Aug 2026· Journal of Intelligent Decision Making and Information Science· 0 citations· 59 references
TL;DR
A new AI assisted Fuzzy Multi-Criteria Decision-Making model is presented to assess the performance of an organization and to assist in making strategic management decisions to enhance the decision consistency, transparency of decision making, strategic alignment and managerial responsiveness.
Abstract
The application of Artificial Intelligence (AI) in decision-making processes is changing the way organizations approach and make decisions.Organizations are increasingly turning to Artificial Intelligence (AI) to analyze complex data, detect trends, predict outcomes, and consider various options in their decision-making process. Organisation performance assessment, however, encompasses numerous both qualitative and quantitative criteria, uncertainty, conflicting priorities of management and imprecise expert opinions – which cannot be fully captured by conventional decision making techniques. This study presents a new AI assisted Fuzzy Multi-Criteria Decision-Making (F-MCDM) model to assess the performance of an organization and to assist in making strategic management decisions. The proposed framework is based on the use of AI-based analytical capabilities and fuzzy decision-making techniques that can handle the vagueness of managerial assessments and prioritize strategic options in a systematic manner. The relative importance of organisational performance criteria are determined by using the fuzzy Analytic Hierarchy Process (FAHP), and the fuzzy Technique for Order Preference by Similarity to Ideal Solution (FTOPSIS) is used to rank the strategic alternatives. It looks at aspects such as financial performance, operational efficiencies, innovation capability, customer orientation, human-resource effectiveness, and strategic adaptability as well as digital capability. The aim of the proposed approach is to enhance the decision consistency, transparency of decision making, strategic alignment and managerial responsiveness. The study illustrates that AI and fuzzy MCDM are able to offer an intelligent decision-support mechanism to organisations in an uncertain and competitive environment.
This paper investigates the role of contemporary MCDM approaches in improving organizational performance assessment and strategic decision-making by examining methodological developments, practical implementation frameworks, evaluation indicators, and performance outcomes and proposes an integrated conceptual framework that facilitates objective strategic prioritization, efficient resource allocation, and sustainable organizational competitiveness.
Dr. Padmavathi SM¹, S² Dr.Suma, B. Dr.LakshmiR et al.· Journal of Intelligent Decis...· 0 citations
This work examines the application of multi-criteria decision-making (MCDM) techniques in conjunction with artificial intelligence (AI) and machine learning (ML) within sustainable supply chain management (SSCM). Traditional MCDM methods struggle with large datasets and decision-making environments that change dynamically. AI and ML help get around these problems by making it easier to deal with uncertainty and by automating some parts of the analysis. The review discusses new methods, such as Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS), Simple Weight Calculation (SIWEC), and the Aczel Alsina Weighted Assessment (ALWAS). These methods give decision-makers more options for how to look at environmental, social, and economic factors. AI and ML tools can help MCDM methods work better with dynamic data, which improves risk assessment and resource allocation. Combining such methods makes MCDM approaches better able to deal with complicated decision-making situations and gives a more complete picture of how well a sustainable supply chain is doing. The review also offers practical guidance on selecting suitable learning algorithms according to the available data and their intended role in the MCDM process. In addition, it distinguishes between reporting gaps, which can be resolved through more consistent reporting practices, and conceptual gaps, which require the development of new methodological approaches.
Unknown authors· Decision Making Advances· 0 citations
Artificial Intelligence (AI) has advanced significantly in the 21st century, evolving into a crucial tool for decision-making. A prominent trend is its integration with Multi-Criteria Decision-Making/Aiding (MCDM/A) methods to support complex decisions across diverse engineering domains. This paper presents a systematic literature review analyzing 111 papers from scientific databases on integrating AI with MCDM/A methods. Unlike prior reviews that primarily catalogue methods or hybrid techniques, this study introduces a socio-technical analytical framework comprising three layers—technical configurations, functional mechanisms, and human–AI collaboration patterns—to explain how and why AI reshapes multicriteria decision processes. The findings reveal recurrent architectural patterns, identify dominant functional roles of AI across decision-process phases, and uncover an emerging shift from automation-oriented systems toward augmentation-based decision support. Rather than providing a purely descriptive mapping of the literature, this review undertakes an investigative task guided by a socio-technical framework. By examining how structural configurations of AI–MCDM/A integration reshape the stages of the decision process and redistribute roles between humans and AI, the study moves beyond cataloguing techniques to uncover underlying integration logics, structural tensions, and developmental trajectories. In doing so, it offers both a conceptual consolidation for scholars and a structured foundation for the design of next-generation, human-centered intelligent decision support systems (IDSS).
