Journal of Contemporary Decision Science
type: channel
description: 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.
publisher: Association for Optimization, Risk and Business Intelligence in Transformation
language: en
publication name: Journal of Contemporary Decision Science
issn: 3104-3380
items: Spherical Fuzzy Aczel–Alsina Aggregation Operators with AHP Approach for Artificial Intelligence in Smart Agriculture
items: An Interval-Valued T-Spherical Fuzzy SWARA Approach with Sugeno–Weber Operators for Artificial Intelligence Selection
items: AHP-Based Aczel–Alsina Prioritization under T-Spherical Fuzzy Information for Artificial Intelligence in Smart Systems
items: Selection of Optimal Stock for Business Investment Using Pythagorean Fuzzy Aczel-Alsina CoCoSo-Method
items: An M-Polar Fuzzy Five-Way Decision-Making Framework with Modified Hamacher Aggregation for Carbon Emission Reduction Assessment
items: Criteria Weighting Methods in Multi-Criteria Decision Making: A Comprehensive Review of Subjective, Objective, and Hybrid Approaches
items: An Intelligent Industry 5.0 Logistics Decision System: Circular Supply Chain Management With Fermatean Neutrosophic Hypersoft Sets And Machine Learning
items: Evaluating the Impact of Cold Email Outreach Channel Using a Fuzzy Decision-Making Framework
items: Analysis of m-Polar CFR-WASPAS Model for Smart Parking in Taiwan: A Personalized Recommender System for Urban Drivers
items: Benchmarking the Financial Performance of the Corporate Governance Index under Borsa Istanbul Companies Using SITDE and MABAC Framework
items: Efficient Solution Strategy for Transportation Linear Fractional Programming under Fuzzy Conditions
items: Smart Technologies for Water Sewage Systems and Decision-Making with Circular Spherical Fuzzy Framework
items: An Artificial Intelligence-Based Framework for Market Optimization of the Pharmaceutical Industry
items: Transforming Decision-Making in Generation Z Education: A Systematic and Bibliometric Review of Artificial Intelligence
items: A Novel Interval-Valued Intuitionistic Fuzzy Distance Measure Incorporating Min/Max Interaction Terms
items: IndetermSoft Sets: A Multi-Valued Framework for Modeling Indeterminacy in Decision Science
items: Business-oriented Stock Market Decision Analysis Using Circular Complex Picture Fuzzy Sets and Advanced MCDM Based on the CRITIC–WASPAS Method
items: A Multi-Criteria-Decision Making Methodology to Prioritizing Telemedicine Expansion Opportunities
items: Ranking Higher Education Institutions Using Entropy–VIKOR with Generalized Pentagonal Intuitionistic Fuzzy Numbers
items: Three-Mode Upside-Down Logic within Plithogenic Double-valued and Triple-valued Neutrosophic Sets
items: A Project Portfolio Selection Model Considering Mental Accounts and Uncertain Synergies

Spherical Fuzzy Aczel–Alsina Aggregation Operators with AHP Approach for Artificial Intelligence in Smart Agriculture
type: item
description: This paper investigates multi-attribute decision-making (MADM) problems within the framework of spherical fuzzy (SF) sets for the evaluation and selection of artificial intelligence (AI) technologies in smart agriculture. The increasing complexity and uncertainty associated with agricultural decision-making, driven by environmental variability, technological advancements, and sustainability requirements, necessitate robust and flexible decision-support methodologies. To address this challenge, the Aczel–Alsina (AA) t-norm and t-conorm are incorporated into the spherical fuzzy environment to extend existing aggregation mechanisms. Accordingly, two novel aggregation operators, namely the Spherical Fuzzy Aczel–Alsina Geometric (SFAAG) operator and the Spherical Fuzzy Aczel–Alsina Averaging (SFAAA) operator, are proposed, and their fundamental mathematical properties, including idempotency, monotonicity, boundedness, and stability, are thoroughly investigated. Furthermore, a MADM framework based on the proposed operators is developed to support the selection of AI-based smart agriculture technologies under spherical fuzzy information. To demonstrate the applicability of the proposed approach, a case study involving the evaluation of five AI technologies, namely smart irrigation systems, crop disease identification systems, agricultural drone surveillance, smart harvesting robots, and AI-based weather forecasting systems, is conducted using productivity improvement, cost reduction, environmental sustainability, and technical reliability as evaluation criteria. Comparative analyses with existing aggregation-based decision-making methods confirm the effectiveness, robustness, and reliability of the proposed framework in handling complex and uncertain decision-making environments. The results indicate that the proposed approach provides an efficient and reliable decision-support tool for the assessment and selection of AI technologies in smart agriculture.
subject: Articles
  topic: section
subject: Spherical Fuzzy Sets
  topic: keywords
subject: Aczel-Alsina
  topic: keywords
subject: MAGDM
  topic: keywords
subject: Smart Agriculture
  topic: keywords
subject: Environmental Sustainability
  topic: keywords
creator: Tahira Karamat
creator: Ilija Stojanović
creator: Arianit Peci
creator: Adis Puška
rights: Copyright (c) 2026 Tahira Karamat, Ilija Stojanović, Arianit Peci, Adis Puška (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-06-05
publication date: 2026-06-05
starting page: 349
ending page: 367

