Argumentation Based Systems Journal
type: channel
description: Argumentation-Based Systems Journal is an international, peer-reviewed journal dedicated to advancing the theory, methodologies, and applications of computational argumentation in artificial intelligence. The journal focuses on how argumentation models support robust knowledge representation, conflict resolution, and explainable reasoning in intelligent systems. It provides a forum for foundational research as well as applied studies demonstrating how argumentation enhances transparency, trust, and decision quality across diverse domains.
publisher: Nexora Academic Press
language: en
publication name: Argumentation Based Systems Journal
issn: 3120-1024
copyright: ABSJ is published Open Access under a Creative Commons CC-BY 4.0 license. Authors retain full copyright, with the first publication right granted to the journal.
items: An Integrated Multi-Criteria Decision-Making Framework for Athlete Selection in Sport Management: The METE Weighting and SEDAT Ranking Methods
items: Analyzing the Drivers of Fashion Supply Chain Sustainability During Pandemics (Case Study: COVID-19)
items: Evaluation of the Utility of Value Creation in the Agricultural Product Supply Chain Empowered by Digital Technology
items: A Shapley Value-Based Argumentation Framework Integrating CRITIC and MARCOS for Sustainable Supplier Evaluation: Evidence from a Global Semiconductor Supply Chain
items: A Fuzzy SIWEC Methodology for Evaluating the Challenges of Cryptocurrencies in Developing Countries
items: Implementation of a Fuzzy Logic Controller for Long-Term Energy Management in a Hybrid AC/DC Microgrid
items: Applying the CRADIS Method with Probabilistic Uncertain Linguistic T-Spherical Fuzzy Sets for Circular Supplier Selection
items: Reasoning About the Non-Existent in Large Language Models: Benchmark Success, Negation Failure, and Exploratory Creativity
items: Multiple Attribute Decision Method Based on Neutrosophic Z-Number Frank Combined Compromise Solution Approach
items: A Generative AI–Based Decision-Support Framework for Early-Stage Architectural Design: Evidence from Sustainable Tiny Houses
items: Dynamic Intuitionistic Fuzzy Soft Set-Based WASPAS Model for Multi-Criteria Decision Making in Renewable Energy Selection
items: A Hybrid Deep Learning-Driven Stochastic MCDM Framework for Uncertainty-Aware Renewable Energy Resource Selection
items: Regional Prioritization Model for Energy Storage Investments: A Strategic Approach to Public Policies in the Case of Türkiye
items: Probabilistic Uncertain Linguistic T-Spherical Fuzzy Aggregation Operators-based Multi-Criteria Decision-Making Method and Its Application with Unknown Weight Information
items: Decision-Oriented Risk Assessment in Micromobility Using AHP
items: Bayesian Hierarchical Modeling and Clustering for Malignant Cancer Diagnosis
items: A study on the location selection of Cold Chain Logistics Distribution Centre Based on Entropy-TOPSIS Model: The Case of a City in Anhui Province
items: Dynamic Financial Stress Indicators Using Machine Learning: An Adaptive Composite Framework for Early Warning and Systemic Risk Assessment
items: Application challenges of modern education technology in China's higher education teaching management: A fuzzy MARCOS method
items: Fuzzy LBWA Framework for Evaluate the Criteria Affecting Ammunition Consumption
items: A Fuzzy Comparative Study in Logistics Risk Assessment

An Integrated Multi-Criteria Decision-Making Framework for Athlete Selection in Sport Management: The METE Weighting and SEDAT Ranking Methods
type: item
description: Athlete selection—talent identification, squad construction, and boat formation—is among the most consequential recurring decisions facing sport managers and coaches, yet in practice it frequently rests on ad-hoc criterion weighting and single-number rankings whose assumptions are never made explicit. Multi-criteria decision-making (MCDM) offers a principled alternative, but widely used techniques carry structural weaknesses that can distort or reverse the orderings they produce. This study develops and validates an integrated MCDM framework composed of two complementary methods. METE is a loss-framed subjective weighting method that embeds criterion interactions through a relational (DEMATEL-type) structure and guarantees monotonicity between expert judgement and the resulting weights. SEDAT is a ranking method that represents alternatives as a dominance network and fuses thresholded outranking, net flow, and a damped eigenvector (prestige) centrality, thereby avoiding the degeneration and rank-reversal problems of additive aggregations. The formal properties of each method are stated, the core axioms are proven, and reliability and sensitivity analysis are treated as built-in stages of the procedure rather than optional add-ons. The framework is demonstrated on the selection of a rowing crew using a fully auditable single-sheet workbook. A 5,000-iteration Monte Carlo simulation, a Markov-chain analysis of the dominance network, internal-consistency and inter-rater statistics (Cronbach's α, Kendall's W, Fleiss' κ, ICC), sensitivity and cross-method comparisons, and a rank-reversal test jointly show that the results are defensible, reproducible, and stable. Implications for evidence-based, accountable decision-making are discussed for both sport management and sport marketing, where transparent and contestable selection procedures carry concrete governance and commercial value.
subject: Articles
  topic: section
subject: Rowing
  topic: keywords
subject: Athlete Selection
  topic: keywords
subject: Criterion Weighting
  topic: keywords
subject: Sports Management
  topic: keywords
subject: Decision Support
  topic: keywords
subject: Superiority Methods
  topic: keywords
subject: Sports Marketing
  topic: keywords
creator: Omer Faruk Gorcun
creator: Volkan Kapçak
creator: Mustafa Subaşı
rights: Copyright (c) 2026 Omer Faruk Gorcun, Volkan Kapçak, Mustafa Subaşı (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-07-27
publication date: 2026-07-27
starting page: 359
ending page: 378
doi: 10.59543/x3cqwy85

