorithm (TSA), the exploitation capability of the Teaching–Learning-Based Optimization (TLBO) algorithm, and an adaptive inertia-weight strategy to improve convergence speed and optimization accuracy. The SELD problem aims to minimize the total fuel cost while satisfying power balance, generator operating limits, transmission losses, and ramp-rate constraints. The performance of the proposed WTSTLBO algorithm is evaluated on standard IEEE 6-unit, 15-unit, and 40-unit test systems. Simulation results demonstrate that WTSTLBO outperforms the Genetic Algorithm (GA), Particle Swarm Optimization (PSO), TSA, Weighted TSA (WTSA), TLBO, Weighted TLBO (WTLBO), and the hybrid Tree-Seed Teaching–Learning-Based Optimization (TSTLBO) algorithm in terms of fuel cost, convergence speed, and robustness. The obtained results confirm the effectiveness and superiority of the proposed algorithm for solving complex large-scale economic load dispatch problems.
Copyright (c) 2026 Aanchal, Parvesh Kumar, Sarika Jain, Rekha Rani (Author) https://creativecommons.org/licenses/by/4.0
A Network-Based Spatial Assessment of Road Accessibility and Logistics Vulnerability
Road accessibility is a key determinant of logistics resilience, territorial connectivity, emergency response capability, and regional development. In geographically extensive countries with uneven settlement patterns, logistics vulnerability is influenced not only by travel distance but also by network topology, corridor dependency, and the availability of alternative routes. Despite Libya's strategic position within regional trade networks, a comprehensive network-based framework for evaluating municipal logistics vulnerability has been lacking. This study develops a spatial network assessment framework to analyze road accessibility and logistics vulnerability across Libya. The national road system is modeled as a strategic logistics network connecting twenty major municipalities with maritime gateways and inland logistics hubs. Six indicators are incorporated to capture complementary dimensions of accessibility and network performance: network distance to ports, network distance to logistics hubs, distance to the coastal corridor, road passability exposure, route criticality exposure, and network redundancy constraint. Objective indicator weights are determined using the CRITIC method, and a composite logistics vulnerability index is constructed to classify municipalities and identify critical transport corridors. The findings reveal two dominant drivers of logistics vulnerability: the geographical isolation of southern and peripheral municipalities from coastal gateways and the concentration of national freight flows along a limited number of highly critical corridors. Kufra, Ghadames, Murzuq, Nalut, Ajdabiya, Gharyan, Sirte, and Zliten are identified as the most vulnerable municipalities, while the Misrata–Sirte, Ajdabiya–Sirte, Ajdabiya–Benghazi, and Misrata–Sabha corridor systems emerge as the most critical network segments. The proposed framework provides a robust spatial decision-support tool for infrastructure investment prioritization, corridor resilience planning, logistics network management, and evidence-based regional development policy in Libya.
Copyright (c) 2026 Ibrahim Badi, Dragan Pamucar (Author) https://creativecommons.org/licenses/by/4.0
An Intuitionistic Fuzzy Hamming and Euclidean Hesitance Distance Measure for Environmental Sustainability Decision-Making
Intuitionistic fuzzy distance measures play a fundamental role in modeling uncertainty in multi-criteria decision-making; however, most existing measures do not explicitly exploit hesitation information, which may limit their ability to distinguish alternatives under uncertain conditions. To address this limitation, this study introduces two novel intuitionistic fuzzy distance measures, namely the Euclidean hesitance distance and the Hamming hesitance distance. Unlike conventional approaches that primarily rely on membership and non-membership degrees, the proposed measures explicitly incorporate hesitation, providing a more comprehensive representation of uncertainty and ambiguity. Their mathematical properties are formally established to ensure theoretical consistency and applicability in decision-making environments. To demonstrate their practical usefulness, the proposed measures are applied to an environmental sustainability assessment problem, where evaluations are often characterized by incomplete, vague, and conflicting information. A systematic algorithm is developed to compute and compare the sustainability performance of multiple regions using both distance measures. The application illustrates how incorporating hesitation information enhances the representation of uncertainty and supports a more informative evaluation process. The proposed framework extends the methodological foundations of intuitionistic fuzzy distance measures and provides an effective tool for sustainability assessment and other decision-making problems involving uncertain and ambiguous information.
Copyright (c) 2026 M Kaviyarasu, Nasreen Kausar (Author) https://creativecommons.org/licenses/by/4.0
A Hamacher Aggregation-Based Pythagorean Fuzzy Z-Number MCDM Framework for Sustainable Urban Green Space Planning
Evaluation of alternatives is a complex task in real-world decision-making, where ambiguity and uncertainty are inherent. To address these challenges, this study proposes an integrated Pythagorean Fuzzy Z-Number (PyFZN)-based framework for multi-attribute decision-making (MADM) in urban green space planning. PyFZNs extend intuitionistic fuzzy sets by incorporating both fuzzy membership information and reliability measures, enabling a more realistic representation of uncertainty and a more effective modeling of expert judgments. Hamacher norms with adjustable parameters are employed to improve the aggregation process, providing greater flexibility and consistency in information fusion. Based on these norms, a family of aggregation operators is developed to enhance decision-making performance under uncertain conditions. The proposed PyFZN-MADM framework is applied to evaluate urban green space alternatives using criteria such as population coverage, accessibility, environmental impact, cost, and social benefits. A metropolitan case study demonstrates the applicability of the proposed framework for ranking alternatives, optimizing resource allocation, and improving urban livability. The results indicate that the proposed approach is robust, sensitive, and reliable, outperforming existing methods in handling uncertainty and prioritizing alternatives. Comparative analysis further confirms its effectiveness in supporting informed, consistent, and reliable decision-making in complex urban planning environments.