An Interval-Valued T-Spherical Fuzzy SWARA Approach with Sugeno–Weber Operators for Artificial Intelligence Selection
type: item
description: 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.
subject: Articles
  topic: section
subject: T-spherical fuzzy set
  topic: keywords
subject: Sugeno–Weber
  topic: keywords
subject: T-SFS
  topic: keywords
subject: Multi-Attribute Decision-Making
  topic: keywords
subject: MADM
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
creator: Rashid Kakis
creator: Szabolcs Fischer
creator: Dmytro Kurhan
rights: Copyright (c) 2026 Rashid Kakis, Szabolcs Fischer, Dmytro Kurhan (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-06-30
publication date: 2026-06-30
starting page: 323
ending page: 348

AHP-Based Aczel–Alsina Prioritization under T-Spherical Fuzzy Information for Artificial Intelligence in Smart Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: T-spherical fuzzy set
  topic: keywords
subject: Fuzzy Stes
  topic: keywords
subject: Aczel-Alsina T-norm
  topic: keywords
subject: Prioritized Operators
  topic: keywords
subject: MADM
  topic: keywords
creator: Mehwish Sarfraz
creator: Duško Tešić
creator: Gülay Demir
rights: Copyright (c) 2026 Mehwish Sarfraz, Duško Tešić, Gülay Demir (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-05-24
publication date: 2026-05-24
starting page: 305
ending page: 322

Selection of Optimal Stock for Business Investment Using Pythagorean Fuzzy Aczel-Alsina CoCoSo-Method
type: item
description: 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.
subject: Articles
  topic: section
subject: Pythagorean Fuzzy Sets
  topic: keywords
subject: Aczel Alsina aggregation operator
  topic: keywords
subject: Unknown weight of Criteria
  topic: keywords
subject: CoCoSo Method
  topic: keywords
subject: Stock investment
  topic: keywords
subject: MCDM
  topic: keywords
creator: Amal Kumar Adak
creator: Stefan Jovcic
creator: Darko Bozanic
creator: Dragan Marinkovic
rights: Copyright (c) 2026 Amal Kumar Adak, Stefan Jovcic, Darko Bozanic, Dragan Marinkovic (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-05-22
publication date: 2026-05-22
starting page: 287
ending page: 304

An M-Polar Fuzzy Five-Way Decision-Making Framework with Modified Hamacher Aggregation for Carbon Emission Reduction Assessment
type: item
description: 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.
subject: Articles
  topic: section
subject: M-Polar Fuzzy Numbers
  topic: keywords
subject: Modified Hamacher Operator
  topic: keywords
subject: Five-Way Decision Model
  topic: keywords
subject: Carbon Emission Reduction
  topic: keywords
subject: Uncertainty Management
  topic: keywords
subject: Multi-Criteria Decision-Making
  topic: keywords
creator: Muhammad Imran Shahid
creator: Muhammad Tahir
rights: Copyright (c) 2026 Muhammad Imran Shahid, Muhammad Tahir (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-05-22
publication date: 2026-05-22
starting page: 1
ending page: 31

Criteria Weighting Methods in Multi-Criteria Decision Making: A Comprehensive Review of Subjective, Objective, and Hybrid Approaches
type: item
description: 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.
subject: Articles
  topic: section
subject: Multi-Criteria Decision Making
  topic: keywords
subject: MCDM
  topic: keywords
subject: Subjective and Objective Weighting
  topic: keywords
subject: Hybrid Weighting Approaches
  topic: keywords
subject: Decision Support Systems
  topic: keywords
subject: Criteria Weighting Methods
  topic: keywords
creator: Arkyadeep Sarkar
creator: Shankha Shubhra Goswami
creator: Dhiren Kumar Behera
creator: Darko Bozanic
rights: Copyright (c) 2026 Arkyadeep Sarkar, Shankha Shubhra Goswami, Dhiren Kumar Behera, Darko Bozanic (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-06-01
publication date: 2026-06-01
starting page: 1
ending page: 34