Analyzing the Drivers of Fashion Supply Chain Sustainability During Pandemics (Case Study: COVID-19)
type: item
description: This research aims to identify and analyze the drivers of sustainability in the fashion supply chain during periods of disruption. The importance of this study lies in providing an integrated model to understand the dynamics governing sustainability under such conditions. This research employs a three-stage mixed-method approach. First, the fuzzy Delphi method was used to screen and finalize key drivers from an initial list extracted from the literature. Next, interpretive structural modeling was applied to develop a six-level hierarchical model illustrating the causal relationships among these drivers. The results indicate that drivers such as “top management commitment” and the “existence of explicit policy” occupy the most fundamental level and serve as prerequisites for other factors. Finally, MICMAC analysis confirmed that the system is highly interconnected and that the identified fundamental drivers exert the greatest influence on the entire system.
subject: Articles
  topic: section
subject: Supply Chain Sustainability
  topic: keywords
subject: Strategic Drivers
  topic: keywords
subject: Fashion Industry
  topic: keywords
subject: COVID-19, Interpretive Structural Modeling
  topic: keywords
subject: MICMAC Analysis
  topic: keywords
creator: Amirhossein Mohebbi
creator: Aliakbar Hasani
rights: Copyright (c) 2026 Amirhossein Mohebbi, Aliakbar Hasani (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-07-27
publication date: 2026-07-27
starting page: 379
ending page: 403
doi: 10.59543/zh06r582

Evaluation of the Utility of Value Creation in the Agricultural Product Supply Chain Empowered by Digital Technology
type: item
description: In the context of rural revitalization and agricultural modernization development, the digital transformation of the agricultural product supply chain is an important means to increase the value of agricultural products. However, due to the diverse ways of digital technology empowerment and the significant differences among participants, the degree of technology adaptation is relatively low. Based on the perspective of digital technology empowering the value creation of agricultural product supply chains, this paper explores 20 evaluation indicators and 10 types of mainstream digital technologies as alternative options through SWOT analysis model and literature retrieval, obtains basic data with expert scoring and information fusion, and determines index weights using the CRITIC method Finally, the CoCoSo model is used for utility ranking and verification. The study found that satellite remote sensing technology was the most effective in enabling utility, while the good digital platform ecosystem and convenient circulation links were the main influencing factors, and the uneven infrastructure and talent shortage were the main constraints. The results can provide guidance for agricultural producers to choose appropriate technical means and for government departments to introduce targeted policies, promoting more refined and green development of supply chains.
subject: Articles
  topic: section
subject: Digital technology;
  topic: keywords
subject: agricultural product supply chain;
  topic: keywords
subject: value creation;
  topic: keywords
subject: SWTO analysis model
  topic: keywords
creator: Yuting Sun
creator: Cai Pan
rights: Copyright (c) 2026 Yuting Sun, Cai Pan (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-05-12
publication date: 2026-05-12
starting page: 324
ending page: 347
doi: 10.59543/hn9c6x29

A Shapley Value-Based Argumentation Framework Integrating CRITIC and MARCOS for Sustainable Supplier Evaluation: Evidence from a Global Semiconductor Supply Chain
type: item
description: Supplier evaluation in technology supply chains requires transparent, defensible multi-criteria decision-making (MCDM) methods that capture complex interactions among financial, environmental, and governance criteria. This study proposes a novel framework integrating Shapley values from cooperative game theory with the MARCOS method, augmented by an argumentation-based interpretation layer grounded in Shapley interaction indices. Unlike classical CRITIC weighting, the Shapley value considers the marginal contribution of each criterion across all 128 possible coalitions, producing weights that reflect each criterion’s true cooperative importance. The Shapley interaction index quantifies synergy or redundancy between every pair of criteria, providing a natural mapping onto argumentation support and attack relations. Applied to 52 first-tier suppliers in a leading semiconductor corporation’s global supply chain across seven evaluation criteria, the framework reveals that Shapley weights are substantially more balanced than classical CRITIC weights. The interaction analysis uncovers that cross-domain criterion pairs exhibit stronger synergies than within-domain pairs. Microsoft, Intel, BMW, SAP, and HP emerge as the top five suppliers, and sensitivity analysis confirms exceptional stability in rankings (mean Spearman ρ = 0.9950). An argumentation-based quadrant classification reveals that 84.6% of suppliers cluster along the performance–ESG diagonal.
subject: Articles
  topic: section
subject: Shapley value
  topic: keywords
subject: cooperative game theory
  topic: keywords
subject: interaction index
  topic: keywords
subject: MARCOS
  topic: keywords
subject: argumentation
  topic: keywords
subject: technology supply chain
  topic: keywords
subject: ESG
  topic: keywords
subject: supplier evaluation
  topic: keywords
creator: Huai-Wei Lo
creator: Sheng-Wei Lin
rights: Copyright (c) 2026 Huai-Wei Lo, Sheng-Wei Lin (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-05-01
publication date: 2026-05-01
starting page: 304
ending page: 323
doi: 10.59543/cg894606

