Effective IG heuristics for a single-machine scheduling problem with family setups and resource constraints
In this paper we investigate the problem of scheduling a set of jobs on a single-machine. The jobs are classified in families and setup times are required between the processing of two jobs of different families. Each job requires a certain amount of a common resource that is supplied through upstre...
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| Vydáno v: | Annals of mathematics and artificial intelligence Ročník 88; číslo 1-3; s. 169 - 185 |
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| Jazyk: | angličtina |
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Cham
Springer International Publishing
01.03.2020
Springer Springer Nature B.V |
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| ISSN: | 1012-2443, 1573-7470 |
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| Abstract | In this paper we investigate the problem of scheduling a set of jobs on a single-machine. The jobs are classified in families and setup times are required between the processing of two jobs of different families. Each job requires a certain amount of a common resource that is supplied through upstream processes. The total resource consumed must not exceed the resource supply up. Therefore, jobs may have to wait and the machine has to be idle due to an insufficient availability of the resource. To minimize the total tardiness, simple and effective iterated greedy (IG) heuristics are proposed. Different neighborhood operators are used in the local search phase. To choose the right neighborhood operators, we propose an adaptive selecting strategy. The heuristics are tested over an extensive computational experience on benchmark of instances from the literature and instances randomly generated in this work. Experimental results and statistical tests show that the proposed heuristics are able to obtain high-quality solutions within reasonable computational effort, and they outperform the state-of-the-art heuristic. |
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| AbstractList | In this paper we investigate the problem of scheduling a set of jobs on a single-machine. The jobs are classified in families and setup times are required between the processing of two jobs of different families. Each job requires a certain amount of a common resource that is supplied through upstream processes. The total resource consumed must not exceed the resource supply up. Therefore, jobs may have to wait and the machine has to be idle due to an insufficient availability of the resource. To minimize the total tardiness, simple and effective iterated greedy (IG) heuristics are proposed. Different neighborhood operators are used in the local search phase. To choose the right neighborhood operators, we propose an adaptive selecting strategy. The heuristics are tested over an extensive computational experience on benchmark of instances from the literature and instances randomly generated in this work. Experimental results and statistical tests show that the proposed heuristics are able to obtain high-quality solutions within reasonable computational effort, and they outperform the state-of-the-art heuristic. In this paper we investigate the problem of scheduling a set of jobs on a single-machine. The jobs are classified in families and setup times are required between the processing of two jobs of different families. Each job requires a certain amount of a common resource that is supplied through upstream processes. The total resource consumed must not exceed the resource supply up. Therefore, jobs may have to wait and the machine has to be idle due to an insufficient availability of the resource. To minimize the total tardiness, simple and effective iterated greedy (IG) heuristics are proposed. Different neighborhood operators are used in the local search phase. To choose the right neighborhood operators, we propose an adaptive selecting strategy. The heuristics are tested over an extensive computational experience on benchmark of instances from the literature and instances randomly generated in this work. Experimental results and statistical tests show that the proposed heuristics are able to obtain high-quality solutions within reasonable computational effort, and they outperform the state-of-the-art heuristic. Keywords Single machine scheduling * Family setup-times * Resource constraints * Total tardiness * Meta-heuristics. Mathematics Subject Classification (2010) 90C27 * 90B35 * 90C59 |
| Audience | Academic |
| Author | Arroyo, José Elias C. Pinheiro, Júlio C. S. N. |
| Author_xml | – sequence: 1 givenname: Júlio C. S. N. surname: Pinheiro fullname: Pinheiro, Júlio C. S. N. organization: Department of Computer Science, Universidade Federal de Viçosa – sequence: 2 givenname: José Elias C. orcidid: 0000-0003-3403-4770 surname: Arroyo fullname: Arroyo, José Elias C. email: jarroyo@dpi.ufv.br organization: Department of Computer Science, Universidade Federal de Viçosa |
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| Cites_doi | 10.1016/j.disopt.2008.04.001 10.1080/00207543.2014.883472 10.1016/j.cor.2011.12.017 10.1016/j.jpdc.2013.03.008 10.1016/j.ejor.2009.04.007 10.1016/j.cor.2013.01.018 10.1057/palgrave.jors.2602434 10.1016/j.ejor.2015.07.001 10.1007/s10951-011-0261-x 10.1016/j.cor.2012.07.004 10.1016/j.ejor.2005.12.009 10.1016/S0377-2217(99)00153-8 10.1080/00207540601009976 10.1287/opre.17.4.701 10.1504/IJCSM.2017.083755 10.1016/j.eswa.2008.08.033 10.1016/j.ejor.2006.06.061 10.1016/j.ijpe.2016.05.014 10.1016/j.ejor.2005.05.018 10.1016/j.ijpe.2004.10.020 10.1016/S0925-5273(02)00265-7 10.1016/j.ejor.2006.06.060 10.1016/j.ejor.2010.03.030 10.1016/j.cor.2005.07.020 10.1057/jors.1992.66 10.1287/moor.15.3.483 10.1016/S0167-5060(08)70742-8 10.1016/j.ejor.2010.05.036 10.1155/2016/9598041 10.1016/j.omega.2018.03.004 |
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| Keywords | Resource constraints Family setup-times 90B35 90C59 90C27 Single machine scheduling Total tardiness Meta-heuristics |
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| SubjectTerms | Artificial Intelligence Complex Systems Computer Science Heuristic Mathematics Operators Scheduling Statistical tests |
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| Title | Effective IG heuristics for a single-machine scheduling problem with family setups and resource constraints |
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