Copyright (c) 2026 Rani Sumaira Kanwal, Saqib Mazher Qurashi, Nasreen Kousar, Fatima Tul Zahra (Author) https://creativecommons.org/licenses/by/4.0
A Multi-Stage Fuzzy Decision Analytics Framework for Evaluating Climate-Induced Risk Factors of Catastrophic Healthcare Expenditure
Climate change is increasingly intensifying health and financial vulnerabilities in rural communities, often leading to catastrophic healthcare expenditures due to rising disease burden, income instability, and reduced adaptive capacity. Identifying and prioritizing the most critical climate-induced risk factors is therefore essential for designing effective health financing and social protection strategies. This study proposes an integrated fuzzy decision analytics framework to evaluate climate-related determinants of catastrophic healthcare expenditure under conditions of uncertainty and imprecise expert judgment. The traditional Fine-Kinney risk assessment framework was extended using q-rung orthopair fuzzy sets to represent ambiguity in expert evaluations. The Comparisons Between Ranked Criteria (COBRAC) method was employed to estimate risk scores for the Fine-Kinney dimensions, while the modified Preference Selection Index (MPSI) and Simple Additive Weighting (SAW) methods were used to prioritize climate-induced healthcare risk factors. Expert assessments from sixteen specialists were aggregated using the Einstein weighted aggregation operator. To verify the robustness and reliability of the proposed framework, a three-stage validation procedure and sensitivity analysis were performed. The findings reveal that increased debt burden (0.1328), disease burden (0.1299), and challenges in maintaining livelihoods (0.1291) are the most influential contributors to catastrophic healthcare expenditure in climate-vulnerable rural communities. The proposed framework provides important implications for health economics and healthcare management by supporting evidence-based prioritization of climate adaptation policies, financial risk protection measures, and rural healthcare planning aimed at reducing the economic consequences of climate-related health shocks.
Copyright (c) 2026 Sanjib Biswas, Biplab Biswas, Aparajita Sanyal, Prasenjit Chatterjee, Dragan Pamucar (Author) https://creativecommons.org/licenses/by/4.0
Comprehensive Insights into Bibliometric Analysis: Tools, Techniques, Applications, Challenges, and Emerging Solutions for Research Excellence
Due to the rapid growth in the volume of scientific literature, there is an increasing need for systematic methods to evaluate scientific output, identify research trends, and support evidence-based decision-making. Bibliometric analysis has become one of the most widely used tools for evaluating scientific productivity, research impact, collaboration networks, and knowledge structures. However, despite its growing popularity, the field of bibliometrics still lacks consistency in terms of the tools, methodologies, reporting practices, and technologies used in bibliometric analysis. The aim of this study is to provide a comprehensive overview of the current state of bibliometrics and propose a practical guide to conducting rigorous and comprehensive bibliometric analyses. Through a systematic literature review and synthesis, the study examines four main aspects of bibliometric analysis: tools, methodologies, application areas, and reporting quality. Major bibliometric software platforms, namely VOSviewer, Bibliometrix/Biblioshiny, CiteSpace, and Gephi, are compared in terms of their functionality, strengths, and limitations. In addition, the study identifies the major stages of bibliometric research, including search strategy development, data collection, data preprocessing, science mapping, scientific performance analysis, visualization, and reporting. The importance of transparency and reproducibility in bibliometric research is highlighted through the presentation of the GLOBAL reporting guideline and the PRISMA-ScR framework. The results show that no single tool can perform all types of bibliometric analyses effectively, while combining multiple tools provides a more comprehensive understanding of the research landscape. Furthermore, the study identifies methodological rigor—particularly the selection of an appropriate database, the development of a robust search strategy, and thorough data cleaning—as a key determinant of high-quality bibliometric research. The study demonstrates how bibliometrics has evolved from a descriptive evaluation instrument into a strategic research assessment tool that supports policy development and promotes research excellence. Finally, emerging developments in artificial intelligence, open science, altmetrics, and open citations are expected to have a significant impact on the future of bibliometric research. The proposed framework can serve as a practical guide for researchers, editors, research managers, and policymakers conducting bibliometric analyses.
