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dc.contributor.author
Rossit, Daniel Alejandro
dc.contributor.author
Toncovich, Adrián Andrés
dc.contributor.author
Rossit, Diego Gabriel
dc.contributor.author
Nesmachnow, Sergio
dc.date.available
2021-03-09T21:16:02Z
dc.date.issued
2020-10
dc.identifier.citation
Rossit, Daniel Alejandro; Toncovich, Adrián Andrés; Rossit, Diego Gabriel; Nesmachnow, Sergio; Solving a flow shop scheduling problem with missing operations in an Industry 4.0 production environment; Growing Science; Journal of Project Management; 6; 10-2020; 33-44
dc.identifier.issn
2371-8366
dc.identifier.uri
http://hdl.handle.net/11336/127869
dc.description.abstract
Industry 4.0 is a modern approach that aims at enhancing the connectivity between the different stages of the production process and the requirements of consumers. This paper addresses a relevant problem for both Industry 4.0 and flow shop literature: the missing operations flow shop scheduling problem. In general, in order to reduce the computational effort required to solve flow shop scheduling problems only permutation schedules (PFS) are considered, i.e., the same job sequence is used for all the machines involved. However, considering only PFS is not a constraint that is based on the real-world conditions of the industrial environments, and it is only a simplification strategy used frequently in the literature. Moreover, non-permutation (NPFS) orderings may be used for most of the real flow shop systems, i.e., different job schedules can be used for different machines in the production line, since NPFS solutions usually outperform the PFS ones. In this work, a novel mathematical formulation to minimize total tardiness and a resolution method, which considers both PFS and (the more computationally expensive) NPFS solutions, are presented to solve the flow shop scheduling problem with missing operations. The solution approach has two stages. First, a Genetic Algorithm, which only considers PFS solutions, is applied to solve the scheduling problem. The resulting solution is then improved in the second stage by means of a Simulated Annealing algorithm that expands the search space by considering NPFS solutions. The experimental tests were performed on a set of instances considering varying proportions of missing operations, as it is usual in the Industry 4.0 production environment. The results show that NPFS solutions clearly outperform PFS solutions for this problem.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Growing Science
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
NON-PERMUTATION FLOW SHOP
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TOTAL TARDINESS
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INDUSTRY 4.0
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MISSING OPERATION
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GENETIC ALGORITHM
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SIMULATED ANNEALING
dc.subject
SCHEDULING
dc.subject.classification
Otras Ingenierías y Tecnologías
dc.subject.classification
Otras Ingenierías y Tecnologías
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Solving a flow shop scheduling problem with missing operations in an Industry 4.0 production environment
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2020-12-04T14:47:51Z
dc.identifier.eissn
2371-8374
dc.journal.volume
6
dc.journal.pagination
33-44
dc.journal.pais
Canadá
dc.description.fil
Fil: Rossit, Daniel Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina
dc.description.fil
Fil: Toncovich, Adrián Andrés. Universidad Nacional del Sur. Departamento de Ingeniería; Argentina
dc.description.fil
Fil: Rossit, Diego Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Bahía Blanca. Instituto de Matemática Bahía Blanca. Universidad Nacional del Sur. Departamento de Matemática. Instituto de Matemática Bahía Blanca; Argentina
dc.description.fil
Fil: Nesmachnow, Sergio. Facultad de Ingeniería; Uruguay
dc.journal.title
Journal of Project Management
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.growingscience.com/jpm/Vol6/jpm_2020_15.pdf
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.5267/j.jpm.2020.10.001
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