An Intelligent Industry 5.0 Logistics Decision System: Circular Supply Chain Management With Fermatean Neutrosophic Hypersoft Sets And Machine Learning
type: item
description: 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.
subject: Articles
  topic: section
subject: Fermatean Neutrosophic Hypersoft Sets
  topic: keywords
subject: Circular Supply Chain
  topic: keywords
subject: Industry 5.0
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Logistics Decision System
  topic: keywords
subject: Deep Reinforcement Learning
  topic: keywords
creator: Muhammad Tahir
creator: Muhammad Imran Shahid
rights: Copyright (c) 2026 Muhammad Tahir, Muhammad Imran Shahid (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-05-08
publication date: 2026-05-08
starting page: 260
ending page: 286

Evaluating the Impact of Cold Email Outreach Channel Using a Fuzzy Decision-Making Framework
type: item
description: 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.
subject: Articles
  topic: section
subject: Cold email outreach
  topic: keywords
subject: Digital Marketing Optimization
  topic: keywords
subject: Circular q-rung Orthopair fuzzy sets
  topic: keywords
subject: MAIRCA model
  topic: keywords
subject: MCDM
  topic: keywords
subject: Decision making framework
  topic: keywords
creator: Raiha Imran
creator: Khadija Basharat
creator: Kifayat Ullah
rights: Copyright (c) 2026 Raiha Imran, Khadija Basharat, Kifayat Ullah (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-05-07
publication date: 2026-05-07
starting page: 243
ending page: 259

Analysis of m-Polar CFR-WASPAS Model for Smart Parking in Taiwan: A Personalized Recommender System for Urban Drivers
type: item
description: 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
subject: Articles
  topic: section
subject: Decision-making strategies
  topic: keywords
subject: M-polar complex fuzzy rough sets
  topic: keywords
subject: Personalized recommender systems
  topic: keywords
subject: Smart parking
  topic: keywords
subject: Urban drivers
  topic: keywords
creator: Tung Chun Yang
creator: Zeeshan Ali
rights: Copyright (c) 2026 Tung Chun Yang, Zeeshan Ali (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-05-01
publication date: 2026-05-01
starting page: 216
ending page: 242

Benchmarking the Financial Performance of the Corporate Governance Index under Borsa Istanbul Companies Using SITDE and MABAC Framework
type: item
description: 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.
subject: Articles
  topic: section
subject: Financial performance
  topic: keywords
subject: Corporate Governance Index
  topic: keywords
subject: Borsa Istanbul
  topic: keywords
subject: SITDE
  topic: keywords
subject: MABAC
  topic: keywords
subject: MCDM
  topic: keywords
creator: Galip Cihan Yalçın
creator: Hamide Özyürek
creator: Ceren Dişçi
rights: Copyright (c) 2026 Galip Cihan Yalçın, Hamide Özyürek, Ceren Dişçi (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-04-26
publication date: 2026-04-26
starting page: 169
ending page: 193

Efficient Solution Strategy for Transportation Linear Fractional Programming under Fuzzy Conditions
type: item
description: 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.
subject: Articles
  topic: section
subject: Piecewise quadratic fuzzy numbers
  topic: keywords
subject: Interval-valued modeling
  topic: keywords
subject: Linear fractional transportation model
  topic: keywords
subject: Transformation techniques
  topic: keywords
subject: Multi-objective optimization
  topic: keywords
subject: LINGO 20.0
  topic: keywords
subject: Efficient solutions
  topic: keywords
subject: Optimal compromise strategy
  topic: keywords
creator: Sultan S. Alodhaibi
creator: Moodi Abdulrahman Abdullah Al- Rajeh
creator: Florentin Smarandache
creator: Hamiden Khalifa
rights: Copyright (c) 2026 Sultan S. Alodhaibi, Moodi Abdulrahman Abdullah Al- Rajeh, Florentin Smarandache, Hamiden Khalifa (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-04-26
publication date: 2026-04-26
starting page: 155
ending page: 168