A Fuzzy SIWEC Methodology for Evaluating the Challenges of Cryptocurrencies in Developing Countries
type: item
description: This study applies a fuzzy simple weight calculation (F-SIWEC) method to systematically assess the challenges of cryptocurrencies in Africa. Data was collected from four domain experts who evaluated six challenges, and the adopted method was then applied to determine the relative importance of each challenge. The findings indicate that market volatility and regulatory uncertainty are the top two critical challenges to cryptocurrencies. Meanwhile, tax considerations and environmental concerns are the least critical challenges. The study makes a meaningful contribution to the decision sciences and management literature by offering practical insights for policymakers and concludes by outlining clear avenues for future research.
subject: Articles
  topic: section
subject: Cryptocurrencies; fuzzy SIWEC; challenge; developing countries.
  topic: keywords
creator: Babatounde Ifred Paterne Zonon
creator: Mouhamed Bayane Bouraima
rights: Copyright (c) 2026 Babatounde Ifred Paterne Zonon, Mouhamed Bayane Bouraima (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-05-12
publication date: 2026-05-12
starting page: 348
ending page: 358
doi: 10.59543/vt0jrv20

Implementation of a Fuzzy Logic Controller for Long-Term Energy Management in a Hybrid AC/DC Microgrid
type: item
description: This paper presents the design and evaluation of a fuzzy logic controller (FLC) for long-term energy management in a hybrid AC/DC microgrid. The study is based on the Smart Energy Office Building (SEOB) system, which integrates photovoltaic (PV) generation, a lithium-ion battery, and a hydrogen-based energy storage system consisting of an electrolyzer, hydrogen tank, and fuel cell. Conventional rule-based state machine control (SMC) methods are limited by their rigidity and inability to adapt to dynamic operating conditions. To address this limitation, a Mamdani-type fuzzy inference system is developed using key system variables, including battery state of charge, power imbalance, and hydrogen storage level. The proposed FLC is implemented in a MATLAB/Simulink environment and evaluated through long-term simulations, including a full-year scenario, a summer week, and a winter week, using real measured data. The results demonstrate that the FLC achieves smoother control behavior, reduces switching frequency, and improves coordination between energy storage components compared with SMC. Specifically, it reduces battery cycling, extends continuous operation of the fuel cell, and enhances overall system stability. While slightly increasing grid interaction, the FLC enables a more balanced and efficient energy distribution within the microgrid. The findings confirm that fuzzy logic control provides a robust, computationally efficient, and interpretable solution for long-term energy management in hybrid microgrids, offering significant potential for improving system performance and component lifetime.
subject: Articles
  topic: section
subject: Fuzzy logic control
  topic: keywords
subject: Energy management system
  topic: keywords
subject: Hybrid energy storage system
  topic: keywords
subject: Photovoltaic system
  topic: keywords
creator: Junze Li
creator: Yan Chen
creator: Junpeng Lyu
rights: Copyright (c) 2026 Junze Li, Yan Chen, Junpeng Lyu (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-04-25
publication date: 2026-04-25
starting page: 281
ending page: 303
doi: 10.59543/nwwejh95

Applying the CRADIS Method with Probabilistic Uncertain Linguistic T-Spherical Fuzzy Sets for Circular Supplier Selection
type: item
description: This paper proposes a multi-criteria decision-making model based on the Probabilistic Uncertain Linguistic T‑Spherical Fuzzy Set (PULTSFS), integrating FUCOM, ITARA, and CRADIS, to address the ambiguity and uncertainty inherent in evaluation information for circular supplier selection. It also tackles the limitations of existing CRADIS methods in complex fuzzy environments, the lack of a scientific mechanism that synergizes subjective and objective weighting, and the incomplete depiction of evaluation information. First, a Euclidean distance measure is defined based on the relevant concepts of PULTSFS. Next, the subjective weights of the criteria are derived using the Full Consistency Method (FUCOM), while the objective weights are determined by extending the ITARA method within the PULTSFS framework, thereby establishing a combined weighting mechanism that integrates subjective and objective considerations. Finally, a multi-criteria decision-making model is constructed based on the novel PULTSF-CRADIS approach. Using circular supplier selection as a case study, sensitivity analysis and comparative validation demonstrate that the proposed model yields robust and reliable ranking results, with significantly enhanced capability in handling uncertain information and improved adaptability to complex decision-making scenarios, offering a more precise decision-making tool for supplier selection in the context of the circular economy.
subject: Articles
  topic: section
subject: PULTSFS; CRADIS; circular supplier selection; combined weighting; multi-criteria decision-making
  topic: keywords
creator: Kuo Zhang
creator: Ning Zhong
creator: Haolun Wang
rights: Copyright (c) 2026 Kuo Zhang, Ning Zhong, Haolun Wang (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-04-22
publication date: 2026-04-22
starting page: 260
ending page: 280
doi: 10.59543/0ajp5435

