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

Kód výsledku v 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>

Nalezeny alternativní kódy

RIV/68407700:21730/23:00361438

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

European Journal of Operational Research

ISSN

0377-2217

e-ISSN

1872-6860

Svazek periodika

308

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

17

Strana od-do

990-1006

Kód UT WoS článku

000957499300001

EID výsledku v databázi Scopus

2-s2.0-85146477715