March 21, 2024Open Access

Enhancing industrial decision-making through ML-integrated frameworks and multi-criteria decision-making approach

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Dynamic responsiveness and recommendation quality improve when multi-criteria decision making integrates with artificial neural network architectures.
Assessment using a six-step framework evaluates real-time performance data to optimize industrial choices, combining machine learning with multi-criteria methods.
Robustness testing under real-time data dependencies remains essential to verify decision-support system stability and avoid longitudinal recommendation bias.

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Implication

Framework analysis demonstrates enhanced operational decision-making in industrial systems, indicating superior dynamic responsiveness over conventional multicriteria methods.

Cite This Study

Hala et al. (2024) studied this question.

synapsesocial.com/papers/68e7318cb6db6435876aaed8 https://doi.org/10.21203/rs.3.rs-4125064/v1

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Implication

Framework analysis demonstrates enhanced operational decision-making in industrial systems, indicating superior dynamic responsiveness over conventional multicriteria methods.

Enhancing industrial decision-making through ML-integrated frameworks and multi-criteria decision-making approach

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Discussion

Implication

Cite This Study

Enhancing industrial decision-making through ML-integrated frameworks and multi-criteria decision-making approach

Key Points

Discussion

Implication

Cite This Study

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