تقرير
Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness
| العنوان: | Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness |
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| المؤلفون: | Bouška, Michal, Šůcha, Přemysl, Novák, Antonín, Hanzálek, Zdeněk |
| المصدر: | European Journal of Operational Research, Volume 308, Issue 3, 1 August 2023, Pages 990-1006 |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science Mathematics |
| مصطلحات موضوعية: | Mathematics - Optimization and Control, Computer Science - Artificial Intelligence, Computer Science - Machine Learning |
| الوصف: | In this paper, we investigate the use of the deep learning method for solving a well-known NP-hard single machine scheduling problem with the objective of minimizing the total tardiness. We propose a deep neural network that acts as a polynomial-time estimator of the criterion value used in a single-pass scheduling algorithm based on Lawler's decomposition and symmetric decomposition proposed by Della Croce et al. Essentially, the neural network guides the algorithm by estimating the best splitting of the problem into subproblems. The paper also describes a new method for generating the training data set, which speeds up the training dataset generation and reduces the average optimality gap of solutions. The experimental results show that our machine learning-driven approach can efficiently generalize information from the training phase to significantly larger instances. Even though the instances used in the training phase have from 75 to 100 jobs, the average optimality gap on instances with up to 800 jobs is 0.26%, which is almost five times less than the gap of the state-of-the-art heuristic. |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1016/j.ejor.2022.11.034 |
| URL الوصول: | http://arxiv.org/abs/2402.14847 |
| رقم الأكسشن: | edsarx.2402.14847 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1016/j.ejor.2022.11.034 |
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