Copyright (c) 2026 Gülay Demir (Author) https://creativecommons.org/licenses/by/4.0
Assessment and Prioritization of Data Analysis Challenges Using Fuzzy Multi-Criteria Decision-Making
Data analysis using different techniques has become increasingly important across a wide range of application domains. However, several challenges arise when datasets are collected, processed, and analysed. This chapter aims to identify and evaluate the most significant challenges associated with data analysis. The proposed methodology is based on multi-criteria decision-making (MCDM). The identified challenges are classified into ten categories: Data Quality Issues, Data Integration, Data Volume, Data Variety, Data Privacy and Security, Bias and Misinterpretation, Skill Gap, Real-Time Analysis, Cost and Infrastructure, and Communicating Results. Two MCDM methods are employed for the numerical evaluation. The data are collected in the form of trapezoidal intuitionistic fuzzy numbers (TrIFNs) to represent the uncertainty and ambiguity inherent in expert assessments. Finally, a comparative analysis is conducted to evaluate the reliability and flexibility of the proposed model.
Trapezoidal intuitionistic fuzzy number (TrIFN)
Copyright (c) 2026 Kamal Hossain Gazi, Fariba Azizzadeh, Aditi Biswas, Prodip Bhaduri, Sankar Prasad Mondal (Author) https://creativecommons.org/licenses/by/4.0
Smart Technologies for Water Sewage Systems and Decision-Making with Circular Spherical Fuzzy Framework
Smart technologies play a significant role in the management of sewage and drainage systems by improving operational efficiency, particularly in critical situations. In modern cities, water distribution networks, drainage systems, and wastewater treatment plants face increasing pressure due to rapid urbanization and population growth. Selecting advanced smart technologies for the effective management of sewage and drainage systems requires an efficient decision-making framework capable of evaluating multiple technological alternatives against conflicting criteria. In this context, the circular spherical fuzzy set (Cir-SFS) provides an effective framework for handling uncertainty and ambiguity in complex real-world decision-making problems. This study extends the theoretical concept of Frank algebraic operations within the circular spherical fuzzy (Cir-SF) environment. Furthermore, a family of robust aggregation operators, including the Cir-SF Frank Weighted Average (Cir-SFFWA) and Cir-SF Frank Weighted Geometric (Cir-SFFWG) operators, is developed. The weighted aggregate sum product assessment (WASPAS) method is also extended under the Cir-SF environment to address multi-criteria decision-making (MCDM) problems. An illustrative numerical example is presented to demonstrate the applicability and effectiveness of the proposed methodology in ranking smart technologies based on multiple conflicting criteria. Finally, comparative and sensitivity analyses are conducted to verify the robustness, consistency, and superiority of the proposed approach over existing decision-making methods.
Copyright (c) 2026 Abrar Hussain, Muhammad Ahmad, Kifayat Ullah, Zeeshan Ali, Oumaima Saidani (Author) https://creativecommons.org/licenses/by/4.0
A Novel Computational Approach for Linear Fractional Programming Problems in a Neutrosophic Environment
This paper introduces a novel computational approach for solving neutrosophic linear fractional programming (NLFP) problems in which all objective coefficients, technological coefficients, resource values, and decision variables are represented by triangular neutrosophic numbers. The proposed method addresses the uncertainty, inconsistency, and indeterminacy inherent in real-world optimization problems by employing a neutrosophic environment characterized by truth, indeterminacy, and falsity membership degrees. A systematic transformation procedure is developed to reformulate the original NLFP model into an equivalent neutrosophic linear programming model through a parametric representation of the fractional objective function. Subsequently, a specialized ranking function is employed to convert triangular neutrosophic numbers into crisp values, enabling the formulation of a solvable linear programming model while preserving the essential uncertainty information contained in the original problem. The proposed methodology simplifies the computational procedure, reduces model complexity, and provides an efficient mechanism for obtaining optimal solutions under neutrosophic uncertainty. A detailed solution algorithm is presented, and a numerical example is provided to demonstrate the applicability and effectiveness of the proposed method. The obtained results are compared with several existing approaches from the literature, demonstrating that the proposed method produces reliable and competitive solutions while maintaining computational efficiency and simplicity. Overall, the findings confirm the suitability of the proposed methodology for solving NLFP problems and highlight its potential for extension to more complex optimization models under uncertain and indeterminate decision-making environments.
Copyright (c) 2026 Rabie Mosaad Rabie, Abd El-Wahed Khalifa (Author) https://creativecommons.org/licenses/by/4.0
Sustainability Challenges for Non-Governmental Funded Higher Educational Institutions: A q-rung Orthopair Fuzzy Sets Coupled MCDM Approach
Financial aid plays a crucial role in the sustainability and development of higher educational institutions, particularly those operating without governmental funding. This study proposes a decision-making framework for identifying and prioritizing the key challenges faced by non-governmentally funded higher educational institutions. To address this problem, a hybrid Multi-Criteria Decision-Making (MCDM) approach is employed, where the MEREC method is used to determine the weights of evaluation criteria and the MARCOS method is applied to rank the identified challenges. To effectively handle uncertainty and ambiguity in expert judgments, q-Rung Orthopair Fuzzy Numbers (q-ROFNs) are integrated into the framework. Furthermore, sensitivity analysis is conducted to assess the robustness and reliability of the obtained results under different conditions. The results reveal the most influential challenges affecting the financial sustainability and operational performance of these institutions, providing valuable insights for strategic decision-making. In addition, the proposed framework offers a systematic and flexible tool that can be adapted to similar decision-making problems in the education sector. Overall, the q-ROF-MEREC-MARCOS framework supports policymakers, institutional administrators, and stakeholders in developing effective strategies for enhancing the resilience and sustainable growth of non-governmentally funded higher educational institutions.
