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dc.contributor.authorLi, Yue ( )
dc.contributor.authorMendez-Mediavilla, Francis A. ( )
dc.contributor.authorTemponi, Cecilia ( Orcid Icon 0000-0003-4103-105X )
dc.contributor.authorKim, Junwoo ( Orcid Icon 0000-0002-0875-8364 )
dc.contributor.authorJimenez, Jesus ( Orcid Icon 0000-0001-6300-2714 )
dc.date.accessioned2021-07-30T18:24:10Z
dc.date.available2021-07-30T18:24:10Z
dc.date.issued2021-02-19
dc.identifier.citationLi, Y., Méndez-Mediavilla, F. A., Temponi, C., Kim, J., & Jimenez, J. A. (2021). A heuristic storage location assignment based on frequent itemset classes to improve order picking operations. Applied Sciences, 11(4), 1839.en_US
dc.identifier.issn2076-3417
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/14142
dc.description.abstractMost large distribution centers’ order picking processes are highly labor-intensive. Increasing the efficiency of order picking allows these facilities to move higher volumes of products. The application of data mining in distribution centers has the capability of generating efficiency improvements, mainly if these techniques are used to analyze the large amount of data generated by orders received by distribution centers and determine correlations in ordering patterns. This paper proposes a heuristic method to optimize the order picking distance based on frequent itemset grouping and nonuniform product weights. The proposed heuristic uses association rule mining (ARM) to create families of products based on the similarities between the stock keeping units (SKUs). SKUs with higher similarities are located near the rest of the members of the family. This heuristic is applied to a numerical case using data obtained from a real distribution center in the food retail industry. The experiment results show that data mining-driven developed layouts can reduce the traveling distance required to pick orders.en_US
dc.formatText
dc.format.extent15 pages
dc.format.medium1 file (.pdf)
dc.language.isoenen_US
dc.publisherMultidisciplinary Digital Publishing Instituteen_US
dc.sourceApplied Sciences, 2021, Vol. 11, No. 4, Article 1839.
dc.subjectDistribution centersen_US
dc.subjectOrder pickingen_US
dc.subjectData miningen_US
dc.subjectAssociation rule miningen_US
dc.titleA Heuristic Storage Location Assignment Based on Frequent Itemset Classes to Improve Order Picking Operationsen_US
dc.typepublishedVersion
txstate.documenttypeArticle
dc.rights.holder© 2021 The Authors.
dc.identifier.doihttps://doi.org/10.3390/app11041839
dc.rights.licenseCreative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
dc.description.departmentBusiness Administration
dc.description.departmentIngram School of Engineering


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