Reasoning About the Non-Existent in Large Language Models: Benchmark Success, Negation Failure, and Exploratory Creativity
type: item
description: Can artificial intelligence reason about things that do not exist, are not true, or are merely hypothetical? This paper addresses these questions with a two-part analysis of non-existence reasoning in state-of-the-art large language models. The analysis includes four components: counterfactual inference, negation handling, creative novelty, and multi-turn persistence of imaginary knowledge. Part 1 uses a benchmark-calibrated forecasting model to predict future performance for 12 models on 15,300 simulated datapoints. Part 2 revisits the question with live API evaluations of 5 state-of-the-art models on over 654 items covering CRASS-style counterfactual questions, negation tasks, creativity prompts, and multi-turn dialogues about imaginary topics. The outcome is a differentiated capability profile rather than a simple yes or no answer to the question of whether AI can reason about non-existence. The sampled CRASS items produced near-ceiling performance relative to the published human baseline, suggesting that some portions of the benchmark may no longer provide strong discriminative power among state-of-the-art models. By contrast, negation was associated with statistically significant performance drops in the four complete model runs, with the same directional tendency also visible in the partial fifth run. The conclusions about creative novelty and multi-turn imaginary persistence are more provisional since they rested on proxy measures and heuristic contradiction detection. In substance, the results imply that top models can execute certain types of structured counterfactual reasoning effectively but display a robust and replicable deficit with respect to negation; indications of difficulty with creative novelty and multi-turn imaginative persistence are present but rest on weaker proxy measures and should be treated as provisional. In method, they underscore the importance of treating benchmark-calibrated forecasts as perishable hypotheses that must be revalidate…
subject: Articles
  topic: section
subject: Frontier Large Language Models
  topic: keywords
subject: Counterfactual Reasoning
  topic: keywords
subject: Negation Blindness
  topic: keywords
subject: Simulation-Reality Gap
  topic: keywords
subject: Benchmark Saturation
  topic: keywords
subject: CRASS
  topic: keywords
subject: Live Evaluation
  topic: keywords
creator: Abdullah Önden
creator: İsmail Önden
rights: Copyright (c) 2026 Abdullah Önden, İsmail Önden (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-27
publication date: 2026-03-27
starting page: 193
ending page: 207
doi: 10.59543/avhexs22

Multiple Attribute Decision Method Based on Neutrosophic Z-Number Frank Combined Compromise Solution Approach
type: item
description: Uncertain multi-attribute decision-making (MADM) represents a fundamental area within decision science, with extensive applications across various fields. The neutrosophic Z-number (NZN) offers a powerful framework for modeling uncertain information, as it captures not only the imprecise, incomplete, and inconsistent preferences of decision-makers but also the associated reliability of those preferences. This study develops a novel MADM approach by integrating the Combined Compromise Solution (CoCoSo) method, Frank aggregation operators, and the Logarithmic Decomposition of Criteria Importance (LODECI) model within the NZN context. Firstly, operational rules for NZNs are defined based on Frank triangular norms. Subsequently, the neutrosophic Z-number Frank weighted averaging (NZNFWA) and neutrosophic Z-number Frank weighted geometric (NZNFWG) operators, along with their ordered variants, are proposed to enhance the flexibility and robustness of aggregating NZN evaluations. To objectively determine attribute weights, an extended LODECI model grounded in the NZN score function is introduced. The enhanced CoCoSo method is then applied, utilizing the proposed aggregation operators to derive the final ranking of alternatives. The applicability and effectiveness of the proposed framework are demonstrated through a case study assessing green logistics development levels. Furthermore, a parameter sensitivity analysis and a comparative analysis with existing NZN-based methods are conducted to validate its robustness and superiority. The results confirm that the proposed NZN-LODECI-CoCoSo approach exhibits strong stability and practicality, providing an effective solution for complex decision-making problems under uncertainty.
subject: Articles
  topic: section
subject: Multiple Criteria Decision Analysis; Neutrosophic Z-Number; LODECI; CoCoSo
  topic: keywords
creator: Dongmei Li
creator: Yuan Rong
rights: Copyright (c) 2026 Dongmei Li, Yuan Rong (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-04-11
publication date: 2026-04-11
starting page: 236
ending page: 259
doi: 10.59543/s2d95334