Smart Technologies for Water Sewage Systems and Decision-Making with Circular Spherical Fuzzy Framework
type: item
description: Smart technologies play an increasingly attractive and transformative role in modern water and sewage systems, particularly under uncertain and dynamic environments. By integrating sensors, Internet of Things (IoT) devices, and artificial intelligence, these technologies enable real-time monitoring and adaptive control of complex water networks. To handle uncertainties such as fluctuating demand, climate variability, and data incompleteness, we develop an improved decision-making (DM) model using the circular spherical fuzzy set (Cr-SFS). The Cr-SFS is a flexible structure for managing uncertainty in human opinions. This article presents a new class of aggregation operators, namely the circular spherical fuzzy Dombi weighted averaging (Cr-SFDWA), circular spherical fuzzy Dombi weighted geometric (Cr-SFDWG), circular spherical fuzzy Dombi ordered weighted averaging (Cr-SFDOWA), and circular spherical fuzzy Dombi ordered weighted geometric (Cr-SFDOWG) operators. The realistic qualities and exceptional cases of these operators are also clarified through appropriate properties. An intelligent decision-making methodology for the MADM problem is applied to resolve real-life applications. A numerical example is presented to investigate an appropriate artificial intelligence-based water sewage system using decision analysis models and mathematical approaches. Moreover, a comprehensive comparison method is presented to illustrate the effectiveness and relevance of the proposed aggregation relative to existing approaches. The study is concluded with a summary of the main findings and the potential contributions of advanced decision-making techniques.
subject: Articles
  topic: section
subject: Circular spherical fuzzy set
  topic: keywords
subject: Dombi aggregation operators
  topic: keywords
subject: Water sewage system
  topic: keywords
subject: Artificial intelligence and decision support system
  topic: keywords
creator: Abrar Hussain
creator: Muhammad Ahmad
creator: Kifayat Ullah
creator: Zeeshan Ali
creator: Oumaima Saidani
rights: Copyright (c) 2026 Abrar Hussain, Muhammad Ahmad, Kifayat Ullah, Zeeshan Ali, Oumaima Saidani (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-04-29
publication date: 2026-04-29
starting page: 194
ending page: 215

An Artificial Intelligence-Based Framework for Market Optimization of the Pharmaceutical Industry
type: item
description: 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.
subject: Articles
  topic: section
subject: Pharmaceutical pricing
  topic: keywords
subject: Dynamic pricing
  topic: keywords
subject: Deep reinforcement learning
  topic: keywords
subject: Ethical governance
  topic: keywords
subject: Market optimization
  topic: keywords
creator: Divanshu Mittal
rights: Copyright (c) 2026 Divanshu Mittal (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-04-13
publication date: 2026-04-13
starting page: 131
ending page: 154

Transforming Decision-Making in Generation Z Education: A Systematic and Bibliometric Review of Artificial Intelligence
type: item
description: The fast adoption of artificial intelligence (AI) in education has reshaped the learning space, particularly in reference to the generation Z learners who are very conversant with the digital technologies. This paper discusses how AI influences the decision-making process in the field of education among the Generation Z through a systematic review methodology. The Dimensions.ai database was searched thoroughly, and the results scanned include the years between 2012 and 2026. The primary search based on keywords was done and 2101 articles were located, and then filtered through inclusion and exclusion criteria. The results indicate that AI-based tools, including adaptive learning systems, intelligent tutoring systems, and decision support systems, enhance individualized learning and promote decision-making skills of the students. Nevertheless, the research also reveals such issues as over-dependence on AI, the decreased ability to independently think, and ethical aspects such as data privacy and bias in algorithms. The research also points to critical gaps in the field of study and proposes the future directions, such as the combination of AI with structured decision-making models to enhance educational performance.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Generation Z
  topic: keywords
subject: Decision Support Systems
  topic: keywords
subject: Adaptive Learning
  topic: keywords
subject: Education
  topic: keywords
creator: Sushil Kumar Sahoo
creator: Supriya Sahu
creator: Bibhuti Bhusan Choudhury
creator: Prasant Ranjan Dhal
creator: Ipsita Dhar
rights: Copyright (c) 2026 Sushil Kumar Sahoo, Supriya Sahu, Bibhuti Bhusan Choudhury, Prasant Ranjan Dhal, Ipsita Dhar (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-24
publication date: 2026-03-24
starting page: 114
ending page: 130

