Machine Learning for Scheduling: A Paradigm Shift from Solver-Centric to Data-Centric Approaches
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866911412795736064 |
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| author | Liu, Anbang Lin, Shaochong Chen, Jingchuan Wu, Peng Shen, Zuojun Max |
| author_facet | Liu, Anbang Lin, Shaochong Chen, Jingchuan Wu, Peng Shen, Zuojun Max |
| contents | Scheduling problems are a fundamental class of combinatorial optimization problems that underpin operational efficiency in manufacturing, logistics, and service systems. While operations research has traditionally developed solver-centric methods emphasizing model structure and optimality, recent advances in machine learning are reshaping scheduling toward a more data-centric approach that leverages experience and enables fast decision-making in dynamic environments. This paper offers a framework-based synthesis and perspective on this methodological transition. We use the paradigm shift from solver-centric optimization to data-centric learning as a unifying lens to organize and interpret a rapidly expanding literature. We first briefly revisit classical optimization-based approaches and discuss how machine learning has been integrated to improve computational efficiency and guide search while retaining solver-based feasibility and accountability. We then synthesize end-to-end learning approaches that generate scheduling solutions (or solution-generating policies) directly from data, clarifying the key design choices in solution generation and feasibility handling. Building on these organizing frameworks, we compare learning mechanisms and training signals (supervised, self-supervised, and reinforcement learning) in terms of scalability, interpretability, and generalization, and highlight the trade-offs that matter for reliable deployment. Finally, we outline an agenda along three interdependent dimensions, scalability, reliability, and universality, that together define a pathway toward adaptive, intelligent, and trustworthy scheduling systems for data-driven operations management. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_22642 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Machine Learning for Scheduling: A Paradigm Shift from Solver-Centric to Data-Centric Approaches Liu, Anbang Lin, Shaochong Chen, Jingchuan Wu, Peng Shen, Zuojun Max Optimization and Control Scheduling problems are a fundamental class of combinatorial optimization problems that underpin operational efficiency in manufacturing, logistics, and service systems. While operations research has traditionally developed solver-centric methods emphasizing model structure and optimality, recent advances in machine learning are reshaping scheduling toward a more data-centric approach that leverages experience and enables fast decision-making in dynamic environments. This paper offers a framework-based synthesis and perspective on this methodological transition. We use the paradigm shift from solver-centric optimization to data-centric learning as a unifying lens to organize and interpret a rapidly expanding literature. We first briefly revisit classical optimization-based approaches and discuss how machine learning has been integrated to improve computational efficiency and guide search while retaining solver-based feasibility and accountability. We then synthesize end-to-end learning approaches that generate scheduling solutions (or solution-generating policies) directly from data, clarifying the key design choices in solution generation and feasibility handling. Building on these organizing frameworks, we compare learning mechanisms and training signals (supervised, self-supervised, and reinforcement learning) in terms of scalability, interpretability, and generalization, and highlight the trade-offs that matter for reliable deployment. Finally, we outline an agenda along three interdependent dimensions, scalability, reliability, and universality, that together define a pathway toward adaptive, intelligent, and trustworthy scheduling systems for data-driven operations management. |
| title | Machine Learning for Scheduling: A Paradigm Shift from Solver-Centric to Data-Centric Approaches |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2512.22642 |