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^*

^*Corresponding author for this work

AI

University of Málaga

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Optimization and Learning - 6th International Conference, OLA 2023, Proceedings
Editors	Bernabé Dorronsoro, Francisco Chicano, Gregoire Danoy, El-Ghazali Talbi
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	48-61
Number of pages	14
ISBN (Print)	9783031340192
DOIs	https://doi.org/10.1007/978-3-031-34020-8_4
Publication status	Published - 2023
Event	6th International Conference on Optimization and Learning, OLA 2023 - Malaga, Spain Duration: 3 May 2023 → 5 May 2023

Publication series

Name	Communications in Computer and Information Science
Volume	1824 CCIS
ISSN (Print)	1865-0929
ISSN (Electronic)	1865-0937

Conference

Conference	6th International Conference on Optimization and Learning, OLA 2023
Country/Territory	Spain
City	Malaga
Period	3/05/23 → 5/05/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Automatic Algorithm Configuration
Job Shop Scheduling
Multi-Objective Optimization

Access to Document

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

Cite this

Para, J., Del Ser, J., & Nebro, A. J. (2023). Solving a Multi-objective Job Shop Scheduling Problem with an Automatically Configured Evolutionary Algorithm. In 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,

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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.\}",

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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. in 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, Spain, 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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. In Dorronsoro B, Chicano F, Danoy G, Talbi EG, editors, 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