Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem
Abstract The aim of this paper is to study multi-objective flexible job shop scheduling problem (MOFJSP). Flexible job shop scheduling problem is a modified version of job shop scheduling problem (JSP) in which an operation is allowed to be processed by any machine from a given set of capable machin...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Rahmati, Seyed Habib A. [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
Multiobjective evolutionary algorithm |
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| Anmerkung: |
© Springer-Verlag London Limited 2012 |
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| Übergeordnetes Werk: |
Enthalten in: The international journal of advanced manufacturing technology - Springer-Verlag, 1985, 64(2012), 5-8 vom: 23. März, Seite 915-932 |
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| Übergeordnetes Werk: |
volume:64 ; year:2012 ; number:5-8 ; day:23 ; month:03 ; pages:915-932 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00170-012-4051-1 |
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| Katalog-ID: |
OLC2026044961 |
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| 520 | |a Abstract The aim of this paper is to study multi-objective flexible job shop scheduling problem (MOFJSP). Flexible job shop scheduling problem is a modified version of job shop scheduling problem (JSP) in which an operation is allowed to be processed by any machine from a given set of capable machines. The objectives that are considered in this study are makespan, critical machine work load, and total work load of machines. In the literature of the MOFJSP, since this problem is known as an NP-hard problem, most of the studies have developed metaheuristic algorithms to solve it. Most of them have integrated their objective functions and used an integrated single-objective metaheuristic algorithm though. In this study, two new version of multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm are adapted for MOFJSP. These algorithms use new multi-objective Pareto-based modules instead of multi-criteria concepts to guide their process. Another contribution of this paper is introducing of famous metrics of the multi-objective evaluation to literature of the MOFJSP. A new measure is also proposed. Finally, through using numerous test problems, calculating a number of measures, performing different statistical tests, and plotting different types of figures, it is shown that proposed algorithms are at least as good as literature’s algorithm. | ||
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10.1007/s00170-012-4051-1 doi (DE-627)OLC2026044961 (DE-He213)s00170-012-4051-1-p DE-627 ger DE-627 rakwb eng 670 VZ Rahmati, Seyed Habib A. verfasserin aut Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2012 Abstract The aim of this paper is to study multi-objective flexible job shop scheduling problem (MOFJSP). Flexible job shop scheduling problem is a modified version of job shop scheduling problem (JSP) in which an operation is allowed to be processed by any machine from a given set of capable machines. The objectives that are considered in this study are makespan, critical machine work load, and total work load of machines. In the literature of the MOFJSP, since this problem is known as an NP-hard problem, most of the studies have developed metaheuristic algorithms to solve it. Most of them have integrated their objective functions and used an integrated single-objective metaheuristic algorithm though. In this study, two new version of multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm are adapted for MOFJSP. These algorithms use new multi-objective Pareto-based modules instead of multi-criteria concepts to guide their process. Another contribution of this paper is introducing of famous metrics of the multi-objective evaluation to literature of the MOFJSP. A new measure is also proposed. Finally, through using numerous test problems, calculating a number of measures, performing different statistical tests, and plotting different types of figures, it is shown that proposed algorithms are at least as good as literature’s algorithm. Scheduling Flexible job shop Multiobjective evolutionary algorithm Non-dominated sorting genetic algorithm Non-dominated ranking genetic algorithm Zandieh, M. aut Yazdani, M. aut Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 64(2012), 5-8 vom: 23. März, Seite 915-932 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:64 year:2012 number:5-8 day:23 month:03 pages:915-932 https://doi.org/10.1007/s00170-012-4051-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_70 GBV_ILN_150 GBV_ILN_2018 GBV_ILN_2333 GBV_ILN_4046 AR 64 2012 5-8 23 03 915-932 |
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10.1007/s00170-012-4051-1 doi (DE-627)OLC2026044961 (DE-He213)s00170-012-4051-1-p DE-627 ger DE-627 rakwb eng 670 VZ Rahmati, Seyed Habib A. verfasserin aut Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2012 Abstract The aim of this paper is to study multi-objective flexible job shop scheduling problem (MOFJSP). Flexible job shop scheduling problem is a modified version of job shop scheduling problem (JSP) in which an operation is allowed to be processed by any machine from a given set of capable machines. The objectives that are considered in this study are makespan, critical machine work load, and total work load of machines. In the literature of the MOFJSP, since this problem is known as an NP-hard problem, most of the studies have developed metaheuristic algorithms to solve it. Most of them have integrated their objective functions and used an integrated single-objective metaheuristic algorithm though. In this study, two new version of multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm are adapted for MOFJSP. These algorithms use new multi-objective Pareto-based modules instead of multi-criteria concepts to guide their process. Another contribution of this paper is introducing of famous metrics of the multi-objective evaluation to literature of the MOFJSP. A new measure is also proposed. Finally, through using numerous test problems, calculating a number of measures, performing different statistical tests, and plotting different types of figures, it is shown that proposed algorithms are at least as good as literature’s algorithm. Scheduling Flexible job shop Multiobjective evolutionary algorithm Non-dominated sorting genetic algorithm Non-dominated ranking genetic algorithm Zandieh, M. aut Yazdani, M. aut Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 64(2012), 5-8 vom: 23. März, Seite 915-932 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:64 year:2012 number:5-8 day:23 month:03 pages:915-932 https://doi.org/10.1007/s00170-012-4051-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_70 GBV_ILN_150 GBV_ILN_2018 GBV_ILN_2333 GBV_ILN_4046 AR 64 2012 5-8 23 03 915-932 |