Challenges of Non-Government-Funded Higher Educational Institutions
Copyright (c) 2026 Tripti Basuri, Kamal Hossain Gazi, Prodip Bhaduri, Srabani Guria Das, Sankar Prasad Mondal (Author) https://creativecommons.org/licenses/by/4.0
A Novel Similarity Measure for Generalized Nonlinear Trapezoidal Fuzzy Numbers with Applications to Multi-Attribute Decision Making
There are several similarity measure techniques for generalized trapezoidal fuzzy numbers. However, no similarity measure technique has yet been proposed for generalized non-linear trapezoidal fuzzy numbers. In the present work, a new similarity measure approach for two generalized non-linear trapezoidal fuzzy numbers is proposed based on distance and center of gravity (COG) measures. Several propositions related to the proposed method are also discussed. Furthermore, a comparative analysis with existing methods is carried out through illustrative examples, demonstrating that the proposed method is applicable to both generalized non-linear and generalized linear trapezoidal fuzzy numbers. Finally, a multi-attribute decision-making problem is considered, and a new algorithm is introduced to identify the best alternative for a decision maker using the proposed similarity measure method.
Generalized Non Linear Trapezoidal Fuzzy Number
Copyright (c) 2026 Kartik Patra (Author) https://creativecommons.org/licenses/by/4.0
Data-driven Controller Selection for Trajectory Control of Unmanned Ground Vehicles Through an Integrated Fuzzy Decision Framework
Technological advances have made unmanned ground vehicles (UGVs) an integral part of modern life. Interest in UGVs has grown significantly, particularly in applications where human safety is a priority or where operations must be performed in environments inaccessible to humans. In recent years, research on UGVs has focused on key topics such as obstacle avoidance, path planning, mapping, localization, navigation, and trajectory tracking. Among these, trajectory tracking remains one of the most challenging problems and has attracted considerable attention from researchers. Furthermore, selecting an appropriate controller for trajectory tracking represents a critical decision-making problem. This study makes an important contribution to the literature, as comprehensive analyses employing multi-criteria decision-making (MCDM) methods for controller selection in UGV trajectory tracking remain scarce. In particular, systematic and comparative evaluations designed to support the controller selection process are still limited. In this context, this study is among the first to simultaneously apply fuzzy AHP and fuzzy ARTASI, two MCDM methods, to address the controller selection problem in UGV trajectory tracking. First, fuzzy AHP was employed to determine the weights of the evaluation criteria. Subsequently, six controllers were evaluated and ranked using the fuzzy ARTASI method based on seven evaluation criteria and linguistic assessments. Finally, the consistency and robustness of the obtained results were verified through a comparative analysis using the fuzzy MABAC, fuzzy TOPSIS, fuzzy VIKOR, and fuzzy MOORA methods. The findings indicate that a hybrid adaptive–sliding mode controller is the most suitable alternative for UGV trajectory tracking.
Copyright (c) 2026 Hayriye Tuğba Sekban, Şeyma Emeç, Abdullah Başçi (Author) https://creativecommons.org/licenses/by/4.0
Analysis of m-Polar CFR-WASPAS Model for Smart Parking in Taiwan: A Personalized Recommender System for Urban Drivers
Analysis of smart parking in Taiwan with a personalized recommender system for urban drivers is a crucial and valuable topic because Taiwan’s high vehicle usage, dense urban environment, and limited parking availability create consistent congestion and driver frustration, making parking a crucial urban mobility problem. Therefore, we develop an m-polar complex fuzzy rough model and its operational laws for the valuation of averaging and geometric operators. These operators can aggregate a finite collection of alternatives into a singleton alternative. Further, we also design a weighted aggregation sum-product model based on derived operators for the analysis of smart parking in Taiwan using a personalized recommender system for urban drivers. A personalized smart parking recommender system provides a particular solution based on predictive analytics, real-time information, and driver performance to guide users to the most suitable and valuable parking space and reduce problems. Finally, we derive some numerical examples for the valuation and comparative analysis between the proposed and existing models to describe the supremacy and validity of the developed approaches
Copyright (c) 2026 Tung Chun Yang, Zeeshan Ali (Author) https://creativecommons.org/licenses/by/4.0
AHP-Based Aczel–Alsina Prioritization under T-Spherical Fuzzy Information for Artificial Intelligence in Smart Systems
This paper presents a novel total prioritized aggregation framework based on the AHP-driven Aczel–Alsina t-norm (AA-TN) and Aczel–Alsina t-conorm (AA-TCN) within the context of T-spherical fuzzy sets (T-SFSs) to address uncertainty in complex decision-making problems. The T-SFS framework provides a flexible and effective environment for representing uncertain information. Furthermore, the AA-TN and AA-TCN offer adaptable operational mechanisms through the parameter , while prioritized aggregation enables the efficient incorporation of the relative importance of attributes. To integrate these concepts, two novel aggregation models, namely the T-spherical fuzzy Aczel–Alsina averaging (T-SFAAA) and T-spherical fuzzy Aczel–Alsina geometric (T-SFAAG) approaches, are developed. In addition, the analytical hierarchy process (AHP) is employed to determine the priority weights of criteria in multi-attribute decision-making (MADM) problems.