A Novel Interval-Valued Intuitionistic Fuzzy Distance Measure Incorporating Min/Max Interaction Terms
type: item
description: Interval-valued intuitionistic fuzzy sets (IVIFSs) are widely employed in decision-making and pattern recognition, where distance measures serve as fundamental tools. However, many existing distance measures for IVIFSs have two limitations: one is the low discriminant ability, which usually produces the same value for different pairs; the second is the violation of the axiom of regularity, and the distance value is not within [0,1]. To overcome these shortcomings, this paper proposes a new distance measure, which integrates a non-linear transformation of endpoint differences with min/max interaction terms. In addition, it is proved that the proposed measure satisfies all four axioms of distance. Finally, comparative experiments show the effectiveness and superiority of the proposed measure. The results establish the proposed measure as a more discriminative and axiomatically sound tool for IVIFS-based analysis.
subject: Articles
  topic: section
subject: Interval-valued intuitionistic fuzzy set
  topic: keywords
subject: Distance measure
  topic: keywords
subject: Decision-making
  topic: keywords
creator: Jiulin Jin
creator: Xianlong Yang
creator: Haixin Du
creator: Dragan Pamucar
rights: Copyright (c) 2026 Dragan Pamucar, Jiulin Jin, Xianlong Yang, Haixin Du (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-22
publication date: 2026-03-22
starting page: 84
ending page: 96

IndetermSoft Sets: A Multi-Valued Framework for Modeling Indeterminacy in Decision Science
type: item
description: 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.
subject: Articles
  topic: section
subject: Soft Set
  topic: keywords
subject: IndetermSoft Set
  topic: keywords
subject: Double-valued IndetermSoft Set
  topic: keywords
subject: Triple-valued IndetermSoft Set
  topic: keywords
subject: Quadruple-valued IndetermSoft Set
  topic: keywords
subject: Quintuple-valued IndetermSoft Set
  topic: keywords
creator: Takaaki Fujita
creator: Ajoy Kanti Das
creator: Suman Das
rights: Copyright (c) 2026 Takaaki Fujita, Ajoy Kanti Das, Suman Das (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-22
publication date: 2026-03-22
starting page: 97
ending page: 113

Business-oriented Stock Market Decision Analysis Using Circular Complex Picture Fuzzy Sets and Advanced MCDM Based on the CRITIC–WASPAS Method
type: item
description: 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.
subject: Articles
  topic: section
subject: Circular complex picture fuzzy set
  topic: keywords
subject: Fuzzy sets
  topic: keywords
subject: Complex picture fuzzy set
  topic: keywords
subject: CRITIC
  topic: keywords
subject: WASPAS
  topic: keywords
subject: MCDM
  topic: keywords
subject: Decision making
  topic: keywords
creator: Kaleem Ullah
creator: Noor Rehman
creator: Abbas Ali
rights: Copyright (c) 2026 Kaleem Ullah, Noor Rehman, Abbas Ali (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-04
publication date: 2026-01-04
starting page: 1
ending page: 54

A Multi-Criteria-Decision Making Methodology to Prioritizing Telemedicine Expansion Opportunities
type: item
description: This article explores the current opportunities related to telemedicine expansion in the Sub-Saharan Africa (SSA) region. The study adopted a decision-making approach which comprises interval-valued spherical fuzzy (IVSF) Stepwise Weight Assessment Ratio Analysis (SWARA) to investigate this expansion within the region. Six opportunities are identified based on expert opinions and a literature review. The findings indicate that the extension of broadband and mobile infrastructure and the development of national policy reforms and regulations are the most important opportunities for the expansion of telemedicine. The study implies that strategic priority should be given to investing in broadband and mobile infrastructure while simultaneously strengthening telemedicine policies and regulatory frameworks to enable scalable, compliant, and sustainable telemedicine implementation across the region.
subject: Articles
  topic: section
subject: Telemedicine
  topic: keywords
subject: Sub-Saharan Africa
  topic: keywords
subject: Opportunities
  topic: keywords
subject: MCDM
  topic: keywords
subject: Interval-valued spherical fuzzy sets
  topic: keywords
subject: IVSF
  topic: keywords
subject: SWARA
  topic: keywords
creator: Samirath Bakary
creator: Mouhamed Bayane Bouraima
creator: Ibrahim Badi
rights: Copyright (c) 2026 Samirath Bakary, Mouhamed Bayane Bouraima, Ibrahim Badi (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-04
publication date: 2026-01-04
starting page: 55
ending page: 63

