A hybrid approach development to solving the storage location assignment problem in a picker-to-parts system
Autor(a) principal: | |
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Data de Publicação: | 2020 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Journal of Operations & Production Management (Online) |
Texto Completo: | https://bjopm.org.br/bjopm/article/view/853 |
Resumo: | Goal: This study developed a structured decision model capable of solving the storage location assignment problem (SLAP) in a picker-to-parts system, using multiples key performance indicators (KPIs). Design / Methodology / Approach: A hybrid approach was developed. For that, a Multi-Objective Genetic Algorithm (MOGA) was used considering three fitness functions, but more functions may be considered. Through MOGA it was possible to verify a high number of solutions and reduce it into a Pareto frontier. After that, a Multiple-Criteria Decision-Making (MCDM) approach was used to choose the best solution. Results: This model was able to find viable solutions considering multiples objectives, warehouse restrictions and decision makers’ preferences, and the required processing time for the simulated cases was insignificant. Limitations of the investigation: One limitation of this work was the consideration of known and predictable data. Practical implications: The proposed model was developed with the purpose of assisting companies that face this type of problem, providing a solution for SLAP requiring the minimum information and operational actions. Originality / Value: SLAP is a NP (Non-Deterministic Polynomial time) complex problem and, after the MOGA, the number of solution can be still high for the final decision making by the engineering manager (decision maker - DM). Thus, the MOGA–MCDM hybrid approach developed was able incorporate the DM' preferences into a compensatory view, vetoing alternatives that were worse in any of the KPIs, to recommend a final solution. |
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Brazilian Journal of Operations & Production Management (Online) |
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A hybrid approach development to solving the storage location assignment problem in a picker-to-parts systemWarehouse operationsOrder picking systemStorage policyNon-dominated sorting genetic algorithm II (NSGA-II)Additive-veto modelGoal: This study developed a structured decision model capable of solving the storage location assignment problem (SLAP) in a picker-to-parts system, using multiples key performance indicators (KPIs). Design / Methodology / Approach: A hybrid approach was developed. For that, a Multi-Objective Genetic Algorithm (MOGA) was used considering three fitness functions, but more functions may be considered. Through MOGA it was possible to verify a high number of solutions and reduce it into a Pareto frontier. After that, a Multiple-Criteria Decision-Making (MCDM) approach was used to choose the best solution. Results: This model was able to find viable solutions considering multiples objectives, warehouse restrictions and decision makers’ preferences, and the required processing time for the simulated cases was insignificant. Limitations of the investigation: One limitation of this work was the consideration of known and predictable data. Practical implications: The proposed model was developed with the purpose of assisting companies that face this type of problem, providing a solution for SLAP requiring the minimum information and operational actions. Originality / Value: SLAP is a NP (Non-Deterministic Polynomial time) complex problem and, after the MOGA, the number of solution can be still high for the final decision making by the engineering manager (decision maker - DM). Thus, the MOGA–MCDM hybrid approach developed was able incorporate the DM' preferences into a compensatory view, vetoing alternatives that were worse in any of the KPIs, to recommend a final solution.Brazilian Association for Industrial Engineering and Operations Management (ABEPRO)2020-02-11info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionPeer-reviewed Articleapplication/pdfhttps://bjopm.org.br/bjopm/article/view/85310.14488/BJOPM.2020.005Brazilian Journal of Operations & Production Management; Vol. 17 No. 1 (2020): March, 2020; 1-142237-8960reponame:Brazilian Journal of Operations & Production Management (Online)instname:Associação Brasileira de Engenharia de Produção (ABEPRO)instacron:ABEPROenghttps://bjopm.org.br/bjopm/article/view/853/918Copyright (c) 2020 Marcele Elisa Fontana, Vilmar Santos Nepomuceno, Thalles Vitelli Garcezinfo:eu-repo/semantics/openAccessFontana, Marcele ElisaNepomuceno, Vilmar SantosGarcez, Thalles Vitelli2020-02-12T10:28:37Zoai:ojs.bjopm.org.br:article/853Revistahttps://bjopm.org.br/bjopmONGhttps://bjopm.org.br/bjopm/oaibjopm.journal@gmail.com2237-89601679-8171opendoar:2023-03-13T09:45:22.938991Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO)false |
dc.title.none.fl_str_mv |
