Enhancing industrial decision-making through Multi-Criteria Decision-Making approaches and ML-Integrated Frameworks
Year: 2024
Type: article
Abstract: Decision-making in current industrial contexts has shifted from intuition to a data-driven approach, requiring prompt processing of huge datasets. However, conventional Multi-Criteria Decision Making (MCDM) methodologies fall short of navigating the intricacy of large datasets. This paper introduces an innovative decision-support system integrating multi-criteria methods with machine learning techniques such as artificial neural networks. The proposed six-step framework aims to optimize operational decisions by analyzing real-time performance data. The research contributes to the advancement of decision-making methodologies in the industrial field, offering dynamic responsiveness and improved recommendations compared to traditional MCDM methods. While results are promising, future work should focus on robustness testing particularly in terms of its dependence on real-time data, to ensure sustained efficacy and mitigate potential biases in recommendations over time.
Source: Data & Metadata
DOI: 10.56294/dm2024391
Authors: Hala Mellouli, Anwar Meddaoui, Abdelhamid Zaki
Institution: University of Hassan II Casablanca
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4401490197