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10.1007/s00170-012-4051-1 doi (DE-627)OLC2026044961 (DE-He213)s00170-012-4051-1-p DE-627 ger DE-627 rakwb eng 670 VZ Rahmati, Seyed Habib A. verfasserin aut Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2012 Abstract The aim of this paper is to study multi-objective flexible job shop scheduling problem (MOFJSP). Flexible job shop scheduling problem is a modified version of job shop scheduling problem (JSP) in which an operation is allowed to be processed by any machine from a given set of capable machines. The objectives that are considered in this study are makespan, critical machine work load, and total work load of machines. In the literature of the MOFJSP, since this problem is known as an NP-hard problem, most of the studies have developed metaheuristic algorithms to solve it. Most of them have integrated their objective functions and used an integrated single-objective metaheuristic algorithm though. In this study, two new version of multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm are adapted for MOFJSP. These algorithms use new multi-objective Pareto-based modules instead of multi-criteria concepts to guide their process. Another contribution of this paper is introducing of famous metrics of the multi-objective evaluation to literature of the MOFJSP. A new measure is also proposed. Finally, through using numerous test problems, calculating a number of measures, performing different statistical tests, and plotting different types of figures, it is shown that proposed algorithms are at least as good as literature’s algorithm. Scheduling Flexible job shop Multiobjective evolutionary algorithm Non-dominated sorting genetic algorithm Non-dominated ranking genetic algorithm Zandieh, M. aut Yazdani, M. aut Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 64(2012), 5-8 vom: 23. März, Seite 915-932 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:64 year:2012 number:5-8 day:23 month:03 pages:915-932 https://doi.org/10.1007/s00170-012-4051-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_70 GBV_ILN_150 GBV_ILN_2018 GBV_ILN_2333 GBV_ILN_4046 AR 64 2012 5-8 23 03 915-932 |
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10.1007/s00170-012-4051-1 doi (DE-627)OLC2026044961 (DE-He213)s00170-012-4051-1-p DE-627 ger DE-627 rakwb eng 670 VZ Rahmati, Seyed Habib A. verfasserin aut Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2012 Abstract The aim of this paper is to study multi-objective flexible job shop scheduling problem (MOFJSP). Flexible job shop scheduling problem is a modified version of job shop scheduling problem (JSP) in which an operation is allowed to be processed by any machine from a given set of capable machines. The objectives that are considered in this study are makespan, critical machine work load, and total work load of machines. In the literature of the MOFJSP, since this problem is known as an NP-hard problem, most of the studies have developed metaheuristic algorithms to solve it. Most of them have integrated their objective functions and used an integrated single-objective metaheuristic algorithm though. In this study, two new version of multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm are adapted for MOFJSP. These algorithms use new multi-objective Pareto-based modules instead of multi-criteria concepts to guide their process. Another contribution of this paper is introducing of famous metrics of the multi-objective evaluation to literature of the MOFJSP. A new measure is also proposed. Finally, through using numerous test problems, calculating a number of measures, performing different statistical tests, and plotting different types of figures, it is shown that proposed algorithms are at least as good as literature’s algorithm. Scheduling Flexible job shop Multiobjective evolutionary algorithm Non-dominated sorting genetic algorithm Non-dominated ranking genetic algorithm Zandieh, M. aut Yazdani, M. aut Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 64(2012), 5-8 vom: 23. März, Seite 915-932 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:64 year:2012 number:5-8 day:23 month:03 pages:915-932 https://doi.org/10.1007/s00170-012-4051-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_70 GBV_ILN_150 GBV_ILN_2018 GBV_ILN_2333 GBV_ILN_4046 AR 64 2012 5-8 23 03 915-932 |
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10.1007/s00170-012-4051-1 doi (DE-627)OLC2026044961 (DE-He213)s00170-012-4051-1-p DE-627 ger DE-627 rakwb eng 670 VZ Rahmati, Seyed Habib A. verfasserin aut Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem 2012 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Limited 2012 Abstract The aim of this paper is to study multi-objective flexible job shop scheduling problem (MOFJSP). Flexible job shop scheduling problem is a modified version of job shop scheduling problem (JSP) in which an operation is allowed to be processed by any machine from a given set of capable machines. The objectives that are considered in this study are makespan, critical machine work load, and total work load of machines. In the literature of the MOFJSP, since this problem is known as an NP-hard problem, most of the studies have developed metaheuristic algorithms to solve it. Most of them have integrated their objective functions and used an integrated single-objective metaheuristic algorithm though. In this study, two new version of multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm are adapted for MOFJSP. These algorithms use new multi-objective Pareto-based modules instead of multi-criteria