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All Journal Asian Journal of Science, Technology, Engineering, and Art
Shokare, Clarke
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Arts Computer Science & IT Engineering Social Sciences

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Journal : Asian Journal of Science, Technology, Engineering, and Art

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Enhancing Decision Support Systems with Hybrid Machine Learning and Operations Research Models Shokare, Clarke
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 2 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i2.4933

Abstract

Decision Support Systems are critical tools in enhancing decision-making across various industries, providing data-driven insights to guide complex choices. Traditional support system , however, often face challenges related to uncertainty, complexity, and adaptability. This paper explores the integration of Hybrid Machine Learning (ML) and Operations Research (OR) models as a solution to these limitations. ML techniques, such as predictive analytics and deep learning, enable data-driven pattern recognition, while OR methodologies, including optimization and stochastic modeling, offer structured problem-solving approaches. By combining these paradigms, the proposed hybrid model aims to improve decision accuracy, resource allocation, and problem-solving efficiency.in addition, Real-world case studies in healthcare, supply chain management, finance, and transportation demonstrate the effectiveness of this hybrid approach in optimizing decision-making processes. A comparative analysis of hybrid ML-OR models with traditional DSS highlights significant improvements in computational efficiency, accuracy, and adaptability. This research underscores the potential of hybrid ML-OR frameworks to drive more intelligent, robust, and scalable decision support solutions for a wide range of applications.
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