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Scheduling Jobs Families with Learning Effect on the Setup
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| Zeitschriftentitel: | Advances in Operations Research |
|---|---|
| Personen und Körperschaften: | , , |
| In: | Advances in Operations Research, 2015, 2015, S. 1-12 |
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| Sprache: | Englisch |
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Hindawi Limited
|
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Fichera, Sergio Costa, Antonio Cappadonna, Fulvio Fichera, Sergio Costa, Antonio Cappadonna, Fulvio |
|---|---|
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Fichera, Sergio Costa, Antonio Cappadonna, Fulvio |
| spellingShingle |
Fichera, Sergio Costa, Antonio Cappadonna, Fulvio Advances in Operations Research Scheduling Jobs Families with Learning Effect on the Setup Management Science and Operations Research |
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fichera, sergio |
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Fichera, Sergio Costa, Antonio Cappadonna, Fulvio 1687-9147 1687-9155 Hindawi Limited Management Science and Operations Research http://dx.doi.org/10.1155/2015/124258 <jats:p>The present paper aims to address the flow-shop sequence-dependent group scheduling problem with learning effect (FSDGSLE). The objective function to be minimized is the total completion time, that is, the makespan. The workers are required to carry out manually the set-up operations on each group to be loaded on the generic machine. The operators skills improve over time due to the learning effects; therefore the set-up time of a group under learning effect decreases depending on the order the group is worked in. In order to effectively cope with the issue at hand, a mathematical model and a hybrid metaheuristic procedure integrating features from genetic algorithms (GA) have been developed. A well-known problem benchmark risen from literature, made by two-, three- and six-machine instances, has been taken as reference for assessing performances of such approach against the two most recent algorithms presented by literature on the FSDGS issue. The obtained results, also supported by a properly developed ANOVA analysis, demonstrate the superiority of the proposed hybrid metaheuristic in tackling the FSDGSLE problem under investigation.</jats:p> Scheduling Jobs Families with Learning Effect on the Setup Advances in Operations Research |
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| title |
Scheduling Jobs Families with Learning Effect on the Setup |
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Scheduling Jobs Families with Learning Effect on the Setup |
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Scheduling Jobs Families with Learning Effect on the Setup |
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Scheduling Jobs Families with Learning Effect on the Setup |
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Scheduling Jobs Families with Learning Effect on the Setup |
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scheduling jobs families with learning effect on the setup |
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Management Science and Operations Research |
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http://dx.doi.org/10.1155/2015/124258 |
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2015 |
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1-12 |
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<jats:p>The present paper aims to address the flow-shop sequence-dependent group scheduling problem with learning effect (FSDGSLE). The objective function to be minimized is the total completion time, that is, the makespan. The workers are required to carry out manually the set-up operations on each group to be loaded on the generic machine. The operators skills improve over time due to the learning effects; therefore the set-up time of a group under learning effect decreases depending on the order the group is worked in. In order to effectively cope with the issue at hand, a mathematical model and a hybrid metaheuristic procedure integrating features from genetic algorithms (GA) have been developed. A well-known problem benchmark risen from literature, made by two-, three- and six-machine instances, has been taken as reference for assessing performances of such approach against the two most recent algorithms presented by literature on the FSDGS issue. The obtained results, also supported by a properly developed ANOVA analysis, demonstrate the superiority of the proposed hybrid metaheuristic in tackling the FSDGSLE problem under investigation.</jats:p> |
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| author | Fichera, Sergio, Costa, Antonio, Cappadonna, Fulvio |
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| description | <jats:p>The present paper aims to address the flow-shop sequence-dependent group scheduling problem with learning effect (FSDGSLE). The objective function to be minimized is the total completion time, that is, the makespan. The workers are required to carry out manually the set-up operations on each group to be loaded on the generic machine. The operators skills improve over time due to the learning effects; therefore the set-up time of a group under learning effect decreases depending on the order the group is worked in. In order to effectively cope with the issue at hand, a mathematical model and a hybrid metaheuristic procedure integrating features from genetic algorithms (GA) have been developed. A well-known problem benchmark risen from literature, made by two-, three- and six-machine instances, has been taken as reference for assessing performances of such approach against the two most recent algorithms presented by literature on the FSDGS issue. The obtained results, also supported by a properly developed ANOVA analysis, demonstrate the superiority of the proposed hybrid metaheuristic in tackling the FSDGSLE problem under investigation.</jats:p> |
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| spelling | Fichera, Sergio Costa, Antonio Cappadonna, Fulvio 1687-9147 1687-9155 Hindawi Limited Management Science and Operations Research http://dx.doi.org/10.1155/2015/124258 <jats:p>The present paper aims to address the flow-shop sequence-dependent group scheduling problem with learning effect (FSDGSLE). The objective function to be minimized is the total completion time, that is, the makespan. The workers are required to carry out manually the set-up operations on each group to be loaded on the generic machine. The operators skills improve over time due to the learning effects; therefore the set-up time of a group under learning effect decreases depending on the order the group is worked in. In order to effectively cope with the issue at hand, a mathematical model and a hybrid metaheuristic procedure integrating features from genetic algorithms (GA) have been developed. A well-known problem benchmark risen from literature, made by two-, three- and six-machine instances, has been taken as reference for assessing performances of such approach against the two most recent algorithms presented by literature on the FSDGS issue. The obtained results, also supported by a properly developed ANOVA analysis, demonstrate the superiority of the proposed hybrid metaheuristic in tackling the FSDGSLE problem under investigation.</jats:p> Scheduling Jobs Families with Learning Effect on the Setup Advances in Operations Research |
| spellingShingle | Fichera, Sergio, Costa, Antonio, Cappadonna, Fulvio, Advances in Operations Research, Scheduling Jobs Families with Learning Effect on the Setup, Management Science and Operations Research |
| title | Scheduling Jobs Families with Learning Effect on the Setup |
| title_full | Scheduling Jobs Families with Learning Effect on the Setup |
| title_fullStr | Scheduling Jobs Families with Learning Effect on the Setup |
| title_full_unstemmed | Scheduling Jobs Families with Learning Effect on the Setup |
| title_short | Scheduling Jobs Families with Learning Effect on the Setup |
| title_sort | scheduling jobs families with learning effect on the setup |
| title_unstemmed | Scheduling Jobs Families with Learning Effect on the Setup |
| topic | Management Science and Operations Research |
| url | http://dx.doi.org/10.1155/2015/124258 |