A Generative AI–Based Decision-Support Framework for Early-Stage Architectural Design: Evidence from Sustainable Tiny Houses
type: item
description: This study investigates the potential of generative artificial intelligence–based text-to-image models as early-stage decision-support tools in sustainable tiny house design. Within the conceptual design process, the study systematically models key components—formal schema development, material selection, and spatial atmosphere—through a controlled prompt framework that enables structured comparison of design alternatives. Design outputs generated by diffusion-based models, including ChatGPT (DALL·E 3), Copilot, and Gemini AI, are evaluated using a multi-criteria framework encompassing sustainability principles (use of natural materials, compact planning, energy-efficient openings), typological consistency, and overall design quality. To examine how prompt variations shape design outcomes, three analytical dimensions are defined: (1) morphological decisions (plan layout, roof typology, form), (2) material decisions (timber, recycled composites, hybrid systems), and (3) spatial atmosphere (daylighting, color palette, interior warmth). The findings show that text-to-image workflows not only accelerate the generation of design alternatives but also enable a structured interpretation of how different design options align with specified criteria. In this sense, generated outputs function as interpretable design propositions that can be comparatively assessed within a transparent evaluation logic. The results further suggest that generative AI supports designers by externalizing design reasoning, facilitating the exploration, comparison, and justification of early-stage decisions. These results position generative AI as a viable component of next-generation design decision-support systems, particularly in contexts where rapid iteration, multi-criteria evaluation, and the articulation of design reasoning are critical.
subject: Articles
  topic: section
subject: Generative Artificial Intelligence, Design Decision Support, Text-to-Image Models, Architectural Design Reasoning, Sustainable Tiny House
  topic: keywords
creator: Tuğçe Çelik
rights: Copyright (c) 2026 Tuğçe Çelik (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-19
publication date: 2026-03-19
starting page: 175
ending page: 192
doi: 10.59543/nnc2a471

Dynamic Intuitionistic Fuzzy Soft Set-Based WASPAS Model for Multi-Criteria Decision Making in Renewable Energy Selection
type: item
description: The nature of the multi-criteria decision-making problem is full of uncertainty in the expert decisions of the task as well as the changes in the technological performance over time, which represent the other characteristics of renewable energy planning. Nevertheless, several of the available fuzzy multi-criteria decision-making solutions are not dynamic in change and they fail to adapt to change over time. In order to overcome this weakness, this paper suggests a dynamic intuitionistic fuzzy soft set based WASPAS model to assess the renewable energy alternatives under uncertainty and time dynamics. The proposed method would incorporate the intuitionistic fuzzy soft set modeling and the WASPAS aggregation protocol in an attempt to model expert hesitation as well as synthesize multi-year appraisals by a recency-based temporal weighting. To show how the framework could be applied, a case study of the renewable energy planning in Pakistan is performed. There are four options that include solar, wind, hydropower, and bioenergy, which are analyzed in eight economic, environmental, and technical parameters, based on the expert ratings of 2021-2023. The findings show that the hydropower gives the best overall performance, then the wind, the solar, and the bioenergy. Sensitivity and comparative analyses also prove the viability and solidity of the obtained rankings. The advanced dynamic intuitionistic fuzzy soft set based WASPAS framework is a powerful decision-support system in the renewable energy planning process in the uncertainties and dynamic conditions and can be applied in other multi-criteria decision-making processes whereby the criteria and expert preference changes with time.
subject: Articles
  topic: section
subject: Dynamic Intuitionistic Fuzzy Soft Set
  topic: keywords
subject: WASPAS Method
  topic: keywords
subject: Temporal Aggregation
  topic: keywords
creator: Sania Saleem
creator: Maria Riaz
creator: Fatima Razaq
creator: Muhammad Saeed
rights: Copyright (c) 2026 Sania Saleem, Maria Riaz, Fatima Razaq, Muhammad Saeed (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-04-07
publication date: 2026-04-07
starting page: 208
ending page: 235
doi: 10.59543/mxct6309

A Hybrid Deep Learning-Driven Stochastic MCDM Framework for Uncertainty-Aware Renewable Energy Resource Selection
type: item
description: Renewable energy source selection is a difficult decision for policymakers due to the high level of uncertainty associated with it, along with the technical factors that accompany it. Most Multi-Criteria Decision Making (MCDM) techniques are based on fixed input parameters or the opinions of experts, which do not account for the predictive uncertainties inherent in energy production data. This research aims to provide a more effective, efficient, and robust framework for the selection of the best mix of renewable energy sources for the future. A five-phase methodology is proposed for this purpose, where the first phase involves the application of the Subjective-Objective Median-based Importance Technique (SOMIT) for the calculation of the weights of the decision criteria. In the second phase, a Multi-Layer Perceptron (MLP) neural network is designed for the prediction of the technical efficiency of solar, wind, biomass, and geothermal energy. Third, the absolute error residuals are monitored for the development of the ANN Uncertainty Criterion. In the fourth phase, the triple validation of the uncertainty-aware data is performed through the application of the MCDM engine, which is based on the TOPSIS, PROMETHEE II, and RAWEC algorithms. In the fifth phase, sensitivity analysis is performed, followed by the Monte Carlo Simulation for testing the robustness of the framework. The results from applying the proposed framework show a clear change in preference. For Türkiye’s 2053 net zero target, solar energy is now considered the best future energy source, with a preference score of 0.685. This is higher than wind energy, which most decision-makers previously favored because of its capacity factor. Thus, the proposed framework is more effective, efficient, and robust, as indicated by the Monte Carlo Simulation results, where the results obtained from the application of the proposed framework were stable for 92.4% of the scenarios.
subject: Articles
  topic: section
subject: Deep Learning
  topic: keywords
subject: Stochastic MCDM
  topic: keywords
subject: Renewable Energy Policy
  topic: keywords
subject: Predictive Trust
  topic: keywords
subject: Monte Carlo Simulation
  topic: keywords
creator: Gülay Demir
rights: Copyright (c) 2026 Gülay Demir (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-03-10
publication date: 2026-03-10
starting page: 149
ending page: 174
doi: 10.59543/9hjx0v16