Bruno Cicciú, E. Frej, A. D. de Almeida· IMA Journal of Management Ma...· 0 citations
In Multi-Criteria Group Decision-Making (MCGDM), the assignment of weights to decision-makers is a crucial but methodologically delicate step, especially when the group includes both human experts and artificial experts such as intelligent agents, Artificial Intelligences (AIs) or Large Language Models (LLMs). Existing weighting strategies are often either difficult to interpret or poorly suited to heterogeneous groups of evaluators. In this paper, we investigate a fuzzy rule-based approach to expert weighting, building on a previously introduced methodological framework and focusing here on its application-oriented validation. The proposed method models expert weighting as a Fuzzy Rule-Based System (FRBS) in which the relevant properties of the experts are represented by linguistic variables and combined through interpretable IF–THEN rules. In this way, weighting policies can be expressed transparently and adapted to the requirements of the decision domain. The framework produces normalised weights in the interval [0,1], which can then be incorporated into standard MCGDM aggregation procedures. To assess the operational behaviour of the approach, we consider an application involving the weighting of four open-source LLMs (apertus:8b, gemma4:e4b, mistral-small3.2:24b, and nemotron-cascade-2:30b) over three multilingual criteria (English, Italian, Portuguese) and two resource-side criteria (VRAM, open-sourceness), each modelled by three trapezoidal fuzzy sets and combined into a five-class output partition; the underlying dataset is built from 10 independent repetitions of 100 questions per model. Under a language-focused rule base of five IF–THEN rules, the four experts receive sharply separated normalised weights (0.003,0.149,0.301,0.548)—a top-to-bottom ratio above 180—whereas a combined linguistic/resource-aware rule base of five rules flattens the distribution to (0.227,0.360,0.222,0.191) and selects a different winner, demonstrating that policy changes are encoded explicitly in the output. A 100-run Kendall’s τ perturbation analysis confirms that the induced rankings remain stable under moderate input noise, particularly for the language-focused policy, while substituting the Product t-norm with Gödel or Lukasiewicz leaves the language-focused ranking invariant but induces rank reversals in the more discriminative resource-aware policy. A comparison against three independent baselines (Markov Logic Networks, ProbLog, TOPSIS) shows that ProbLog reproduces the FRBS ordering in both case studies, MLN compresses the normalised scores under its global probabilistic interaction, and TOPSIS diverges whenever conditional IF–THEN preferences must be encoded. A worked end-to-end aggregation example with three alternatives, three criteria, and four experts further shows that the FRBS weights propagate into a clear selection of the best alternative, with aggregated scores (S1,S2,S3)=(8.88,6.78,6.21). These results confirm both the practical usability of the method and its suitability for contexts in which multiple, potentially competing, objectives must be balanced explicitly. Overall, the paper provides an application-oriented study of an FRBS-based weighting scheme for artificial experts, highlighting its interpretability, adaptability, and potential relevance for contemporary MCGDM settings.
L. Castronovo, Giuseppe Filippone, G. Giacopelli et al.· Electronics· 0 citations
Decision-making is one of the most fundamental activities within various organizations, and human resources are consistently involved in it, either individually or collectively. Decisions are always made in alignment with achieving organizational objectives; therefore, their quality directly or indirectly affects the entire organization and its human capital. Consequently, identifying and ranking the factors influencing decision-making plays a crucial role in enhancing organizational performance. In this regard, the present study aims to identify and rank the sub-criteria affecting the decision-making process using the Fuzzy Analytic Hierarchy Process (FAHP) technique. The findings indicate that the individual criterion exerts the highest influence, while the external organizational criterion is the least influential in the decision-making process. Among the sub-criteria, emotions and the alignment between competencies and job content rank the highest. This underscores their significance in performance evaluations and competency assessments during recruitment, selection, appointment, or strategic decision-making processes within organizations. Therefore, it is imperative for organizations to invest in these criteria to strengthen their decision-making processes.
Unknown authors· Management, Education and De...· 0 citations
The article examines the potential of artificial intelligence to optimize management processes and improve decision-making in large organizations and proposes a conceptual AI-based management framework that integrates data sources, analytical models, and decision support systems into a unified adaptive cycle with a feedback mechanism.
Serhii Kubitskyi, Yevhen Kozlovskyi, K. Balabukha et al.· Human Resources Management a...· 0 citations
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