Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00361438" target="_blank" >RIV/68407700:21230/23:00361438 - isvavai.cz</a>
Alternative codes found
RIV/68407700:21730/23:00361438
Result on the web
<a href="http://hdl.handle.net/10467/113234" target="_blank" >http://hdl.handle.net/10467/113234</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ejor.2022.11.034" target="_blank" >10.1016/j.ejor.2022.11.034</a>

Result language
angličtina
Original language name
Deep learning-driven scheduling algorithm for a single machine problem minimizing the total tardiness
Original language description
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.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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