A hybrid approach development to solving the storage location assignment problem in a picker-to-parts system |
title |
A hybrid approach development to solving the storage location assignment problem in a picker-to-parts system |
spellingShingle |
A hybrid approach development to solving the storage location assignment problem in a picker-to-parts system Fontana, Marcele Elisa Warehouse operations Order picking system Storage policy Non-dominated sorting genetic algorithm II (NSGA-II) Additive-veto model |
title_short |
A hybrid approach development to solving the storage location assignment problem in a picker-to-parts system |
title_full |
A hybrid approach development to solving the storage location assignment problem in a picker-to-parts system |
title_fullStr |
A hybrid approach development to solving the storage location assignment problem in a picker-to-parts system |
title_full_unstemmed |
A hybrid approach development to solving the storage location assignment problem in a picker-to-parts system |
title_sort |
A hybrid approach development to solving the storage location assignment problem in a picker-to-parts system |
author |
Fontana, Marcele Elisa |
author_facet |
Fontana, Marcele Elisa Nepomuceno, Vilmar Santos Garcez, Thalles Vitelli |
author_role |
author |
author2 |
Nepomuceno, Vilmar Santos Garcez, Thalles Vitelli |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Fontana, Marcele Elisa Nepomuceno, Vilmar Santos Garcez, Thalles Vitelli |
dc.subject.por.fl_str_mv |
Warehouse operations Order picking system Storage policy Non-dominated sorting genetic algorithm II (NSGA-II) Additive-veto model |
topic |
Warehouse operations Order picking system Storage policy Non-dominated sorting genetic algorithm II (NSGA-II) Additive-veto model |
description |
Goal: This study developed a structured decision model capable of solving the storage location assignment problem (SLAP) in a picker-to-parts system, using multiples key performance indicators (KPIs). Design / Methodology / Approach: A hybrid approach was developed. For that, a Multi-Objective Genetic Algorithm (MOGA) was used considering three fitness functions, but more functions may be considered. Through MOGA it was possible to verify a high number of solutions and reduce it into a Pareto frontier. After that, a Multiple-Criteria Decision-Making (MCDM) approach was used to choose the best solution. Results: This model was able to find viable solutions considering multiples objectives, warehouse restrictions and decision makers’ preferences, and the required processing time for the simulated cases was insignificant. Limitations of the investigation: One limitation of this work was the consideration of known and predictable data. Practical implications: The proposed model was developed with the purpose of assisting companies that face this type of problem, providing a solution for SLAP requiring the minimum information and operational actions. Originality / Value: SLAP is a NP (Non-Deterministic Polynomial time) complex problem and, after the MOGA, the number of solution can be still high for the final decision making by the engineering manager (decision maker - DM). Thus, the MOGA–MCDM hybrid approach developed was able incorporate the DM' preferences into a compensatory view, vetoing alternatives that were worse in any of the KPIs, to recommend a final solution. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-02-11 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Peer-reviewed Article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/853 10.14488/BJOPM.2020.005 |
url |
https://bjopm.org.br/bjopm/article/view/853 |
identifier_str_mv |
10.14488/BJOPM.2020.005 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://bjopm.org.br/bjopm/article/view/853/918 |
dc.rights.driver.fl_str_mv |
Copyright (c) 2020 Marcele Elisa Fontana, Vilmar Santos Nepomuceno, Thalles Vitelli Garcez info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
Copyright (c) 2020 Marcele Elisa Fontana, Vilmar Santos Nepomuceno, Thalles Vitelli Garcez |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Brazilian Association for Industrial Engineering and Operations Management (ABEPRO) |
publisher.none.fl_str_mv |
Brazilian Association for Industrial Engineering and Operations Management (ABEPRO) |
dc.source.none.fl_str_mv |
Brazilian Journal of Operations & Production Management; Vol. 17 No. 1 (2020): March, 2020; 1-14 2237-8960 reponame:Brazilian Journal of Operations & Production Management (Online) instname:Associação Brasileira de Engenharia de Produção (ABEPRO) instacron:ABEPRO |
instname_str |
Associação Brasileira de Engenharia de Produção (ABEPRO) |
instacron_str |
ABEPRO |
institution |
ABEPRO |
reponame_str |
Brazilian Journal of Operations & Production Management (Online) |
collection |
Brazilian Journal of Operations & Production Management (Online) |
repository.name.fl_str_mv |
Brazilian Journal of Operations & Production Management (Online) - Associação Brasileira de Engenharia de Produção (ABEPRO) |
repository.mail.fl_str_mv |
bjopm.journal@gmail.com |
_version_ |
1797051461433032704 |