Design of a supply chain network for determining the optimal number of items at the inventory groups based on ABC analysis: a comparison of exact and meta-heuristic methods
One of the most applicable techniques in the inventory management field is inventory classification based on ABC analysis, a well-known method to set the items in a different group, according to their importance and values. In this paper, a bi-objective mathematical model is proposed to improve the...
Gespeichert in:
Veröffentlicht in: | Neural computing & applications 2021-06, Vol.33 (12), p.6641-6656 |
---|---|
Hauptverfasser: | , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 6656 |
---|---|
container_issue | 12 |
container_start_page | 6641 |
container_title | Neural computing & applications |
container_volume | 33 |
creator | Abdolazimi, Omid Esfandarani, Mitra Salehi Shishebori, Davood |
description | One of the most applicable techniques in the inventory management field is inventory classification based on ABC analysis, a well-known method to set the items in a different group, according to their importance and values. In this paper, a bi-objective mathematical model is proposed to improve the inventory grouping based on ABC analysis. The first objective function maximizes the total net profit of the items in the central stock, and the second objective function maximizes the total net profit of items in different wards. The proposed model simultaneously optimizes the service level, the number of inventory groups, and the number of assigned items. To solve the model in small and large dimensions, two exact methods (LP-metric and
ε
-constraint) and two meta-heuristic methods (NSGA-II and MOPSO) are used. Then, to compare those methods in terms of efficiency, the statistical analysis besides the AHP and VIKOR techniques is implemented. The results show the superiority of the
ε
-constraint among the exact methods and MOPSO among meta-heuristic methods. Finally, the proposed model has been implemented in two sets of numerical examples to demonstrate its applicability. |
doi_str_mv | 10.1007/s00521-020-05428-y |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2534472022</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2534472022</sourcerecordid><originalsourceid>FETCH-LOGICAL-c367t-ae879da4b2d3dd06e30da50a19b5d39115d932834804e45eb0beb979ddc787253</originalsourceid><addsrcrecordid>eNp9kctu1DAUhi0EEkPhBVhZYp32xJdJwq6dcpMqdQNry4nPzLhM7NTHgeadeMh6ZpDYsbKs7_-Oj_wz9r6GyxqguSIALeoKBFSglWir5QVb1UrKSoJuX7IVdKrgtZKv2RuiBwBQ61av2J9bJL8LPG655TRP02Hhw976wAPm3zH95NuYuMOMafTBhx3Pe-Rxyn60Bx7mscd0lH3GkbjNJ-zDLww5poXvUpwn4r0ldDwGfn2z4TbYw0KePpYXhzhONnmKpw3wyQ65cMdHzLba41xQ9sPxuo-O3rJXW3sgfPf3vGA_Pn_6vvla3d1_-ba5vqsGuW5yZbFtOmdVL5x0DtYowVkNtu567WRX19p1UrRStaBQaeyhx74rihuathFaXrAP57lTio8zUjYPcU5lbTKFKtUIEKKkxDk1pEiUcGumVH4lLaYGc2zFnFsxpRVzasUsRZJniUo47DD9G_0f6xm4vpNe</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2534472022</pqid></control><display><type>article</type><title>Design of a supply chain network for determining the optimal number of items at the inventory groups based on ABC analysis: a comparison of exact and meta-heuristic methods</title><source>SpringerLink Journals - AutoHoldings</source><creator>Abdolazimi, Omid ; Esfandarani, Mitra Salehi ; Shishebori, Davood</creator><creatorcontrib>Abdolazimi, Omid ; Esfandarani, Mitra Salehi ; Shishebori, Davood</creatorcontrib><description>One of the most applicable techniques in the inventory management field is inventory classification based on ABC analysis, a well-known method to set the items in a different group, according to their importance and values. In this paper, a bi-objective mathematical model is proposed to improve the inventory grouping based on ABC analysis. The first objective function maximizes the total net profit of the items in the central stock, and the second objective function maximizes the total net profit of items in different wards. The proposed model simultaneously optimizes the service level, the number of inventory groups, and the number of assigned items. To solve the model in small and large dimensions, two exact methods (LP-metric and
ε
-constraint) and two meta-heuristic methods (NSGA-II and MOPSO) are used. Then, to compare those methods in terms of efficiency, the statistical analysis besides the AHP and VIKOR techniques is implemented. The results show the superiority of the
ε