Regional Prioritization Model for Energy Storage Investments: A Strategic Approach to Public Policies in the Case of Türkiye
type: item
description: This study aims to identify the most suitable energy storage technologies for Türkiye’s seven geographical regions by developing a scientifically grounded and region-sensitive decision support model that accounts for socioeconomic and environmental disparities. The proposed framework seeks to enhance the efficiency of energy investments, ensure more effective use of public resources, and strengthen energy supply security while contributing to sustainable development goals. Expert evaluations obtained from 70 specialists across the seven regions are analyzed using a Euclidean distance-based expert weighting method, enabling an objective and distance-sensitive assessment of expert judgments. The importance weights of decision criteria are calculated through the Fractal Fuzzy LOPCOW method, which integrates objective data structures with multi-layered uncertainty modeling, while energy storage alternatives are ranked using the Fractal Fuzzy RATGOS technique, allowing the simultaneous processing of the geometric properties of the decision space and structural uncertainty. The study’s primary innovation lies in employing fractal fuzzy set theory to capture uncertainty in a more detailed and dynamic manner than classical fuzzy approaches, and in integrating Euclidean-based expert weighting, Fractal Fuzzy LOPCOW, and Fractal Fuzzy RATGOS within a unified and high-resolution multi-criteria decision-making framework. By providing region-specific energy storage priorities, the model offers a strategic tool for policymakers, regional development agencies, and private investors, facilitating evidence-based energy planning, more accurate investment decisions in high-cost storage systems, improved renewable energy integration, efficient allocation of public funds, and the promotion of environmentally sustainable and economically beneficial regional development.
subject: Articles
  topic: section
subject: Fractal Fuzzy Sets
  topic: keywords
subject: Energy Storage Technology Selection
  topic: keywords
subject: Multi-Criteria Decision-Making
  topic: keywords
subject: Regional Energy Planning
  topic: keywords
subject: Decision Support Systems
  topic: keywords
creator: Hasan Dinçer
creator: Serhat Yüksel
creator: Serkan Eti
rights: Copyright (c) 2026 Hasan Dinçer, Serhat Yüksel, Serkan Eti (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-28
publication date: 2026-02-28
starting page: 136
ending page: 148
doi: 10.59543/352zs860

Probabilistic Uncertain Linguistic T-Spherical Fuzzy Aggregation Operators-based Multi-Criteria Decision-Making Method and Its Application with Unknown Weight Information
type: item
description: To comprehensively and precisely capture the fuzziness, uncertainty, hesitancy, and inherent randomness of evaluation information, this paper introduces a novel generalized concept: the probabilistic uncertain linguistic T-spherical fuzzy set (PULTSFS). This concept is developed by leveraging the unique advantages of both probabilistic uncertain linguistic term set (PULTS) and T-spherical fuzzy set (TSFS). The study begins by proposing a standardization method for PULTSFS, defining its score and accuracy functions, and establishing a Hamming distance measure for any two probabilistic uncertain linguistic T-spherical fuzzy numbers (PULTSFNs). Building upon the fundamental operational rules of PULTSFNs, the research then develops and analyzes the properties of weighted average and weighted geometric aggregation operators (AOs) tailored for PULTSFS. A decision-making framework using these AOs is subsequently constructed to address multi-criteria decision-making (MCDM) challenges where criteria weights are entirely unknown. Finally, the practicality and efficacy of the proposed method are validated through a numerical example, which includes a sensitivity analysis and comparative benchmarks against existing methods. The results demonstrate that the proposed method significantly improves the accuracy and reliability of resolving complex decision-making scenarios, ensuring that the resulting decisions are more practical and better aligned with real-world requirements.
subject: Articles
  topic: section
subject: Probabilistic uncertain linguistic T-spherical fuzzy set;
  topic: keywords
subject: Aggregation operators;
  topic: keywords
subject: Multi-criteria decision-making
  topic: keywords
creator: JingYing Huang
creator: Haolun Wang
creator: Kuo Zhang
rights: Copyright (c) 2026 JingYing HUANG, HaoLun WANG, Kuo ZHANG (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-15
publication date: 2026-02-15
starting page: 111
ending page: 135
doi: 10.59543/289xyy59

