Acquiring Knowledge Needed for Pull Production System Design through Data Mining Methods

Article deals with defining relationships for setting the optimal number of kanban cards in individual circuits of pull production systems, in order to minimize work in progress, while maximizing the number of completed orders in the observed time interval. To achieve this objective, data mining met...

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Main Authors: Peter Bubenik, Filip Horak, Viktor Hancinsky
Format: Article
Language:English
Published: University of Žilina 2015-08-01
Series:Communications
Subjects:
Online Access:https://komunikacie.uniza.sk/artkey/csl-201503-0013_acquiring-knowledge-needed-for-pull-production-system-design-through-data-mining-methods.php
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author Peter Bubenik
Filip Horak
Viktor Hancinsky
author_facet Peter Bubenik
Filip Horak
Viktor Hancinsky
author_sort Peter Bubenik
collection DOAJ
description Article deals with defining relationships for setting the optimal number of kanban cards in individual circuits of pull production systems, in order to minimize work in progress, while maximizing the number of completed orders in the observed time interval. To achieve this objective, data mining methods were used.
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spelling doaj.art-ed68c789093c4f3297ba6c26b86b087f2023-04-14T06:31:00ZengUniversity of ŽilinaCommunications1335-42052585-78782015-08-01173788210.26552/com.C.2015.3.78-82csl-201503-0013Acquiring Knowledge Needed for Pull Production System Design through Data Mining MethodsPeter Bubenik0Filip Horak1Viktor Hancinsky2Department of Industrial Engineering, Faculty of Mechanical Engineering University of Zilina, SlovakiaDepartment of Industrial Engineering, Faculty of Mechanical Engineering University of Zilina, SlovakiaDepartment of Industrial Engineering, Faculty of Mechanical Engineering University of Zilina, SlovakiaArticle deals with defining relationships for setting the optimal number of kanban cards in individual circuits of pull production systems, in order to minimize work in progress, while maximizing the number of completed orders in the observed time interval. To achieve this objective, data mining methods were used.https://komunikacie.uniza.sk/artkey/csl-201503-0013_acquiring-knowledge-needed-for-pull-production-system-design-through-data-mining-methods.phpdata miningproduction planningknowledge acquisitiongenetic algorithm
spellingShingle Peter Bubenik
Filip Horak
Viktor Hancinsky
Acquiring Knowledge Needed for Pull Production System Design through Data Mining Methods
Communications
data mining
production planning
knowledge acquisition
genetic algorithm
title Acquiring Knowledge Needed for Pull Production System Design through Data Mining Methods
title_full Acquiring Knowledge Needed for Pull Production System Design through Data Mining Methods
title_fullStr Acquiring Knowledge Needed for Pull Production System Design through Data Mining Methods
title_full_unstemmed Acquiring Knowledge Needed for Pull Production System Design through Data Mining Methods
title_short Acquiring Knowledge Needed for Pull Production System Design through Data Mining Methods
title_sort acquiring knowledge needed for pull production system design through data mining methods
topic data mining
production planning
knowledge acquisition
genetic algorithm
url https://komunikacie.uniza.sk/artkey/csl-201503-0013_acquiring-knowledge-needed-for-pull-production-system-design-through-data-mining-methods.php
work_keys_str_mv AT peterbubenik acquiringknowledgeneededforpullproductionsystemdesignthroughdataminingmethods
AT filiphorak acquiringknowledgeneededforpullproductionsystemdesignthroughdataminingmethods
AT viktorhancinsky acquiringknowledgeneededforpullproductionsystemdesignthroughdataminingmethods