dc.creatorToncovich, Adrián Andrés
dc.creatorRossit, Daniel Alejandro
dc.creatorFrutos, Mariano
dc.creatorRossit, Diego Gabriel
dc.date.accessioned2020-08-26T16:43:20Z
dc.date.accessioned2022-10-15T02:38:24Z
dc.date.available2020-08-26T16:43:20Z
dc.date.available2022-10-15T02:38:24Z
dc.date.created2020-08-26T16:43:20Z
dc.date.issued2019-01
dc.identifierToncovich, Adrián Andrés; Rossit, Daniel Alejandro; Frutos, Mariano; Rossit, Diego Gabriel; Solving a multi-objective manufacturing cell scheduling problem with the consideration of warehouses using a simulated annealing based procedure; Growing Science; International Journal of Industrial Engineering Computations; 10; 1; 1-2019; 1-16
dc.identifier1923-2926
dc.identifierhttp://hdl.handle.net/11336/112474
dc.identifier1923-2934
dc.identifierCONICET Digital
dc.identifierCONICET
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/4335987
dc.description.abstractThe competition manufacturing companies face has driven the development of novel and efficient methods that enhance the decision making process. In this work, a specific flow shop scheduling problem of practical interest in the industry is presented and formalized using a mathematical programming model. The problem considers a manufacturing system arranged as a work cell that takes into account the transport operations of raw material and final products between the manufacturing cell and warehouses. For solving this problem, we present a multiobjective metaheuristic strategy based on simulated annealing, the Pareto Archived Simulated Annealing (PASA). We tested this strategy on two kinds of benchmark problem sets proposed by the authors. The first group is composed by small-sized problems. On these tests, PASA was able to obtain optimal or near-optimal solutions in significantly short computing times. In order to complete the analysis, we compared these results to the exact Pareto front of the instances obtained with augmented ε-constraint method. Then, we also tested the algorithm in a set of larger problems to evaluate its performance in more extensive search spaces. We performed this assessment through an analysis of the hypervolume metric. Both sets of tests showed the competitiveness of the Pareto Archived Simulated Annealing to efficiently solve this problem and obtain good quality solutions while using reasonable computational resources.
dc.languageeng
dc.publisherGrowing Science
dc.relationinfo:eu-repo/semantics/altIdentifier/url/http://growingscience.com/beta/ijiec/2830-solving-a-multi-objective-manufacturing-cell-scheduling-problem-with-the-consideration-of-warehouses-using-a-simulated-annealing-based-procedure.html
dc.relationinfo:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.5267/j.ijiec.2018.6.001
dc.rightshttps://creativecommons.org/licenses/by/2.5/ar/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectANNEALING
dc.subjectFLOW-SHOP
dc.subjectMULTI-OBJECTIVE OPTIMIZATION
dc.subjectPARETO ARCHIVED SIMULATED
dc.subjectPRODUCTION SCHEDULING
dc.subjectWAREHOUSES
dc.titleSolving a multi-objective manufacturing cell scheduling problem with the consideration of warehouses using a simulated annealing based procedure
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:ar-repo/semantics/artículo
dc.typeinfo:eu-repo/semantics/publishedVersion


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