modeling and simulation
Atoosa Ebrahimi Shah Abadi; Jahangir Yadollahi Farsi; Niloofar Nobari
Abstract
The rapid growth of AI-based platforms in the past decade has made it necessary to examine the technological and managerial dimensions of this phenomenon. This research aimed to conduct a comparative analysis of the structure and consequences of three types of transactional, innovative, and cohesive ...
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The rapid growth of AI-based platforms in the past decade has made it necessary to examine the technological and managerial dimensions of this phenomenon. This research aimed to conduct a comparative analysis of the structure and consequences of three types of transactional, innovative, and cohesive platforms within the ADO framework. The present study was conducted using meta-synthesis and qualitative analysis, through three stages of open, axial, and selective coding, based on a review of 70 selected articles from 2015 to 2025, and the data were examined in three axes of prerequisites, decisions, and consequences. The analytical framework, combining platform categorization and the ADO model, provided the basis for a detailed comparison of key components. The results showed that transactional platforms with microservice architecture and open API, innovation platforms with deep learning frameworks and GPU/TPU, and cohesive platforms with data integrity and big data governance, took different paths to improve productivity and information security. The findings also highlighted that cloud computing, data governance, and information security were common and essential elements of these types. The analysis showed that the application of the ADO framework can help design effective policies for the management, security, and development of data-driven ecosystems and provide new insights for researchers and policymakers on how to optimize the performance of AI platforms.
multiple-criteria decision-making
Fatemeh Mojibian; Maryam Daneshvar; Ehsan Kafash Abdi
Abstract
With the rapid expansion of digital technologies and the ongoing transformation of financial services, FinTech has emerged as a key driver of change in the banking industry. Banks, as core components of the financial system, face fundamental shifts in service delivery, customer behavior, and competitive ...
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With the rapid expansion of digital technologies and the ongoing transformation of financial services, FinTech has emerged as a key driver of change in the banking industry. Banks, as core components of the financial system, face fundamental shifts in service delivery, customer behavior, and competitive patterns following the integration of FinTech. However, the adoption of these technologies is inherently associated with multiple risks that may affect banks’ performance, security, and reputation. Thus, timely identification and effective management of FinTech-related risks are essential. This study aims to evaluate the critical risks of FinTech adoption in banks using a multi-criteria decision-making (MCDM) framework in a neutrosophic environment, with a case study on Pasargad Bank. First, relevant risk factors were identified through an extensive literature review. Using the neutrosophic Delphi method, seven major risks were confirmed: security, credit, operational, strategic and competitive, legal and regulatory, reputational, and liquidity risks. The relative importance of these risks was then assessed using the neutrosophic Best–Worst Method (BWM). The results highlight security risk as the most significant factor influencing FinTech adoption in Pasargad Bank. Finally, various FinTech implementation scenarios were ranked using the neutrosophic Multi-Attributive Border Approximation Area Comparison (MABAC) method, with the scenario of “collaboration with other banks to establish a FinTech consortium” receiving the highest priority. The findings provide valuable insights for banks to better understand critical risk dimensions and to select optimal strategies for the successful implementation of FinTech solutions.
multiple-criteria decision-making
Iraj Rouhi; Mahsa Pishdar; Maryam Hassanikordede
Abstract
Food loss and waste represent a major global challenge threatening food security and exacerbating climate change. Upcycling food waste into value-added products is increasingly recognized as an effective pathway toward a circular economy. This study introduces a novel integrated multi-criteria decision-making ...
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Food loss and waste represent a major global challenge threatening food security and exacerbating climate change. Upcycling food waste into value-added products is increasingly recognized as an effective pathway toward a circular economy. This study introduces a novel integrated multi-criteria decision-making (MCDM) framework based on Circular Intuitionistic Fuzzy Sets (CIFS) combined with MEREC objective weighting and CIFS-MARCOS ranking an approach not previously applied to food waste upcycling. Ten prominent upcycling strategies were identified from recent literature and evaluated against twelve sustainability criteria by ten food industry experts. Results revealed “market potential,” “public awareness of upcycled products,” and “food quality and safety” as the most influential criteria. Among strategies, producing sustainable textiles from food waste ranked first, followed by sustainable packaging, novel food ingredients, and bioenergy production. The proposed framework effectively handles uncertainty and dynamic interdependencies among criteria, offering a robust and original tool for prioritizing upcycling pathways. Findings provide policymakers and industry stakeholders with evidence-based guidance to maximize environmental, economic, and social benefits while supporting multiple Sustainable Development Goals (SDGs).
Industrial management
Hossein Sayyadi Tooranloo; Mohammad Zarei Mahmoudabadi; Reza Norouzi Avargani
Abstract
Purpose: Over the past decade, the development of Network Data Envelopment Analysis (NDEA) models has enabled researchers to capture the internal structures and interrelationships among sub-units of decision-making units (DMUs). Compared to conventional DEA models, this approach provides deeper managerial ...
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Purpose: Over the past decade, the development of Network Data Envelopment Analysis (NDEA) models has enabled researchers to capture the internal structures and interrelationships among sub-units of decision-making units (DMUs). Compared to conventional DEA models, this approach provides deeper managerial and analytical insights into performance evaluation. The purpose of this study is to systematically review and clarify the overall trends in the development and application of NDEA models, with a particular focus on uncertainty-based approaches, over the period 2014–2024.Methodology: This research employs Microsoft Excel and VOSviewer software tools to conduct co-word analysis, visualize scientific networks, and identify research clusters. The reviewed articles were classified into two major dimensions: research domain (application areas) and research logic (deterministic or non-deterministic).Findings: The results indicate that most NDEA applications are concentrated in industrial sectors, and that deterministic logic dominates the existing body of literature. The term network data envelopment analysis was identified as the second most frequent keyword following data envelopment analysis. Based on the synthesis of reviewed studies, this research proposes a conceptual framework for NDEA and outlines potential future research directions centered on the integration of NDEA with uncertainty theories.Originality/Value: To the best of our knowledge, no comprehensive study has simultaneously addressed network data envelopment analysis and uncertainty. By identifying research gaps, mapping the scientific structure of the field, and highlighting emerging themes and future avenues, this study provides a valuable reference for researchers and practitioners interested in performance evaluation under uncertainty.
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