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.
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.
Evaluation of the Utility of Value Creation in the Agricultural Product Supply Chain Empowered by Digital Technology
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.
Copyright (c) 2026 Yuting Sun, Cai Pan (Author) https://creativecommons.org/licenses/by/4.0
A Shapley Value-Based Argumentation Framework Integrating CRITIC and MARCOS for Sustainable Supplier Evaluation: Evidence from a Global Semiconductor Supply Chain
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.
Copyright (c) 2026 Huai-Wei Lo, Sheng-Wei Lin (Author) https://creativecommons.org/licenses/by/4.0
A Fuzzy SIWEC Methodology for Evaluating the Challenges of Cryptocurrencies in Developing Countries
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.
Cryptocurrencies; fuzzy SIWEC; challenge; developing countries.
Copyright (c) 2026 Babatounde Ifred Paterne Zonon, Mouhamed Bayane Bouraima (Author) https://creativecommons.org/licenses/by/4.0
Implementation of a Fuzzy Logic Controller for Long-Term Energy Management in a Hybrid AC/DC Microgrid
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.
Copyright (c) 2026 Junze Li, Yan Chen, Junpeng Lyu (Author) https://creativecommons.org/licenses/by/4.0
Applying the CRADIS Method with Probabilistic Uncertain Linguistic T-Spherical Fuzzy Sets for Circular Supplier Selection
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.
PULTSFS; CRADIS; circular supplier selection; combined weighting; multi-criteria decision-making
Copyright (c) 2026 Kuo Zhang, Ning Zhong, Haolun Wang (Author) https://creativecommons.org/licenses/by/4.0
Reasoning About the Non-Existent in Large Language Models: Benchmark Success, Negation Failure, and Exploratory Creativity
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 revalidated with live model evaluations in a rapidly evolving LLM environment.
Copyright (c) 2026 Abdullah Önden, İsmail Önden (Author) https://creativecommons.org/licenses/by/4.0
Multiple Attribute Decision Method Based on Neutrosophic Z-Number Frank Combined Compromise Solution Approach
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.
Multiple Criteria Decision Analysis; Neutrosophic Z-Number; LODECI; CoCoSo
Copyright (c) 2026 Dongmei Li, Yuan Rong (Author) https://creativecommons.org/licenses/by/4.0
A Generative AI–Based Decision-Support Framework for Early-Stage Architectural Design: Evidence from Sustainable Tiny Houses
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.
Generative Artificial Intelligence, Design Decision Support, Text-to-Image Models, Architectural Design Reasoning, Sustainable Tiny House
Copyright (c) 2026 Tuğçe Çelik (Author) https://creativecommons.org/licenses/by/4.0
Dynamic Intuitionistic Fuzzy Soft Set-Based WASPAS Model for Multi-Criteria Decision Making in Renewable Energy Selection
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.
Copyright (c) 2026 Sania Saleem, Maria Riaz, Fatima Razaq, Muhammad Saeed (Author) https://creativecommons.org/licenses/by/4.0
A Hybrid Deep Learning-Driven Stochastic MCDM Framework for Uncertainty-Aware Renewable Energy Resource Selection
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.
Copyright (c) 2026 Gülay Demir (Author) https://creativecommons.org/licenses/by/4.0
Regional Prioritization Model for Energy Storage Investments: A Strategic Approach to Public Policies in the Case of Türkiye
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.
Copyright (c) 2026 Hasan Dinçer, Serhat Yüksel, Serkan Eti (Author) https://creativecommons.org/licenses/by/4.0
Probabilistic Uncertain Linguistic T-Spherical Fuzzy Aggregation Operators-based Multi-Criteria Decision-Making Method and Its Application with Unknown Weight Information
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.
Probabilistic uncertain linguistic T-spherical fuzzy set;
Copyright (c) 2026 JingYing HUANG, HaoLun WANG, Kuo ZHANG (Author) https://creativecommons.org/licenses/by/4.0
Decision-Oriented Risk Assessment in Micromobility Using AHP
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.
Copyright (c) 2026 https://creativecommons.org/licenses/by/4.0
Bayesian Hierarchical Modeling and Clustering for Malignant Cancer Diagnosis
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.
Late-stage cancer; Bayesian hierarchical logistic regression; No-U-Turn sampler; Patient heterogeneity; Cancer risk cluster
Copyright (c) 2026 Sijia Zhu, Jonathan Ma, Zhe Liu (Author) https://creativecommons.org/licenses/by/4.0
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
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.
Cold Chain Logistics Distribution Centre Location Selection; Entropy method; TOPSIS
Copyright (c) 2026 Ping He (Author) https://creativecommons.org/licenses/by/4.0
Dynamic Financial Stress Indicators Using Machine Learning: An Adaptive Composite Framework for Early Warning and Systemic Risk Assessment
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.
Copyright (c) 2026 Abdullah Kürşat Merter, Yavuz Selim Balcıoğlu (Author) https://creativecommons.org/licenses/by/4.0
Application challenges of modern education technology in China's higher education teaching management: A fuzzy MARCOS method
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.
Multi-criteria decision-making; q-rung picture fuzzy set; barriers; MARCOS
Copyright (c) 2026 Huawei Wang (Author) https://creativecommons.org/licenses/by/4.0
Fuzzy LBWA Framework for Evaluate the Criteria Affecting Ammunition Consumption
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.
Copyright (c) 2025 Argumentation Based Systems Journal https://creativecommons.org/licenses/by/4.0
A Fuzzy Comparative Study in Logistics Risk Assessment
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.
Copyright (c) 2025 Mine Konur Bilgen, Kaan Bilgen (Author) https://creativecommons.org/licenses/by/4.0