Ranking Higher Education Institutions Using Entropy–VIKOR with Generalized Pentagonal Intuitionistic Fuzzy Numbers
type: item
description: 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.
subject: Articles
  topic: section
subject: Higher educational institution ranking
  topic: keywords
subject: Generalized pentagonal intuitionistic fuzzy numbers
  topic: keywords
subject: Fuzzy sets
  topic: keywords
subject: MCDM
  topic: keywords
subject: Decision making
  topic: keywords
subject: Entropy
  topic: keywords
subject: VIKOR
  topic: keywords
creator: Tripti Basuri
creator: Kamal Hossain Gazi
creator: Srabani Guria Das
creator: Sankar Prasad Mondal
rights: Copyright (c) 2026 Tripti Basuri, Kamal Hossain Gazi, Srabani Guria Das, Sankar Prasad Mondal (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-04
publication date: 2026-01-04
starting page: 64
ending page: 83

Three-Mode Upside-Down Logic within Plithogenic Double-valued and Triple-valued Neutrosophic Sets
type: item
description: In the real world, reversal phenomena are common—for example, assertions once judged false may later be recognized as true. Upside–Down Logic formalizes such reversals by contextually transforming truth and falsity (and their attendant uncertainties), thereby capturing ambiguity and temporal change in reasoning. A Plithogenic Set represents elements via attribute–driven membership and contradiction functions, extending fuzzy, intuitionistic, and neutrosophic paradigms. A Plithogenic Neutrosophic Set further models truth, indeterminacy, and falsity under explicit contradictions, enriching neutrosophic semantics with contextual sensitivity. Double–Valued Neutrosophic Logic assigns truth, falsity, and two indeterminacy components (one leaning toward truth, one toward falsity) to each proposition, whereas Triple–Valued Neutrosophic Logic adds a neutral indeterminacy component. Despite recent interest, extended forms of the Plithogenic Neutrosophic Set remain underexplored in terms of properties and concrete use–cases. To bridge this gap, this paper introduces and formalizes Plithogenic Double–Valued and Plithogenic Triple–Valued Neutrosophic Sets, establishes their fundamental properties and reduction relations, and presents application scenarios demonstrating the practical deployment of Upside–Down Logic.
subject: Articles
  topic: section
subject: Upside-down-logic
  topic: keywords
subject: Plithogenic Set
  topic: keywords
subject: Three-Mode Upside-Down Logic
  topic: keywords
subject: Double–Valued Neutrosophic Logic
  topic: keywords
subject: Triple–Valued Neutrosophic Logic
  topic: keywords
creator: Takaaki Fujita
creator: Ajoy Kanti Das
creator: Sankar Prasad Mondal
creator: Arif Mehmood
creator: Suman Das
creator: Volkan Duran
creator: Arkan A. Ghaib
rights: Copyright (c) 2025 Takaaki Fujita, Ajoy Kanti Das, Sankar Prasad Mondal, Arif Mehmood, Suman Das, Volkan Duran, Arkan A. Ghaib (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-22
publication date: 2025-12-22
starting page: 19
ending page: 45

A Project Portfolio Selection Model Considering Mental Accounts and Uncertain Synergies
type: item
description: Project portfolio selection serves as a critical step for enterprise strategy. However, the psychological behaviors of decision-makers and uncertain synergies hinder project portfolio selection. Besides, few studies explore how to select projects considering the impact of these two factors. To address these gaps, mental accounting theory and uncertainty theory are applied in this study to build a project portfolio selection model. First, the paper describes a pyramidal structure of portfolio selection for decision-makers based on mental accounts to conduct project selection. Second, based on the above structure, a project portfolio selection model considering uncertain synergies is constructed. Finally, an improved genetic algorithm is used to solve the model. An illustrative example is used to validate the proposed model. The analysis results show that: 1) the proposed model is closer to the actual decision-making environment; 2) the proposed model can improve the efficient use of resources; and 3) the proposed model can provide a smaller decision-making basis in an uncertain environment.
subject: Articles
  topic: section
subject: Portfolio selection
  topic: keywords
subject: Mental accounting theory
  topic: keywords
subject: Uncertainty theory
  topic: keywords
subject: Genetic algorithm
  topic: keywords
creator: Tizhuang Zhang
creator: Qiming Liu
rights: Copyright (c) 2025 Tizhuang Zhang, Qiming Liu (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-17
publication date: 2025-12-17
starting page: 1
ending page: 18