Copyright (c) 2026 Mehwish Sarfraz, Duško Tešić, Gülay Demir (Author) https://creativecommons.org/licenses/by/4.0
Ranking Higher Education Institutions Using Entropy–VIKOR with Generalized Pentagonal Intuitionistic Fuzzy Numbers
Ranking the higher educational institutes is a very challenging task. In ranking higher educational institutions, many criteria and sub-criteria must be considered. These criteria may be beneficial or non-beneficial, which also increases the problem's complexity. Here, our aim is to develop an advanced framework to determine the rankings of higher educational institutions, incorporating uncertainty in the environment and the dataset. Here, we will take the help of Generalized Pentagonal Intuitionistic Fuzzy Numbers (GPIFNs), which are an extension of Pentagonal Fuzzy Numbers (PFNs), to capture the uncertainty of the model. For this purpose, we will include two popular MCDM methodologies: the Entropy weighted method for evaluating the criteria weights and the VIKOR method for ranking alternatives across different higher educational institutions. In this model, the opinions of different decision experts will be taken in linguistic terms as a dataset and will further be converted to GPIFNs. Lastly, sensitivity analysis and comparative analysis will be performed to assess the stability and robustness of the results obtained with this model. This model will be very effective for making policies for the advancement of higher educational institutions, and it will provide a clear view of the strengths and weaknesses of an institution to the administrator so that they can take necessary strategies to improve the quality of education.
Generalized pentagonal intuitionistic fuzzy numbers
Copyright (c) 2026 Tripti Basuri, Kamal Hossain Gazi, Srabani Guria Das, Sankar Prasad Mondal (Author) https://creativecommons.org/licenses/by/4.0
An Intelligent Industry 5.0 Logistics Decision System: Circular Supply Chain Management With Fermatean Neutrosophic Hypersoft Sets And Machine Learning
Industry 5.0 has transformed the logistics value chain with human-centricity, sustainability, and resilience at its core, with circular supply chain strategies playing a pivotal role through reuse, remanufacturing, and recycling. Circular logistics, however, brings complex uncertainties such as quality issues with returns, volume fluctuations, conflicting information, and multi-attribute decision-making, which cannot be captured by traditional models. The paper proposes a new model based on Fermatean Neutrosophic Hypersoft Sets (FNHSS) and machine learning to optimize circular supply chain management in the era of Industry 5.0. The framework makes four contributions: first, it provides a mathematical framework for FNHSS that accounts for three-dimensional uncertainty (truth, indeterminacy, and falsity) under Fermatean constraints (0 ≤ φ³ + υ³ ≤ 1), along with hypersoft multi-attribute parameterization; second, it introduces dual solution paths, including numerical defuzzification optimized using the Modified Distribution method and a deep reinforcement learning architecture based on a transformer encoder and a graph neural network (GNN); third, it proposes a circular economy reward function that balances logistics costs, returns, and circularity; fourth, it validates the framework through an automotive electric vehicle battery recycling case study. Empirical results show that the uncertainty capture index reaches 0.87, significantly above fuzzy machine learning baselines, with cost improvements of 6.16% and 1.99% compared to Type-1 fuzzy and intuitionistic fuzzy approaches, respectively. The numerical and ML solutions show no significant difference (p > 0.05), and the trained ML model enables constant-time inference essential for dynamic Industry 5.0 contexts. The FNHSS-ML architecture offers a robust approach for resilient, adaptable, and sustainable intelligent logistics systems.
Copyright (c) 2026 Muhammad Tahir, Muhammad Imran Shahid (Author) https://creativecommons.org/licenses/by/4.0
An Artificial Intelligence-Based Framework for Market Optimization of the Pharmaceutical Industry
Pharmaceutical pricing is increasingly difficult because firms must respond to shifting demand, competitive pressure, public-health trends, and strict regulatory and ethical expectations at the same time. Static and rule-based pricing methods often react too slowly to these changing conditions and struggle to balance profitability with patient access and compliance. This study proposes an AI-based dynamic pricing framework that combines deep reinforcement learning with market dynamics modeling inspired by partial differential equations. The framework learns pricing policies directly from evolving market signals, including inventory conditions, competitive behavior, and public-health indicators represented through an Ornstein–Uhlenbeck process. An ethical governance layer is built into the system through reward penalties and action constraints so that pricing decisions remain aligned with responsible healthcare practice and regulatory requirements. A distributed training architecture is also introduced to support large pharmaceutical portfolios and real-time decision environments. Experimental results across six therapeutic areas show that the proposed approach outperforms conventional pricing strategies, delivering higher profit while preserving strong market share, patient access, and full regulatory compliance. These findings suggest that AI-driven pricing can support more adaptive, evidence-based, and ethically grounded decision-making in pharmaceutical markets. Overall, the study demonstrates that combining reinforcement learning, stochastic market modeling, and built-in governance can produce a scalable and practical framework for sustainable pharmaceutical price optimization.
