Enhancing Decision Support Systems with Hybrid Machine Learning and Operations Research Models

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Page Numbers: 240-253
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Published: Feb 7, 2025
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Clarke Shokare1
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Clarke Shokare
University of Benin, Nigeria

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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How to Cite
Shokare, C. (2025). Enhancing Decision Support Systems with Hybrid Machine Learning and Operations Research Models. Asian Journal of Science, Technology, Engineering, and Art, 3(2), 240-253. https://doi.org/10.58578/ajstea.v3i2.4933

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Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

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