-constraint among the exact methods and MOPSO among meta-heuristic methods. Finally, the proposed model has been implemented in two sets of numerical examples to demonstrate its applicability.</description><identifier>ISSN: 0941-0643</identifier><identifier>EISSN: 1433-3058</identifier><identifier>DOI: 10.1007/s00521-020-05428-y</identifier><language>eng</language><publisher>London: Springer London</publisher><subject>Artificial Intelligence ; Computational Biology/Bioinformatics ; Computational Science and Engineering ; Computer Science ; Data Mining and Knowledge Discovery ; Heuristic ; Heuristic methods ; Image Processing and Computer Vision ; Inventory ; Inventory management ; Mathematical models ; Original Article ; Probability and Statistics in Computer Science ; Statistical analysis ; Supply chains</subject><ispartof>Neural computing & applications, 2021-06, Vol.33 (12), p.6641-6656</ispartof><rights>Springer-Verlag London Ltd., part of Springer Nature 2020</rights><rights>Springer-Verlag London Ltd., part of Springer Nature 2020.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c367t-ae879da4b2d3dd06e30da50a19b5d39115d932834804e45eb0beb979ddc787253</citedby><cites>FETCH-LOGICAL-c367t-ae879da4b2d3dd06e30da50a19b5d39115d932834804e45eb0beb979ddc787253</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00521-020-05428-y$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00521-020-05428-y$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Abdolazimi, Omid</creatorcontrib><creatorcontrib>Esfandarani, Mitra Salehi</creatorcontrib><creatorcontrib>Shishebori, Davood</creatorcontrib><title>Design of a supply chain network for determining the optimal number of items at the inventory groups based on ABC analysis: a comparison of exact and meta-heuristic methods</title><title>Neural computing & applications</title><addtitle>Neural Comput & Applic</addtitle><description>One of the most applicable techniques in the inventory management field is inventory classification based on ABC analysis, a well-known method to set the items in a different group, according to their importance and values. In this paper, a bi-objective mathematical model is proposed to improve the inventory grouping based on ABC analysis. The first objective function maximizes the total net profit of the items in the central stock, and the second objective function maximizes the total net profit of items in different wards. The proposed model simultaneously optimizes the service level, the number of inventory groups, and the number of assigned items. To solve the model in small and large dimensions, two exact methods (LP-metric and
ε
-constraint) and two meta-heuristic methods (NSGA-II and MOPSO) are used. Then, to compare those methods in terms of efficiency, the statistical analysis besides the AHP and VIKOR techniques is implemented. The results show the superiority of the
ε
-constraint among the exact methods and MOPSO among meta-heuristic methods. Finally, the proposed model has been implemented in two sets of numerical examples to demonstrate its applicability.</description><subject>Artificial Intelligence</subject><subject>Computational Biology/Bioinformatics</subject><subject>Computational Science and Engineering</subject><subject>Computer Science</subject><subject>Data Mining and Knowledge Discovery</subject><subject>Heuristic</subject><subject>Heuristic methods</subject><subject>Image Processing and Computer Vision</subject><subject>Inventory</subject><subject>Inventory management</subject><subject>Mathematical models</subject><subject>Original Article</subject><subject>Probability and Statistics in Computer Science</subject><subject>Statistical analysis</subject><subject>Supply chains</subject><issn>0941-0643</issn><issn>1433-3058</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp9kctu1DAUhi0EEkPhBVhZYp32xJdJwq6dcpMqdQNry4nPzLhM7NTHgeadeMh6ZpDYsbKs7_-Oj_wz9r6GyxqguSIALeoKBFSglWir5QVb1UrKSoJuX7IVdKrgtZKv2RuiBwBQ61av2J9bJL8LPG655TRP02Hhw976wAPm3zH95NuYuMOMafTBhx3Pe-Rxyn60Bx7mscd0lH3GkbjNJ-zDLww5poXvUpwn4r0ldDwGfn2z4TbYw0KePpYXhzhONnmKpw3wyQ65cMdHzLba41xQ9sPxuo-O3rJXW3sgfPf3vGA_Pn_6vvla3d1_-ba5vqsGuW5yZbFtOmdVL5x0DtYowVkNtu567WRX19p1UrRStaBQaeyhx74rihuathFaXrAP57lTio8zUjYPcU5lbTKFKtUIEKKkxDk1pEiUcGumVH4lLaYGc2zFnFsxpRVzasUsRZJniUo47DD9G_0f6xm4vpNe</recordid><startdate>20210601</startdate><enddate>20210601</enddate><creator>Abdolazimi, Omid</creator><creator>Esfandarani, Mitra Salehi</creator><creator>Shishebori, Davood</creator><general>Springer London</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>8FE</scope><scope>8FG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>P5Z</scope><scope>P62</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope></search><sort><creationdate>20210601</creationdate><title>Design of a supply chain network for determining the optimal number of items at the inventory groups based