Decision-Oriented Risk Assessment in Micromobility Using AHP
type: item
description: In the modern world, a lot of people and organizations are impacted both directly and indirectly by the micromobility sector, which is one of the most significant subheadings of the sharing economy model. The existing condition should be made public, and any hazards should be identified in order to reduce the negative consequences and take the appropriate actions. This study identified the hazards associated with the micromobility sector after looking at the sharing economy and risk allocation concepts. The optimal option is then identified using the AHP approach. It is a chance to direct and distribute accountability for the applications that will be submitted after that.
subject: Articles
  topic: section
subject: Micromobility; AHP; Risk assessment
  topic: keywords
creator: Ilgin Gokasar
creator: Emir Yemlihalioglu
rights: Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
date: 2026-02-06
publication date: 2026-02-06
starting page: 75
ending page: 95
doi: 10.59543/c1k9hg77

Bayesian Hierarchical Modeling and Clustering for Malignant Cancer Diagnosis
type: item
description: Late-stage cancer diagnosis remains a major barrier to improving survival rates, yet the relative contributions of tumor, patient, and region-level factors have not been well quantified. In this study, we develop a 3-layer Bayesian hierarchical logistic regression model to investigate late-stage cancer diagnosis. The model includes fixed effects for tumor characteristics and random effects for patients and regions. Model parameters are estimated using the No-U-Turn Sampler, and posterior samples are evaluated with effective sample sizes and convergence diagnostics. From intra-class correlation estimates, we find that patient-level variation has a substantially stronger influence on late-stage diagnosis than region-level variation. Lastly, we utilize a Gaussian Mixture Model to cluster posterior patient-level random effects, identifying nine distinct clusters characterized by age, sex, and tumor features. Our findings suggest that individualized, patient-focused strategies may be more effective than geographically targeted approaches for promoting earlier cancer detection.
subject: Articles
  topic: section
subject: Late-stage cancer; Bayesian hierarchical logistic regression; No-U-Turn sampler; Patient heterogeneity; Cancer risk cluster
  topic: keywords
creator: Sijia Zhu
creator: Jonathan Ma
creator: Zhe Liu
rights: Copyright (c) 2026 Sijia Zhu, Jonathan Ma, Zhe Liu (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-02-07
publication date: 2026-02-07
starting page: 96
ending page: 110
doi: 10.59543/w22sya71

A study on the location selection of Cold Chain Logistics Distribution Centre Based on Entropy-TOPSIS Model: The Case of a City in Anhui Province
type: item
description: The construction of cold chain distribution centres serves as a critical means of reducing post‑harvest agricultural product losses and food waste during circulation. Hence, how to select and locate cold chain logistics distribution centres scientifically has become an important issue. This research proposes a location selection method based on the Entropy-Weighted TOPSIS approach to quantitatively select cold chain logistics distribution centres. First, a site selection index system is constructed from four dimensions: economic development level, distribution operation capacity, economic environments and cost factors. Subsequently, entropy method is employed to determine the weights, and a site evaluation model is constructed based on TOPSIS. The findings illustrate that Entropy-TOPSIS method can be applied to the cold chain logistics centre selection problems. Finally, this paper takes a selected city as a case study and applies the developed site evaluation model to determine the location of its cold chain logistics distribution centre.
subject: Articles
  topic: section
subject: Cold Chain Logistics Distribution Centre Location Selection; Entropy method; TOPSIS
  topic: keywords
creator: Ping He
rights: Copyright (c) 2026 Ping He (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-31
publication date: 2026-01-31
starting page: 58
ending page: 74
doi: 10.59543/m3mz3h54

Dynamic Financial Stress Indicators Using Machine Learning: An Adaptive Composite Framework for Early Warning and Systemic Risk Assessment
type: item
description: The present study proposes a machine learning-enhanced forecasting framework for financial stress that addresses critical limitations in static threshold approaches used by regulatory authorities. Utilizing a comprehensive dataset comprising daily S&P 500 information spanning multiple crisis episodes, we employ gradient boosting algorithms with dynamic threshold detection to predict market stress occurrences. The hybrid ensemble model utilized in this study has been shown to significantly surpass conventional econometric methods in terms of forecasting accuracy and the timeliness of early warning systems. The framework demonstrated a high degree of efficacy in predicting major crisis events during the hold-out period, exhibiting a substantial improvement in detection rates when compared to Federal Reserve indices. The application of feature importance analysis has yielded findings that demonstrate the presence of regime-dependent patterns. These findings indicate that there is a notable increase in sensitivity to real-economy variables, such as unemployment, during periods of recession. For practitioners, the continuous stress probability forecasts enable graduated risk management protocols and generate tangible portfolio gains. Researchers are particularly interested in the establishment of novel benchmarks for financial stress forecasting and in how machine learning can capture non-linear transmission mechanisms that conventional approaches cannot detect.
subject: Articles
  topic: section
subject: Composite stress indicator
  topic: keywords
subject: Machine learning
  topic: keywords
subject: Financial market stress
  topic: keywords
subject: Early warning systems
  topic: keywords
subject: Gradient boosting
  topic: keywords
creator: Abdullah Kürşat Merter
creator: Yavuz Selim Balcıoğlu
rights: Copyright (c) 2026 Abdullah Kürşat Merter, Yavuz Selim Balcıoğlu (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-15
publication date: 2026-01-15
starting page: 51
ending page: 57
doi: 10.59543/zrej1n55

