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A Neural Network-based Hybrid Method to Generate Feasible Neighbors for Flexible Job Shop Scheduling Problem

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dc.contributor.authorTeymourifar, Aydin
dc.contributor.authorÖztürk, Gürkan
dc.contributor.orcid0000-0002-0285-0266
dc.contributor.orcid0000-0002-9480-176X
dc.date.accessioned2025-11-13T10:18:20Z
dc.date.issued2018-01-01
dc.identifier.doihttps://doi.org/10.13189/ujam.2018.060101
dc.identifier.endpage16
dc.identifier.issn2331-6446
dc.identifier.issue1
dc.identifier.openalexW2788797456
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/11421/3746
dc.identifier.urihttps://doi.org/10.13189/ujam.2018.060101
dc.identifier.volume6
dc.language.isoen
dc.relation.ispartofUniversal Journal of Applied Mathematics
dc.rightsopenAccess
dc.subjectComputer science
dc.subjectArtificial neural network
dc.subjectMathematical optimization
dc.subjectJob shop scheduling
dc.subjectScheduling (production processes)
dc.subjectMathematics
dc.subjectArtificial intelligence
dc.subjectSchedule
dc.subjectOperating system
dc.subject.sdg8
dc.titleA Neural Network-based Hybrid Method to Generate Feasible Neighbors for Flexible Job Shop Scheduling Problem
dc.typeArticle
dspace.entity.typePublication
local.authorid.openalexA5080770456
local.authorid.openalexA5004800651

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