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Iterated Greedy methods for the distributed permutation flowshop scheduling problem

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Iterated Greedy methods for the distributed permutation flowshop scheduling problem

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dc.contributor.author Ruiz García, Rubén es_ES
dc.contributor.author Pan, Quan-Ke es_ES
dc.contributor.author Naderi, Bahman es_ES
dc.date.accessioned 2021-01-26T04:32:05Z
dc.date.available 2021-01-26T04:32:05Z
dc.date.issued 2019-03 es_ES
dc.identifier.issn 0305-0483 es_ES
dc.identifier.uri http://hdl.handle.net/10251/159840
dc.description.abstract [EN] Large manufacturing firms operate more than one production center. As a result, in relation to scheduling problems, which factory manufactures which product is an important consideration. In this paper we study an extension of the well known permutation flowshop scheduling problem in which there is a set of identical factories, each one with a flowshop structure. The objective is to minimize the maximum completion time or makespan among all factories. The resulting problem is known as the distributed permutation flowshop and has attracted considerable interest over the last few years. Contrary to the recent trend in the scheduling literature, where complex nature-inspired or metaphor-based methods are often proposed, we present simple Iterated Greedy algorithms that have performed well in related problems. Improved initialization, construction and destruction procedures, along with a local search with a strong intensification are proposed. The result is a very effective algorithm with little problem-specific knowledge that is shown to provide demonstrably better solutions in a comprehensive and thorough computational and statistical campaign. es_ES
dc.description.sponsorship Ruben Ruiz is partially supported by the Spanish Ministry of Economy and Competitiveness, under the project "SCHEYARD - Optimization of Scheduling Problems in Container Yards" (No. DPI2015-65895-R) financed by FEDER funds. Quan-Ke Pan is supported by the National Natural Science Foundation of China (Grant No. 51575212). es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation.ispartof Omega es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Distributed flowshop es_ES
dc.subject Makespan es_ES
dc.subject Metaheuristics es_ES
dc.subject Iterated Greedy es_ES
dc.subject.classification ESTADISTICA E INVESTIGACION OPERATIVA es_ES
dc.title Iterated Greedy methods for the distributed permutation flowshop scheduling problem es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.omega.2018.03.004 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NSFC//51575212/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-094940-B-I00/ES/OPTIMIZACION DE OPERACIONES EN TERMINALES PORTUARIAS/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Estadística e Investigación Operativa Aplicadas y Calidad - Departament d'Estadística i Investigació Operativa Aplicades i Qualitat es_ES
dc.description.bibliographicCitation Ruiz García, R.; Pan, Q.; Naderi, B. (2019). Iterated Greedy methods for the distributed permutation flowshop scheduling problem. Omega. 83:213-222. https://doi.org/10.1016/j.omega.2018.03.004 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.omega.2018.03.004 es_ES
dc.description.upvformatpinicio 213 es_ES
dc.description.upvformatpfin 222 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 83 es_ES
dc.relation.pasarela S\405880 es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder National Natural Science Foundation of China es_ES
dc.description.references Behnamian, J., & Fatemi Ghomi, S. M. T. (2014). A survey of multi-factory scheduling. Journal of Intelligent Manufacturing, 27(1), 231-249. doi:10.1007/s10845-014-0890-y es_ES
dc.description.references Chan, H. K., & Chung, S. H. (2013). Optimisation approaches for distributed scheduling problems. International Journal of Production Research, 51(9), 2571-2577. doi:10.1080/00207543.2012.755345 es_ES
dc.description.references Deng, J., & Wang, L. (2017). A competitive memetic algorithm for multi-objective distributed permutation flow shop scheduling problem. Swarm and Evolutionary Computation, 32, 121-131. doi:10.1016/j.swevo.2016.06.002 es_ES