Copyright (c) 2026 Divanshu Mittal (Author) https://creativecommons.org/licenses/by/4.0
Criteria Weighting Methods in Multi-Criteria Decision Making: A Comprehensive Review of Subjective, Objective, and Hybrid Approaches
In Multi-Criteria Decision Making (MCDM) method, the criteria weighting is one of the most critical parameters that affect the stability of the ranks, sensitivity of the decision and the robustness of the model. A variety of methods have been developed to weight; these can be categorized as subjective, objective and hybrid. The weights derived from the statistical or information theoretic properties of the inherent data are called objective methods, while those based on expert judgment and cognitive evaluations are called subjective methods. Combining both paradigms is known as hybrid methods and provides a more reliable and unbiased approach. Although significant methodological progress has been made, there remain many different methods, variations in method selection, validation and contextual applicability. The paper comprehensively reviews and critically analyzes the most important criteria weighting techniques in MCDM. It categorizes and compares current solutions, analyzes their theory and limitations, and explores their use in different fields including engineering, energy systems, supply chain and financial decision-making. The research also identifies the new trends like fuzzy logic integration, machine learning based weighting and adaptive decision frameworks. Thirdly, the main research gaps and future research directions are suggested to lead next generation weighting models in MCDM under uncertainty.
Copyright (c) 2026 Arkyadeep Sarkar, Shankha Shubhra Goswami, Dhiren Kumar Behera, Darko Bozanic (Author) https://creativecommons.org/licenses/by/4.0
Three Decades of Multiple Criteria Decision-Making (MCDM) Methods (1996–2026): A Comprehensive Review of Advancements, Applications, and Future Directions
Multiple Criteria Decision-Making (MCDM) techniques have proven to be indispensable tools for addressing decision problems involving multiple conflicting criteria across a wide range of applications. This review paper comprehensively examines the evolution, applications, and future directions of MCDM methods from 1996 to 2026. A systematic literature review was conducted using publications retrieved from multiple scientific databases, and the collected studies were analyzed through bibliometric and thematic approaches. The review highlights the growing importance of widely adopted methods such as AHP, TOPSIS, VIKOR, DEMATEL, ELECTRE, PROMETHEE, MOORA, COPRAS, MARCOS, and CoCoSo across diverse domains, including engineering, healthcare, robotics, renewable energy, transportation, and sustainability. Furthermore, hybrid frameworks integrating fuzzy logic, grey theory, machine learning, deep learning, and artificial intelligence have significantly enhanced decision-support capabilities under uncertainty. The study also identifies key research gaps and future directions related to computational complexity, scalability, interpretability, and real-time intelligent decision-making systems.
Copyright (c) 2026 Sushil Kumar Sahoo, Bibhuti Bhusan Choudhury, Prasant Ranjan Dhal, Sudhakar Majhi, Ipsita Dhar, Supriya Sahu (Author) https://creativecommons.org/licenses/by/4.0
An Interval-Valued T-Spherical Fuzzy SWARA Approach with Sugeno–Weber Operators for Artificial Intelligence Selection
The T-spherical fuzzy set (T-SFS) has emerged as a powerful and flexible framework for modelling uncertainty and ambiguity in decision-making processes. In this study, we examine the integration of Sugeno–Weber (SW) t-norms within an interval-valued T-spherical fuzzy (IVT-SF) environment. Based on this framework, a novel family of aggregation operators is developed, including the interval-valued T-spherical fuzzy Sugeno–Weber power averaging (IVT-SFSWPA), power geometric (IVT-SFSWPG), power-weighted averaging (IVT-SFSWPWA), and power-weighted geometric (IVT-SFSWPWG) operators. The proposed operators are analyzed in terms of their fundamental properties and special cases, demonstrating their flexibility and effectiveness in handling complex decision-making problems. In addition, a new multi-attribute decision-making (MADM) method is developed within the IVT-SF environment. Furthermore, a comparative analysis with existing approaches is conducted to highlight the superiority, robustness, and practicality of the proposed aggregation operators. The results indicate that the proposed method provides more consistent and reliable decision-making outcomes. This study advances fuzzy decision-making methodologies and offers a promising approach to addressing real-world problems in dynamic, complex environments.
