Order picking is the most costly activity among warehouse operations. Any unnecessary travel during order picking results in unnecessary travel time and increases the cost of operating a warehouse. Many scholars and researchers have worked to develop answers to the question of how to minimize travel distances during order picking.
The goal of this paper is to investigate how Data Mining can be used to reduce excessive travel during order picking using a Data Mining Demand-based method. This algorithm is used to mine knowledge from historical warehouse transactions and define clusters. After defining the clusters, warehouses within warehouse are defined and SKUs are arranged according to the assigned aisles for each cluster. The methodology developed is tested and compared with traditional aisle assignment algorithms. The test results show that picker travel was reduced by about 14% when using the new Data Mining model.
","abstract_html":"<p>Order picking is the most costly activity among warehouse operations. Any unnecessary travel during order picking results in unnecessary travel time and increases the cost of operating a warehouse. Many scholars and researchers have worked to develop answers to the question of how to minimize travel distances during order picking.</p><p>The goal of this paper is to investigate how Data Mining can be used to reduce excessive travel during order picking using a Data Mining Demand-based method. This algorithm is used to mine knowledge from historical warehouse transactions and define clusters. After defining the clusters, warehouses within warehouse are defined and SKUs are arranged according to the assigned aisles for each cluster. The methodology developed is tested and compared with traditional aisle assignment algorithms. The test results show that picker travel was reduced by about 14% when using the new Data Mining model.</p>","abstract_has_math":false,"creators":["Egas, Carlos A."],"institution":"Ohio University","degree_name":"Master of Science (MS)","degree_level":"masters","degree_discipline":"Industrial and Systems Engineering (Engineering and Technology)","degree_department":null,"school":null,"contributors":["Masel, Dale","Koonce, David","Berisso, Kevin","Lamb, William"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2012,"date_issued":"2012","date_published":"2012","updated_at":"2026-07-24T03:35:52Z","subjects":["Industrial Engineering","storage assignment","warehousing"],"languages":["English"],"rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1345223808","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Masel, Dale","Koonce, David","Berisso, Kevin","Lamb, William"]},{"key":"dc:creator","label":"Author","values":["Egas, Carlos A."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2012"]},{"key":"dc:publisher","label":"Institution","values":["Ohio University / OhioLINK"]},{"key":"dc:type","label":"Dc Type","values":["Electronic Thesis or Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Industrial and Systems Engineering (Engineering and Technology)"]},{"key":"thesis:degree_level","label":"Degree Level","values":["masters"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Ohio University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Industrial Engineering","storage assignment","warehousing"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]},{"key":"dc:rights","label":"Dc Rights","values":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1345223808"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Order picking is the most costly activity among warehouse operations. Any unnecessary travel during order picking results in unnecessary travel time and increases the cost of operating a warehouse. Many scholars and researchers have worked to develop answers to the question of how to minimize travel distances during order picking.
The goal of this paper is to investigate how Data Mining can be used to reduce excessive travel during order picking using a Data Mining Demand-based method. This algorithm is used to mine knowledge from historical warehouse transactions and define clusters. After defining the clusters, warehouses within warehouse are defined and SKUs are arranged according to the assigned aisles for each cluster. The methodology developed is tested and compared with traditional aisle assignment algorithms. The test results show that picker travel was reduced by about 14% when using the new Data Mining model.
"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf","p.96","811.6 KB"]},{"key":"dc:title","label":"Title","values":["Methodology for Data Mining Customer Order History for Storage Assignment"]}]}],"canonical_facts":{"dc:contributor":["Masel, Dale","Koonce, David","Berisso, Kevin","Lamb, William"],"dc:creator":["Egas, Carlos A."],"dc:date":["2012"],"dc:description":["Order picking is the most costly activity among warehouse operations. Any unnecessary travel during order picking results in unnecessary travel time and increases the cost of operating a warehouse. Many scholars and researchers have worked to develop answers to the question of how to minimize travel distances during order picking.
The goal of this paper is to investigate how Data Mining can be used to reduce excessive travel during order picking using a Data Mining Demand-based method. This algorithm is used to mine knowledge from historical warehouse transactions and define clusters. After defining the clusters, warehouses within warehouse are defined and SKUs are arranged according to the assigned aisles for each cluster. The methodology developed is tested and compared with traditional aisle assignment algorithms. The test results show that picker travel was reduced by about 14% when using the new Data Mining model.
"],"dc:format":["application/pdf","p.96","811.6 KB"],"dc:identifier":["http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1345223808"],"dc:language":["English"],"dc:publisher":["Ohio University / OhioLINK"],"dc:rights":["unrestricted","This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws."],"dc:subject":["Industrial Engineering","storage assignment","warehousing"],"dc:title":["Methodology for Data Mining Customer Order History for Storage Assignment"],"dc:type":["Electronic Thesis or Dissertation"],"thesis:degree_discipline":["Industrial and Systems Engineering (Engineering and Technology)"],"thesis:degree_level":["masters"],"thesis:degree_name":["Master of Science (MS)"],"thesis:institution_name":["Ohio University"]},"updated_at":"2026-07-24T03:35:52Z"}