dc.description.references Fernandez-Viagas, V., & Framinan, J. M. (2014). A bounded-search iterated greedy algorithm for the distributed permutation flowshop scheduling problem. International Journal of Production Research, 53(4), 1111-1123. doi:10.1080/00207543.2014.948578 es_ES
dc.description.references Fernandez-Viagas, V., Ruiz, R., & Framinan, J. M. (2017). A new vision of approximate methods for the permutation flowshop to minimise makespan: State-of-the-art and computational evaluation. European Journal of Operational Research, 257(3), 707-721. doi:10.1016/j.ejor.2016.09.055 es_ES
dc.description.references Framinan, J. M., Gupta, J. N. D., & Leisten, R. (2004). A review and classification of heuristics for permutation flow-shop scheduling with makespan objective. Journal of the Operational Research Society, 55(12), 1243-1255. doi:10.1057/palgrave.jors.2601784 es_ES
dc.description.references Gao, J., & Chen, R. (2011). A hybrid genetic algorithm for the distributed permutation flowshop scheduling problem. International Journal of Computational Intelligence Systems, 4(4), 497-508. doi:10.1080/18756891.2011.9727808 es_ES
dc.description.references Gao, J., Chen, R., & Deng, W. (2013). An efficient tabu search algorithm for the distributed permutation flowshop scheduling problem. International Journal of Production Research, 51(3), 641-651. doi:10.1080/00207543.2011.644819 es_ES
dc.description.references Garey, M. R., Johnson, D. S., & Sethi, R. (1976). The Complexity of Flowshop and Jobshop Scheduling. Mathematics of Operations Research, 1(2), 117-129. doi:10.1287/moor.1.2.117 es_ES
dc.description.references Graham, R. L., Lawler, E. L., Lenstra, J. K., & Kan, A. H. G. R. (1979). Optimization and Approximation in Deterministic Sequencing and Scheduling: a Survey. Annals of Discrete Mathematics, 287-326. doi:10.1016/s0167-5060(08)70356-x es_ES
dc.description.references Gupta, J. N. D., & Stafford, E. F. (2006). Flowshop scheduling research after five decades. European Journal of Operational Research, 169(3), 699-711. doi:10.1016/j.ejor.2005.02.001 es_ES
dc.description.references Hatami, S., Ruiz, R., & Andrés-Romano, C. (2013). The Distributed Assembly Permutation Flowshop Scheduling Problem. International Journal of Production Research, 51(17), 5292-5308. doi:10.1080/00207543.2013.807955 es_ES
dc.description.references Hatami, S., Ruiz, R., & Andrés-Romano, C. (2015). Heuristics and metaheuristics for the distributed assembly permutation flowshop scheduling problem with sequence dependent setup times. International Journal of Production Economics, 169, 76-88. doi:10.1016/j.ijpe.2015.07.027 es_ES
dc.description.references Reza Hejazi *, S., & Saghafian, S. (2005). Flowshop-scheduling problems with makespan criterion: a review. International Journal of Production Research, 43(14), 2895-2929. doi:10.1080/0020754050056417 es_ES
dc.description.references Johnson, S. M. (1954). Optimal two- and three-stage production schedules with setup times included. Naval Research Logistics Quarterly, 1(1), 61-68. doi:10.1002/nav.3800010110 es_ES
dc.description.references Kendall, G., Bai, R., Błazewicz, J., De Causmaecker, P., Gendreau, M., John, R., … Yee, A. (2016). Good Laboratory Practice for optimization research. Journal of the Operational Research Society, 67(4), 676-689. doi:10.1057/jors.2015.77 es_ES
dc.description.references Lin, S.-W., Ying, K.-C., & Huang, C.-Y. (2013). Minimising makespan in distributed permutation flowshops using a modified iterated greedy algorithm. International Journal of Production Research, 51(16), 5029-5038. doi:10.1080/00207543.2013.790571 es_ES
dc.description.references MACCARTHY, B. L., & LIU, J. (1993). Addressing the gap in scheduling research: a review of optimization and heuristic methods in production scheduling. International Journal of Production Research, 31(1), 59-79. doi:10.1080/00207549308956713 es_ES
dc.description.references MCKAY, K., PINEDO, M., & WEBSTER, S. (2009). PRACTICE-FOCUSED RESEARCH ISSUES FOR SCHEDULING SYSTEMS*. Production and Operations Management, 11(2), 249-258. doi:10.1111/j.1937-5956.2002.tb00494.x es_ES
dc.description.references McKay, K. N., Safayeni, F. R., & Buzacott, J. A. (1988). Job-Shop Scheduling Theory: What Is Relevant? Interfaces, 18(4), 84-90. doi:10.1287/inte.18.4.84 es_ES