Copyright (c) 2026 Rashid Kakis, Szabolcs Fischer, Dmytro Kurhan (Author) https://creativecommons.org/licenses/by/4.0
Selection of Optimal Stock for Business Investment Using Pythagorean Fuzzy Aczel-Alsina CoCoSo-Method
Selecting an optimal stock for business investment is a complex multi-attribute decision-making (MADM) problem often characterized by vague, inconsistent, and uncertain information. This article introduces a novel decision framework to address this challenge under Pythagorean fuzzy environments, offering greater flexibility than intuitionistic fuzzy sets. To aggregate uncertain evaluation information, Pythagorean fuzzy Aczel-Alsina weighted aggregation operators are developed, leveraging the parameterized family of Aczel-Alsina t-norms and t-conorms to capture the full range of decision-makers’ risk attitudes. A modified score function is first proposed to effectively rank aggregated values. Subsequently, the combined compromise solution (CoCoSo) method is extended within the Pythagorean fuzzy Aczel-Alsina context to evaluate and rank investment alternatives under conditions of unknown attribute weights. The viability and procedural clarity of the proposed methodology are demonstrated through a numerical example involving stock selection in the financial market with ten alternatives and eight criteria. Finally, a comprehensive comparative analysis using different aggregation operators and score functions is conducted, confirming that the proposed method yields robust, consistent, and discriminative results, thereby offering a superior tool for strategic investment decision-making under complex uncertainty.
Copyright (c) 2026 Amal Kumar Adak, Stefan Jovcic, Darko Bozanic, Dragan Marinkovic (Author) https://creativecommons.org/licenses/by/4.0
An M-Polar Fuzzy Five-Way Decision-Making Framework with Modified Hamacher Aggregation for Carbon Emission Reduction Assessment
The paper presents a new Five-Way Decision Model (5WDM) that is founded on M-Polar Fuzzy Numbers (MPFNs) and a revised version of the Hamacher aggregation operator to improve decision-making in case of uncertainty in carbon emission reduction. The suggested framework appraises major mitigation options such as carbon capture, renewable energy, energy efficiency improvement, green transportation, and waste-to-energy conversion systematically through the use of probabilistic criteria to address uncertainty and interdependence between attributes. It generates a reformulated Hamacher operator that enhances the aggregation behavior by having a more adaptable representation of the truth, indeterminacy, and falsity elements in the MPFN setting and leads to a more stable and discriminative ranking result. The model also breaks down alternatives into five regions of decision-making, which are fully accepted, partially accepted, boundary, partially rejected, and fully rejected, offering a more efficient and understandable classification scheme than traditional methods. The superiority of the proposed method compared to the current fuzzy and neutrosophic frameworks is confirmed by the comparative and sensitivity analysis in the three aspects of robustness, consistency, and flexibility. The findings mention renewable energy as the most viable means of mitigating carbon emissions. On the whole, the analysis provides a mathematically sound and versatile decision model that can be generalised to three-way or binary models, to make informed policymaking in complicated environmental systems.
Copyright (c) 2026 Muhammad Imran Shahid, Muhammad Tahir (Author) https://creativecommons.org/licenses/by/4.0
Evaluating the Impact of Cold Email Outreach Channel Using a Fuzzy Decision-Making Framework
Cold emailing is the most popular technique for B2B lead generation, but it is hard to select the best option due to uncertainty, subjective judgment, and the dynamic behaviour of users. Conventional analytical models usually do not recognize qualitative aspects such as personalization, timing sensitivity, and user perception and hence provide inconsistent decision-making. To overcome this shortcoming, this paper presents a new decision-making model that incorporates circular q-rung orthopair fuzzy sets (Cq-RoFS) combined with the MAIRCA approach. The proposed method is useful in modelling uncertainty, hesitation, and linguistic ambiguity, as well as offering a deviation-based comparative analysis of options. A study is carried out in a SaaS setting where five cold email strategies are assessed using four benefit criteria. The judgments of the experts, in the form of linguistic expressions, are converted into Cq-RoFS values and processed within the suggested framework. The findings show that the hybrid semi-automated approach has the lowest degree of deviation, which means that it is the most efficient one, followed by AI-adaptive and bulk automated strategies. The sensitivity analysis of parameter q at different values also proves that the model is stable and robust, and no rank reversal has been noted. The research paper reveals the necessity of a balance between personalization and automation in cold email campaigns and shows that the proposed framework is superior in its ability to manage complex and uncertain decision-making conditions. The Cq-RoFS MAIRCA model enhances the evolution of fuzzy multi-criteria decision-making and provides a useful, dependable, and comprehensible instrument to streamline digital marketing initiatives.
