Integrating machine learning and operations research methods for scheduling problems: a bibliometric analysis and literature review - Design, Engineering and Operation Management of Systems and Services
Communication Dans Un Congrès Année : 2024

Integrating machine learning and operations research methods for scheduling problems: a bibliometric analysis and literature review

Résumé

Operations research (OR) techniques have been widely used for optimizing problems, such as manufacturing scheduling, supply chain optimization, and resource allocation. Despite its effectiveness, traditional OR, especially exact methods, often struggle with scalability, computational efficiency, and adaptability to the dynamic and uncertain environments of Industry 4.0. While machine learning (ML) advancements provide novel approaches for addressing these challenges, they also present limitations, such as the lack of guaranteeing exact solutions and the need of relevant data. Therefore, the integration of OR and ML offers a balanced solution, leveraging ML's capability to extract patterns from large datasets and making predictive decisions and OR's precision to enhance decision-making processes, especially in scheduling tasks withing the context of Industry 4.0. This combination not only improves solution robustness and efficiency but also mitigates individual limitations of both fields. and make predictive decisions under uncertainty complements the decision-making process of OR. This paper aims to conduct a bibliometric analysis and a brief literature review on the integration of ML and OR, focusing on their application in scheduling problems.
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Dates et versions

hal-04689354 , version 1 (06-09-2024)

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  • HAL Id : hal-04689354 , version 1

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Ayoub Ouhadi, Zakaria Yahouni, Maria Di Mascolo. Integrating machine learning and operations research methods for scheduling problems: a bibliometric analysis and literature review. 18th IFAC Symposium on Information Control Problems in Manufacturing (INCOM 2024), Aug 2024, Vienna (AUSTRIA), Austria. ⟨hal-04689354⟩
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