Multi-objective Iterated Local Search based on decomposition for job scheduling problems with machine deterioration effect
This work addresses an unrelated parallel machine scheduling problem in which the jobs cause deterioration of the machines. This factor decreases the performance of the machines, increasing the processing times of the jobs over time. We propose a mixed-integer nonlinear programming model for the pro...
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| Veröffentlicht in: | Engineering applications of artificial intelligence Jg. 112; S. 104826 |
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| Sprache: | Englisch |
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01.06.2022
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| ISSN: | 0952-1976, 1873-6769 |
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| Abstract | This work addresses an unrelated parallel machine scheduling problem in which the jobs cause deterioration of the machines. This factor decreases the performance of the machines, increasing the processing times of the jobs over time. We propose a mixed-integer nonlinear programming model for the problem that has two objectives: to minimize the maximum completion time of jobs (makespan) and to minimize the total time of delay of the jobs. In this paper, we also develop a different approach to extend Iterated Local Search (ILS) meta-heuristic to multi-objective problems. The Iterated Local Search Based on Decomposition (ILS/D) employs the decomposition strategy similar to the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), in which the ILS is used as the search engine to improve the search process within the structure of the MOEA/D. We compared the ILS/D, MOEA/D and Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithms. The results show that the ILS/D outperforms the MOEA/D and NSGA-II algorithms by a significant margin. These findings show that the decomposition strategy is beneficial not only for evolutionary algorithms, but is also an efficient way to extend the ILS to multi-objective problems.
•Multi-objective approach to job scheduling problem with machine deterioration effect.•Presentation of the multi-objective mixed-integer nonlinear programming model.•A new heuristic called Iterated Local Search Based on Decomposition (ILS/D).•The ILS/D outperforms MOEA/D and NSGA-II by a significant margin.•Decomposition is an effective way to extend ILS to multi-objective problems. |
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| AbstractList | This work addresses an unrelated parallel machine scheduling problem in which the jobs cause deterioration of the machines. This factor decreases the performance of the machines, increasing the processing times of the jobs over time. We propose a mixed-integer nonlinear programming model for the problem that has two objectives: to minimize the maximum completion time of jobs (makespan) and to minimize the total time of delay of the jobs. In this paper, we also develop a different approach to extend Iterated Local Search (ILS) meta-heuristic to multi-objective problems. The Iterated Local Search Based on Decomposition (ILS/D) employs the decomposition strategy similar to the Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), in which the ILS is used as the search engine to improve the search process within the structure of the MOEA/D. We compared the ILS/D, MOEA/D and Non-dominated Sorting Genetic Algorithm II (NSGA-II) algorithms. The results show that the ILS/D outperforms the MOEA/D and NSGA-II algorithms by a significant margin. These findings show that the decomposition strategy is beneficial not only for evolutionary algorithms, but is also an efficient way to extend the ILS to multi-objective problems.
•Multi-objective approach to job scheduling problem with machine deterioration effect.•Presentation of the multi-objective mixed-integer nonlinear programming model.•A new heuristic called Iterated Local Search Based on Decomposition (ILS/D).•The ILS/D outperforms MOEA/D and NSGA-II by a significant margin.•Decomposition is an effective way to extend ILS to multi-objective problems. |
| ArticleNumber | 104826 |
| Author | Santos, Vívian Ludimila Aguiar Guimarães, Frederico Gadelha Weiss-Cohen, Miri Carvalho, Thales Francisco Mota de Assis, Luciana Pereira |
| Author_xml | – sequence: 1 givenname: Vívian Ludimila Aguiar orcidid: 0000-0001-7282-3493 surname: Santos fullname: Santos, Vívian Ludimila Aguiar email: vivian.santos@ifnmg.edu.br organization: Instituto Federal do Norte de Minas Gerais, Rua Humberto Mallard 1355, Pirapora 39274-140, MG, Brazil – sequence: 2 givenname: Thales Francisco Mota orcidid: 0000-0001-8107-7998 surname: Carvalho fullname: Carvalho, Thales Francisco Mota email: thales.mota@ufvjm.edu.br organization: Graduate Program in Electrical Engineering, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte, 31270-901, MG, Brazil – sequence: 3 givenname: Luciana Pereira orcidid: 0000-0002-7891-7172 surname: de Assis fullname: de Assis, Luciana Pereira email: lpassis@ufvjm.edu.br organization: Computer Department, Universidade Federal dos Vales do Jequitinhonha e Mucuri, Rodovia MGT 367 – Km 583, Diamantina, 39100-000, MG, Brazil – sequence: 4 givenname: Miri orcidid: 0000-0001-5250-1016 surname: Weiss-Cohen fullname: Weiss-Cohen, Miri email: miri@braude.ac.il organization: Department of Software Engineering, Braude College of Engineering, Karmiel, 2161002, Israel – sequence: 5 givenname: Frederico Gadelha orcidid: 0000-0001-9238-8839 surname: Guimarães fullname: Guimarães, Frederico Gadelha email: fredericoguimaraes@ufmg.br organization: Machine Intelligence and Data Science (MINDS) Laboratory, School of Engineering, Universidade Federal de Minas Gerais, Av. Antônio Carlos 6627, Belo Horizonte, 31270-901, MG, Brazil |
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| Keywords | Unrelated parallel machine Decomposition and aggregation Multi-objective optimization Deterioration effect Job scheduling Meta-heuristics |
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