Copyright (c) 2026 Raiha Imran, Khadija Basharat, Kifayat Ullah (Author) https://creativecommons.org/licenses/by/4.0
Benchmarking the Financial Performance of the Corporate Governance Index under Borsa Istanbul Companies Using SITDE and MABAC Framework
Financial performance is a fundamental indicator of how effectively, efficiently, and sustainably firms utilize their financial resources within a given period. Financial analysis techniques are employed to assess a firm's current financial position, evaluate past performance, and provide a basis for future strategic and financial planning. Financial analysis is a critical decision-support tool for internal managers, investors, creditors, and other stakeholders. This study aims to examine the financial performance of companies listed in the Corporate Governance Index under Borsa Istanbul for the period 2018–2023 by employing multi-criteria decision-making methods. The analysis utilizes the SITDE and MABAC techniques. Banks, financial institutions, insurance companies, and firms with incomplete financial data during the research period were excluded, resulting in a final sample of 28 companies. Financial data were obtained from the Public Disclosure Platform, and a total of 23 financial ratios were incorporated into the analysis. The findings indicate that total liabilities emerged as the most influential criterion in 2018, 2019, and 2021, highlighting the significant role of capital structure in firm performance. In 2020 and 2023, net profit/loss for the period was the dominant factor, suggesting that value creation capacity primarily shaped performance outcomes. Operating profit was the leading criterion in 2022, emphasizing the importance of operational efficiency and core business profitability. Firm-level results reveal that Iskenderun Demir ve Celik A.S. ranked highest in 2018 and 2020, Enka Insaat ve Sanayi A.S. in 2021, and Pegasus Hava Tasimaciligi A.S. in 2019, 2022, and 2023, with pandemic-related effects explaining Pegasus's relatively lower rankings in 2020–2021.
Copyright (c) 2026 Galip Cihan Yalçın, Hamide Özyürek, Ceren Dişçi (Author) https://creativecommons.org/licenses/by/4.0
Efficient Solution Strategy for Transportation Linear Fractional Programming under Fuzzy Conditions
Linear fractional programming (LFP) serves as an effective optimization framework in which the objective function is formulated as the ratio of two linear expressions. In numerous daily-life situations, the parameters involved in the objective function may not be defined precisely, leading to the incorporation of fuzzy representations. Transportation problems with fractional objectives are commonly described through system representations involving a limited number of lumps interconnected by curves. This study investigates a transportation model with a linear fractional objective where the parameters are characterized using fully piecewise quadratic fuzzy (PQF) numbers (PQFNs). To address the inherent uncertainty, a closeness-based interval approximation of these fuzzy numbers is introduced, allowing the original problem to be converted into an interval-valued LFP model. By applying the Charnes–Cooper transformation, this formulation is further reduced to an interval-valued linear transportation problem. This is then converted into a multi-objective optimization problem. Order relations on interval-valued outcomes are used to represent preferences of decision makers, considering lower and upper bounds, midpoints, and interval widths. An appropriate weighting scheme is then used to arrive at an ideal cooperation outcome. An example of the applicability and computational efficiency of the proposed approach is illustrated mathematically. Concluding the paper, the main findings and possible areas for further research are given.
Copyright (c) 2026 Sultan S. Alodhaibi, Moodi Abdulrahman Abdullah Al- Rajeh, Florentin Smarandache, Hamiden Khalifa (Author) https://creativecommons.org/licenses/by/4.0
IndetermSoft Sets: A Multi-Valued Framework for Modeling Indeterminacy in Decision Science
Soft sets are parameterized families of subsets of a universe, where each parameter selects elements relevant under a condition. This paper develops IndetermSoft sets, a class designed to treat indeterminacy explicitly through structured value slices. We define five variants: (i) single-valued, mapping each attribute value to one subset capturing undirected indeterminacy; (ii) double-valued, splitting into truth-leaning and falsity-leaning slices; (iii) triple-valued, adding a neutral slice; (iv) quadruple-valued, refining polarity by distinguishing strong and ordinary truth- and falsity-leanings; and (v) quintuple-valued, further introducing a central neutral band between the two strengths. These constructions yield a uniform framework for modeling ambiguous evidence, conflicts, and missing context.
Copyright (c) 2026 Takaaki Fujita, Ajoy Kanti Das, Suman Das (Author) https://creativecommons.org/licenses/by/4.0
Business-oriented Stock Market Decision Analysis Using Circular Complex Picture Fuzzy Sets and Advanced MCDM Based on the CRITIC–WASPAS Method
Multi-criteria evaluation and financial sustainability analysis in stock markets often involve uncertain and imprecise information, which requires advanced decision-making models. In this paper, we discuss the concepts of Circular Complex Picture Fuzzy Sets (CrC-PiFS) for handling uncertainty in financial assessments. The circular complex T-spherical fuzzy set is an extension of the complex picture fuzzy set, complex spherical fuzzy set, and complex T-spherical fuzzy set. We define improved algebraic operations for CrC-PiFS, including direct sum, direct product, and scalar multiplication, based on t-norms and t-conorms. The aim of this study is to enhance the representation of uncertainty in multi-criteria stock-market decision-making. To achieve this, we introduce circular complex picture fuzzy weighted/ordered weighted arithmetic mean and geometric mean aggregation operators under a new class of algebraic circular complex T-spherical fuzzy operational laws, and discuss their properties. Finally, we present a novel decision-making framework incorporating the CRITIC–WASPAS method and highlight its applicability to stock-market analysis, particularly in evaluating and prioritizing market stability factors, investment strategies, and financial sustainability indicators.
Copyright (c) 2026 Kaleem Ullah, Noor Rehman, Abbas Ali (Author) https://creativecommons.org/licenses/by/4.0