on ABC analysis: a comparison of exact and meta-heuristic methods</title><author>Abdolazimi, Omid ; Esfandarani, Mitra Salehi ; Shishebori, Davood</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c367t-ae879da4b2d3dd06e30da50a19b5d39115d932834804e45eb0beb979ddc787253</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Artificial Intelligence</topic><topic>Computational Biology/Bioinformatics</topic><topic>Computational Science and Engineering</topic><topic>Computer Science</topic><topic>Data Mining and Knowledge Discovery</topic><topic>Heuristic</topic><topic>Heuristic methods</topic><topic>Image Processing and Computer Vision</topic><topic>Inventory</topic><topic>Inventory management</topic><topic>Mathematical models</topic><topic>Original Article</topic><topic>Probability and Statistics in Computer Science</topic><topic>Statistical analysis</topic><topic>Supply chains</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Abdolazimi, Omid</creatorcontrib><creatorcontrib>Esfandarani, Mitra Salehi</creatorcontrib><creatorcontrib>Shishebori, Davood</creatorcontrib><collection>CrossRef</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Neural computing & applications</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Abdolazimi, Omid</au><au>Esfandarani, Mitra Salehi</au><au>Shishebori, Davood</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Design of a supply chain network for determining the optimal number of items at the inventory groups based on ABC analysis: a comparison of exact and meta-heuristic methods</atitle><jtitle>Neural computing & applications</jtitle><stitle>Neural Comput & Applic</stitle><date>2021-06-01</date><risdate>2021</risdate><volume>33</volume><issue>12</issue><spage>6641</spage><epage>6656</epage><pages>6641-6656</pages><issn>0941-0643</issn><eissn>1433-3058</eissn><abstract>One of the most applicable techniques in the inventory management field is inventory classification based on ABC analysis, a well-known method to set the items in a different group, according to their importance and values. In this paper, a bi-objective mathematical model is proposed to improve the inventory grouping based on ABC analysis. The first objective function maximizes the total net profit of the items in the central stock, and the second objective function maximizes the total net profit of items in different wards. The proposed model simultaneously optimizes the service level, the number of inventory groups, and the number of assigned items. To solve the model in small and large dimensions, two exact methods (LP-metric and
ε
-constraint) and two meta-heuristic methods (NSGA-II and MOPSO) are used. Then, to compare those methods in terms of efficiency, the statistical analysis besides the AHP and VIKOR techniques is implemented. The results show the superiority of the
ε
-constraint among the exact methods and MOPSO among meta-heuristic methods. Finally, the proposed model has been implemented in two sets of numerical examples to demonstrate its applicability.</abstract><cop>London</cop><pub>Springer London</pub><doi>10.1007/s00521-020-05428-y</doi><tpages>16</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0941-0643 |
ispartof | Neural computing & applications, 2021-06, Vol.33 (12), p.6641-6656 |
issn | 0941-0643 1433-3058 |
language | eng |
recordid | cdi_proquest_journals_2534472022 |
source | SpringerLink Journals - AutoHoldings |
subjects | Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Data Mining and Knowledge Discovery Heuristic Heuristic methods Image Processing and Computer Vision Inventory Inventory management Mathematical models Original Article Probability and Statistics in Computer Science Statistical analysis Supply chains |
title | Design of a supply chain network for determining the optimal number of items at the inventory groups based on ABC analysis: a comparison of exact and meta-heuristic methods |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-25T01%3A57%3A27IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Design%20of%20a%20supply%20chain%20network%20for%20determining%20the%20optimal%20number%20of%20items%20at%20the%20inventory%20groups%20based%20on%20ABC%20analysis:%20a%20comparison%20of%20exact%20and%20meta-heuristic%20methods&rft.jtitle=Neural%20computing%20&%20applications&rft.au=Abdolazimi,%20Omid&rft.date=2021-06-01&rft.volume=33&rft.issue=12&rft.spage=6641&rft.epage=6656&rft.pages=6641-6656&rft.issn=0941-0643&rft.eissn=1433-3058&rft_id=info:doi/10.1007/s00521-020-05428-y&rft_dat=%3Cproquest_cross%3E2534472022%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2534472022&rft_id=info:pmid/&rfr_iscdi=true |