concepts to guide their process. Another contribution of this paper is introducing of famous metrics of the multi-objective evaluation to literature of the MOFJSP. A new measure is also proposed. Finally, through using numerous test problems, calculating a number of measures, performing different statistical tests, and plotting different types of figures, it is shown that proposed algorithms are at least as good as literature’s algorithm. Scheduling Flexible job shop Multiobjective evolutionary algorithm Non-dominated sorting genetic algorithm Non-dominated ranking genetic algorithm Zandieh, M. aut Yazdani, M. aut Enthalten in The international journal of advanced manufacturing technology Springer-Verlag, 1985 64(2012), 5-8 vom: 23. März, Seite 915-932 (DE-627)129185299 (DE-600)52651-4 (DE-576)014456192 0268-3768 nnns volume:64 year:2012 number:5-8 day:23 month:03 pages:915-932 https://doi.org/10.1007/s00170-012-4051-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_20 GBV_ILN_70 GBV_ILN_150 GBV_ILN_2018 GBV_ILN_2333 GBV_ILN_4046 AR 64 2012 5-8 23 03 915-932 |
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Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem |
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Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem |
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Rahmati, Seyed Habib A. |
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The international journal of advanced manufacturing technology |
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Rahmati, Seyed Habib A. Zandieh, M. Yazdani, M. |
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developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem |
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Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem |
| abstract |
Abstract The aim of this paper is to study multi-objective flexible job shop scheduling problem (MOFJSP). Flexible job shop scheduling problem is a modified version of job shop scheduling problem (JSP) in which an operation is allowed to be processed by any machine from a given set of capable machines. The objectives that are considered in this study are makespan, critical machine work load, and total work load of machines. In the literature of the MOFJSP, since this problem is known as an NP-hard problem, most of the studies have developed metaheuristic algorithms to solve it. Most of them have integrated their objective functions and used an integrated single-objective metaheuristic algorithm though. In this study, two new version of multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm are adapted for MOFJSP. These algorithms use new multi-objective Pareto-based modules instead of multi-criteria concepts to guide their process. Another contribution of this paper is introducing of famous metrics of the multi-objective evaluation to literature of the MOFJSP. A new measure is also proposed. Finally, through using numerous test problems, calculating a number of measures, performing different statistical tests, and plotting different types of figures, it is shown that proposed algorithms are at least as good as literature’s algorithm. © Springer-Verlag London Limited 2012 |
| abstractGer |
Abstract The aim of this paper is to study multi-objective flexible job shop scheduling problem (MOFJSP). Flexible job shop scheduling problem is a modified version of job shop scheduling problem (JSP) in which an operation is allowed to be processed by any machine from a given set of capable machines. The objectives that are considered in this study are makespan, critical machine work load, and total work load of machines. In the literature of the MOFJSP, since this problem is known as an NP-hard problem, most of the studies have developed metaheuristic algorithms to solve it. Most of them have integrated their objective functions and used an integrated single-objective metaheuristic algorithm though. In this study, two new version of multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm are adapted for MOFJSP. These algorithms use new multi-objective Pareto-based modules instead of multi-criteria concepts to guide their process. Another contribution of this paper is introducing of famous metrics of the multi-objective evaluation to literature of the MOFJSP. A new measure is also proposed. Finally, through using numerous test problems, calculating a number of measures, performing different statistical tests, and plotting different types of figures, it is shown that proposed algorithms are at least as good as literature’s algorithm. © Springer-Verlag London Limited 2012 |
| abstract_unstemmed |
Abstract The aim of this paper is to study multi-objective flexible job shop scheduling problem (MOFJSP). Flexible job shop scheduling problem is a modified version of job shop scheduling problem (JSP) in which an operation is allowed to be processed by any machine from a given set of capable machines. The objectives that are considered in this study are makespan, critical machine work load, and total work load of machines. In the literature of the MOFJSP, since this problem is known as an NP-hard problem, most of the studies have developed metaheuristic algorithms to solve it. Most of them have integrated their objective functions and used an integrated single-objective metaheuristic algorithm though. In this study, two new version of multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm are adapted for MOFJSP. These algorithms use new multi-objective Pareto-based modules instead of multi-criteria concepts to guide their process. Another contribution of this paper is introducing of famous metrics of the multi-objective evaluation to literature of the MOFJSP. A new measure is also proposed. Finally, through using numerous test problems, calculating a number of measures, performing different statistical tests, and plotting different types of figures, it is shown that proposed algorithms are at least as good as literature’s algorithm. © Springer-Verlag London Limited 2012 |
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Developing two multi-objective evolutionary algorithms for the multi-objective flexible job shop scheduling problem |
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