Application challenges of modern education technology in China's higher education teaching management: A fuzzy MARCOS method
type: item
description: To systematically evaluate the barriers to the adoption of modern educational technology in the teaching management of higher education institutions in China, this study introduces an integrated evaluation framework based on MCDM to discuss existing problems. First, a comprehensive barrier evaluation indicator system is constructed by synthesizing existing studies on educational technology implementation and the practical objectives of teaching management in Chinese higher education. Second, a q-rung picture fuzzy set (q-RPFS) is employed to characterize experts’ assessments, enabling a more flexible and accurate representation of uncertainty, hesitation, and conflicting judgments inherent in barrier evaluation. The weighted averaging (WA) operator is then applied to aggregate fuzzy evaluation information across indicators. Third, the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method is adopted to rank the identified barriers by considering both ideal and anti-ideal solutions, thereby enhancing the robustness and discriminative power of the evaluation results. Finally, a case study involving representative higher education institutions in China is conducted to validate the feasibility and effectiveness of the proposed framework. This article considers different types of universities as alternatives, and digital competencies of teaching management staff has the greatest impact on them. The results demonstrate that the integrated approach provides a systematic and reliable tool for identifying and prioritizing key barriers to modern educational technology in higher education teaching management, offering valuable insights for policy formulation and managerial decision-making.
subject: Articles
  topic: section
subject: Multi-criteria decision-making; q-rung picture fuzzy set; barriers; MARCOS
  topic: keywords
creator: Huawei Wang
rights: Copyright (c) 2026 Huawei Wang (Author) https://creativecommons.org/licenses/by/4.0
date: 2026-01-09
publication date: 2026-01-09
starting page: 37
ending page: 50
doi: 10.59543/9gaa3t67

Fuzzy LBWA Framework for Evaluate the Criteria Affecting Ammunition Consumption
type: item
description: One of the most important aspects of military logistics is ammunition. Ammunition is one of the most crucial materials to consider in order to win a war. Therefore, this study examines the criteria affecting ammunition consumption. The aim is to provide war planners with an analytical perspective. Military expert opinions were consulted for the evaluation of the criteria, and fuzzy multi-criteria decision-making (FMCDM) was chosen as the solution methodology. The Level Based Weight Assessment (LBWA), commonly used in military problems in the literature, was employed as the method. The most important criterion is the number of enemy targets. This is followed by interceptions and aerial photographs containing potential targets obtained through intelligence activities. Finally, the changes in criterion weights were examined using different scenarios for the coefficient of elasticity value, and the results were validated.
subject: Articles
  topic: section
subject: Fuzzy sets
  topic: keywords
subject: Multi-criteria decision making
  topic: keywords
subject: Level Based Weight Assessment
  topic: keywords
subject: Ammunition
  topic: keywords
creator: Kemal Gürol Kurtay
creator: Hakan Ayhan Dağıstanlı
rights: Copyright (c) 2025 Argumentation Based Systems Journal https://creativecommons.org/licenses/by/4.0
date: 2025-12-21
publication date: 2025-12-21
starting page: 24
ending page: 36
doi: 10.59543/dczp6c77

A Fuzzy Comparative Study in Logistics Risk Assessment
type: item
description: Logistics and supply chain systems operate under increasingly complex risk environments shaped by globalization, digital transformation, and multi-layered operational interdependencies. In such contexts, fuzzy multi-criteria decision-making (FMCDM) approaches are widely employed to support risk evaluation problems characterized by qualitative judgments and epistemic uncertainty. Despite their extensive use, limited attention has been paid to how different fuzzy weighting paradigms implicitly construct and interpret criterion importance within the same decision framework. This study addresses this issue by comparatively examining expert-driven, data-driven, and impact-oriented fuzzy weighting approaches within a unified logistics risk assessment context. Fermatean fuzzy SWARA, fuzzy CRITIC, and fuzzy MEREC are employed to represent cognition-driven, structure-driven, and consequence-oriented constructions of importance, respectively. All methods are applied to the same expert-based decision structure to ensure analytical consistency and comparability. The results reveal that substantially different risk prioritization patterns emerge across weighting approaches, even when expert judgments and decision conditions are held constant. These differences reflect methodological variations in how importance is operationalized rather than inconsistencies in data or expert input. By highlighting the method-dependent nature of importance construction, the study contributes to a clearer understanding of fuzzy weighting behavior and provides methodological guidance for selecting appropriate FMCDM weighting paradigms in complex logistics risk assessment problems.
subject: Articles
  topic: section
subject: Fuzzy-MCDM;
  topic: keywords
subject: Risk Assessment
  topic: keywords
subject: Criterion importance
  topic: keywords
subject: Data-driven weighting
  topic: keywords
subject: Explainability Augmentation
  topic: keywords
creator: Mine Konur Bilgen
creator: Kaan Bilgen
rights: Copyright (c) 2025 Mine Konur Bilgen, Kaan Bilgen (Author) https://creativecommons.org/licenses/by/4.0
date: 2025-12-19
publication date: 2025-12-19
starting page: 1
ending page: 23
doi: 10.59543/k4az9k72