Solving a Multi-objective Job Shop Scheduling Problem with an Automatically Configured Evolutionary Algorithm

Jesús Para; Javier Del Ser; Antonio J. Nebro

doi:10.1007/978-3-031-34020-8_4

Solving a Multi-objective Job Shop Scheduling Problem with an Automatically Configured Evolutionary Algorithm

Jesús Para
, Javier Del Ser
, Antonio J. Nebro^*

^*Autor correspondiente de este trabajo

IA

University of Málaga

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

In this work we focus on optimizing a multi-objective formulation of the Job Shop Scheduling Problem (JSP) which considers the minimization of energy consumption as one of the objectives. In practice, users experts in the problem domain but with a low knowledge in metaheuristics usually take an existing algorithm with default settings to optimize problem instances but, in this context, the use of automatic parameter configuration techniques can help to find ad-hoc configurations of algorithms that effectively solve optimization problems. Our aim is to study what improvement in results can be obtained by applying an autoconfiguration approach versus using a set of well-known multi-objective evolutionary algorithms (NSGA-II, SPEA2, SMS-EMOA and MOEA/D) for different instances of the JSP, with varying dimensionality. Our experiments showcase the potential of automated algorithmic configuration for energy-efficient production scheduling, producing better balanced solutions than the multi-objective solvers considered in the study.

Idioma original	Inglés
Título de la publicación alojada	Optimization and Learning - 6th International Conference, OLA 2023, Proceedings
Editores	Bernabé Dorronsoro, Francisco Chicano, Gregoire Danoy, El-Ghazali Talbi
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	48-61
Número de páginas	14
ISBN (versión impresa)	9783031340192
DOI	https://doi.org/10.1007/978-3-031-34020-8_4
Estado	Publicada - 2023
Evento	6th International Conference on Optimization and Learning, OLA 2023 - Malaga, Espana Duración: 3 may 2023 → 5 may 2023

Serie de la publicación

Nombre	Communications in Computer and Information Science
Volumen	1824 CCIS
ISSN (versión impresa)	1865-0929
ISSN (versión digital)	1865-0937

Conferencia

Conferencia	6th International Conference on Optimization and Learning, OLA 2023
País/Territorio	Espana
Ciudad	Malaga
Período	3/05/23 → 5/05/23

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

ODS 7: Energía asequible y no contaminante

Acceder al documento

10.1007/978-3-031-34020-8_4

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Citar esto

Para, J., Del Ser, J., & Nebro, A. J. (2023). Solving a Multi-objective Job Shop Scheduling Problem with an Automatically Configured Evolutionary Algorithm. En B. Dorronsoro, F. Chicano, G. Danoy, & E.-G. Talbi (Eds.), Optimization and Learning - 6th International Conference, OLA 2023, Proceedings (pp. 48-61). (Communications in Computer and Information Science; Vol. 1824 CCIS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-34020-8_4

Para, Jesús ; Del Ser, Javier ; Nebro, Antonio J. / Solving a Multi-objective Job Shop Scheduling Problem with an Automatically Configured Evolutionary Algorithm. Optimization and Learning - 6th International Conference, OLA 2023, Proceedings. editor / Bernabé Dorronsoro ; Francisco Chicano ; Gregoire Danoy ; El-Ghazali Talbi. Springer Science and Business Media Deutschland GmbH, 2023. pp. 48-61 (Communications in Computer and Information Science).

@inproceedings{b654287748004da1a960db7fb45a1958,

title = "Solving a Multi-objective Job Shop Scheduling Problem with an Automatically Configured Evolutionary Algorithm",

abstract = "In this work we focus on optimizing a multi-objective formulation of the Job Shop Scheduling Problem (JSP) which considers the minimization of energy consumption as one of the objectives. In practice, users experts in the problem domain but with a low knowledge in metaheuristics usually take an existing algorithm with default settings to optimize problem instances but, in this context, the use of automatic parameter configuration techniques can help to find ad-hoc configurations of algorithms that effectively solve optimization problems. Our aim is to study what improvement in results can be obtained by applying an autoconfiguration approach versus using a set of well-known multi-objective evolutionary algorithms (NSGA-II, SPEA2, SMS-EMOA and MOEA/D) for different instances of the JSP, with varying dimensionality. Our experiments showcase the potential of automated algorithmic configuration for energy-efficient production scheduling, producing better balanced solutions than the multi-objective solvers considered in the study.",

keywords = "Automatic Algorithm Configuration, Job Shop Scheduling, Multi-Objective Optimization",

author = "Jes{\'u}s Para and \{Del Ser\}, Javier and Nebro, \{Antonio J.\}",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 6th International Conference on Optimization and Learning, OLA 2023 ; Conference date: 03-05-2023 Through 05-05-2023",

year = "2023",

doi = "10.1007/978-3-031-34020-8\_4",

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Para, J, Del Ser, J & Nebro, AJ 2023, Solving a Multi-objective Job Shop Scheduling Problem with an Automatically Configured Evolutionary Algorithm. En B Dorronsoro, F Chicano, G Danoy & E-G Talbi (eds.), Optimization and Learning - 6th International Conference, OLA 2023, Proceedings. Communications in Computer and Information Science, vol. 1824 CCIS, Springer Science and Business Media Deutschland GmbH, pp. 48-61, 6th International Conference on Optimization and Learning, OLA 2023, Malaga, Espana, 3/05/23. https://doi.org/10.1007/978-3-031-34020-8_4

Solving a Multi-objective Job Shop Scheduling Problem with an Automatically Configured Evolutionary Algorithm. / Para, Jesús; Del Ser, Javier; Nebro, Antonio J.
Optimization and Learning - 6th International Conference, OLA 2023, Proceedings. ed. / Bernabé Dorronsoro; Francisco Chicano; Gregoire Danoy; El-Ghazali Talbi. Springer Science and Business Media Deutschland GmbH, 2023. p. 48-61 (Communications in Computer and Information Science; Vol. 1824 CCIS).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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AU - Nebro, Antonio J.

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AB - In this work we focus on optimizing a multi-objective formulation of the Job Shop Scheduling Problem (JSP) which considers the minimization of energy consumption as one of the objectives. In practice, users experts in the problem domain but with a low knowledge in metaheuristics usually take an existing algorithm with default settings to optimize problem instances but, in this context, the use of automatic parameter configuration techniques can help to find ad-hoc configurations of algorithms that effectively solve optimization problems. Our aim is to study what improvement in results can be obtained by applying an autoconfiguration approach versus using a set of well-known multi-objective evolutionary algorithms (NSGA-II, SPEA2, SMS-EMOA and MOEA/D) for different instances of the JSP, with varying dimensionality. Our experiments showcase the potential of automated algorithmic configuration for energy-efficient production scheduling, producing better balanced solutions than the multi-objective solvers considered in the study.

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Para J, Del Ser J, Nebro AJ. Solving a Multi-objective Job Shop Scheduling Problem with an Automatically Configured Evolutionary Algorithm. En Dorronsoro B, Chicano F, Danoy G, Talbi EG, editores, Optimization and Learning - 6th International Conference, OLA 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 48-61. (Communications in Computer and Information Science). doi: 10.1007/978-3-031-34020-8_4