dc.description.references Mladenović, N., & Hansen, P. (1997). Variable neighborhood search. Computers & Operations Research, 24(11), 1097-1100. doi:10.1016/s0305-0548(97)00031-2 es_ES
dc.description.references Naderi, B., & Ruiz, R. (2010). The distributed permutation flowshop scheduling problem. Computers & Operations Research, 37(4), 754-768. doi:10.1016/j.cor.2009.06.019 es_ES
dc.description.references Naderi, B., & Ruiz, R. (2014). A scatter search algorithm for the distributed permutation flowshop scheduling problem. European Journal of Operational Research, 239(2), 323-334. doi:10.1016/j.ejor.2014.05.024 es_ES
dc.description.references Nawaz, M., Enscore, E. E., & Ham, I. (1983). A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem. Omega, 11(1), 91-95. doi:10.1016/0305-0483(83)90088-9 es_ES
dc.description.references Pan, Q.-K., & Ruiz, R. (2014). An effective iterated greedy algorithm for the mixed no-idle permutation flowshop scheduling problem. Omega, 44, 41-50. doi:10.1016/j.omega.2013.10.002 es_ES
dc.description.references Pan, Q.-K., Ruiz, R., & Alfaro-Fernández, P. (2017). Iterated search methods for earliness and tardiness minimization in hybrid flowshops with due windows. Computers & Operations Research, 80, 50-60. doi:10.1016/j.cor.2016.11.022 es_ES
dc.description.references Rad, S. F., Ruiz, R., & Boroojerdian, N. (2009). New high performing heuristics for minimizing makespan in permutation flowshops. Omega, 37(2), 331-345. doi:10.1016/j.omega.2007.02.002 es_ES
dc.description.references Reisman, A., Kumar, A., & Motwani, J. (1997). Flowshop scheduling/sequencing research: a statistical review of the literature, 1952-1994. IEEE Transactions on Engineering Management, 44(3), 316-329. doi:10.1109/17.618173 es_ES
dc.description.references Ribas, I., Companys, R., & Tort-Martorell, X. (2017). Efficient heuristics for the parallel blocking flow shop scheduling problem. Expert Systems with Applications, 74, 41-54. doi:10.1016/j.eswa.2017.01.006 es_ES
dc.description.references Rifai, A. P., Nguyen, H.-T., & Dawal, S. Z. M. (2016). Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling. Applied Soft Computing, 40, 42-57. doi:10.1016/j.asoc.2015.11.034 es_ES
dc.description.references Ruiz, R., & Maroto, C. (2005). A comprehensive review and evaluation of permutation flowshop heuristics. European Journal of Operational Research, 165(2), 479-494. doi:10.1016/j.ejor.2004.04.017 es_ES
dc.description.references Ruiz, R., & Stützle, T. (2007). A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem. European Journal of Operational Research, 177(3), 2033-2049. doi:10.1016/j.ejor.2005.12.009 es_ES
dc.description.references Sörensen, K. (2013). Metaheuristics-the metaphor exposed. International Transactions in Operational Research, 22(1), 3-18. doi:10.1111/itor.12001 es_ES
dc.description.references Taillard, E. (1990). Some efficient heuristic methods for the flow shop sequencing problem. European Journal of Operational Research, 47(1), 65-74. doi:10.1016/0377-2217(90)90090-x es_ES
dc.description.references Taillard, E. (1993). Benchmarks for basic scheduling problems. European Journal of Operational Research, 64(2), 278-285. doi:10.1016/0377-2217(93)90182-m es_ES
dc.description.references Urlings, T., Ruiz, R., & Stützle, T. (2010). Shifting representation search for hybrid flexible flowline problems. European Journal of Operational Research, 207(2), 1086-1095. doi:10.1016/j.ejor.2010.05.041 es_ES
dc.description.references Wang, K., Huang, Y., & Qin, H. (2016). A fuzzy logic-based hybrid estimation of distribution algorithm for distributed permutation flowshop scheduling problems under machine breakdown. Journal of the Operational Research Society, 67(1), 68-82. doi:10.1057/jors.2015.50 es_ES
dc.description.references Wang, S., Wang, L., Liu, M., & Xu, Y. (2013). An effective estimation of distribution algorithm for solving the distributed permutation flow-shop scheduling problem. International Journal of Production Economics, 145(1), 387-396. doi:10.1016/j.ijpe.2013.05.004 es_ES
dc.description.references Xu, Y., Wang, L., Wang, S., & Liu, M. (2013). An effective hybrid immune algorithm for solving the distributed permutation flow-shop scheduling problem. Engineering Optimization, 46(9), 1269-1283. doi:10.1080/0305215x.2013.827673 es_ES


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