Biased random-key genetic algorithm for warehouse reshuffling
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UFPE |
Texto Completo: | https://repositorio.ufpe.br/handle/123456789/31914 |
Resumo: | Due to its strategical importance, the efficient stock management in a warehouse presents several challenges that can be approached using optimization methods. In this universe, frequently explored problems are ambient dimensioning, department organization and layout, stock organization and layout, pilling design, product storage and recovery methodology. Design and operation imprecisions and failures can result in large delays in the product delivery or even in missing items in final client stocks. Among the main causes of missing items in inventories, there are the incongruity between storage capacity and refilling frequency; infrequency, delay, or nonexistence of product restitution in shelves; inexact or wrong inventories; storages with the inadequate organization, package disruption and scarce availability; poor storage layout and inefficient operational services. To determine the optimized product stocking is a problem frequently approached in the literature throughout the decades. However, the increasing need or changes in the storage, increase the importance of other problem: the sequence of movement to obtain a particular stock organization, given the current organization of the items. This problem is known as stock rearrangement, stock shuffling, or stock reshuffling. The optimization of package reshuffling in large warehouses directly impacts the profits. Large warehouses need, very frequently, to reorganize stock because of: seasonality, market changes, logistics, and other factors. Certain types of products have higher demand during specific periods of the year. Products on sale may leave the stock faster, new products may have higher output. All these are examples that justify a frequent stock reshuffling. Warehouse stock reshuffling consists of repositioning items by moving them sequentially. Several studies aim to solve reshuffling problems by applying exact methods. However, due to the complexity of the problem, only heuristics result in practical solutions. This study investigates how to optimize unit-load warehouse reshuffling in multiple empty locations scenarios. Traditional heuristics are reviewed and an evolutionary programming approach is proposed for the unit-load warehouse reshuffling problem. Experimental results indicate the proposed heuristic perform satisfactorily in terms of computational time and is able to improve solution quality upon benchmark heuristics. |
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BUENO, Leonardo de Almeida ehttp://lattes.cnpq.br/5177365240267092http://lattes.cnpq.br/6234141909588262SILVA, Ricardo Martins de Abreu2019-08-19T18:21:45Z2019-08-19T18:21:45Z2018-08-08https://repositorio.ufpe.br/handle/123456789/31914Due to its strategical importance, the efficient stock management in a warehouse presents several challenges that can be approached using optimization methods. In this universe, frequently explored problems are ambient dimensioning, department organization and layout, stock organization and layout, pilling design, product storage and recovery methodology. Design and operation imprecisions and failures can result in large delays in the product delivery or even in missing items in final client stocks. Among the main causes of missing items in inventories, there are the incongruity between storage capacity and refilling frequency; infrequency, delay, or nonexistence of product restitution in shelves; inexact or wrong inventories; storages with the inadequate organization, package disruption and scarce availability; poor storage layout and inefficient operational services. To determine the optimized product stocking is a problem frequently approached in the literature throughout the decades. However, the increasing need or changes in the storage, increase the importance of other problem: the sequence of movement to obtain a particular stock organization, given the current organization of the items. This problem is known as stock rearrangement, stock shuffling, or stock reshuffling. The optimization of package reshuffling in large warehouses directly impacts the profits. Large warehouses need, very frequently, to reorganize stock because of: seasonality, market changes, logistics, and other factors. Certain types of products have higher demand during specific periods of the year. Products on sale may leave the stock faster, new products may have higher output. All these are examples that justify a frequent stock reshuffling. Warehouse stock reshuffling consists of repositioning items by moving them sequentially. Several studies aim to solve reshuffling problems by applying exact methods. However, due to the complexity of the problem, only heuristics result in practical solutions. This study investigates how to optimize unit-load warehouse reshuffling in multiple empty locations scenarios. Traditional heuristics are reviewed and an evolutionary programming approach is proposed for the unit-load warehouse reshuffling problem. Experimental results indicate the proposed heuristic perform satisfactorily in terms of computational time and is able to improve solution quality upon benchmark heuristics.Devido à sua importância estratégica, a gestão eficiente de grandes armazéns apresenta diversos desafios que podem ser resolvidos via métodos de otimização. Neste universo, são frequentemente explorados pela literatura os problemas de: dimensionamento de ambientes, organização e layout de departamentos e estoques, padrão de empilhamento, metodologia de armazenamento e recuperação de produtos. Imprecisões e falhas de projeto e operação de armazéns podem resultar em grandes atrasos na entrega de produtos e até na falta de itens em inventários de clientes finais. Entre as causas principais de falta de inventário se encontram: incongruência entre capacidade e frequência de abastecimento; infrequência, atraso ou inexistência de reposição de artigos em prateleiras; inventário inexato ou errado; armazenamento com organização inadequada, rompimento de embalagens ou pouca disponibilidade; mal projeto do estoque e serviços operacionais ineficientes. Determinar a forma otimizada de armazenamento de produtos é um problema que vem sido estudado há décadas, porém, a cada vez mais frequente necessidade de mudança nos estoques trouxe um novo problema à tona: a sequência de movimento para obtenção de uma organização em particular, dado o estado atual das cargas no estoque. Este problema é conhecido como reorganização de estoque. Otimizar a reorganização de itens em grandes armazéns impacta diretamente e de forma positiva os rendimentos. Grandes armazéns frequentemente necessitam de reorganizações por motivos sazonais, de mercado, logísticos, etc. Determinados tipos de produtos tem maior demanda em uma época do ano do que em outras, produtos postos em promoção vão ser liquidados e vão sair do estoque mais rapidamente, novos produtos são recebidos constantemente nos depósitos, todos esses são exemplos que requerem uma reorganização frequente no estoque. Reorganização de pacotes em centros de distribuição consiste em reposicionar itens movendo-os sequencialmente. Vários estudos da literatura se propõem a solucionar problemas de reorganização de pacotes aplicando métodos exatos. No entanto, devido à complexidade do problema, apenas heurísticas obtém tempos de processamento viáveis para aplicações reais. Este estudo investiga como otimizar a reorganização de centros de distribuição de cargas unitárias em cenários onde existem múltiplas localizações vazias. Heurísticas tradicionais são revisadas e uma abordagem de programação evolucionária é proposta para o problema. Resultados experimentais indicam que a heurística proposta tem desempenho satisfatório em termos de tempo computacional e é capaz de melhorar a qualidade das soluções em comparação com heurísticas de referência.engUniversidade Federal de PernambucoPrograma de Pos Graduacao em Ciencia da ComputacaoUFPEBrasilAttribution-NonCommercial-NoDerivs 3.0 Brazilhttp://creativecommons.org/licenses/by-nc-nd/3.0/br/info:eu-repo/semantics/openAccessCiência da computaçãoPesquisa operacionalOtimizaçãoBiased random-key genetic algorithm for warehouse reshufflinginfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesismestradoreponame:Repositório Institucional da UFPEinstname:Universidade Federal de Pernambuco (UFPE)instacron:UFPETHUMBNAILDISSERTAÇÃO Leonardo de Almeida e Bueno.pdf.jpgDISSERTAÇÃO Leonardo de Almeida e Bueno.pdf.jpgGenerated Thumbnailimage/jpeg1255https://repositorio.ufpe.br/bitstream/123456789/31914/5/DISSERTA%c3%87%c3%83O%20Leonardo%20de%20Almeida%20e%20Bueno.pdf.jpg36eaa9cb64c0889d4628a5b47218d366MD55ORIGINALDISSERTAÇÃO Leonardo de Almeida e Bueno.pdfDISSERTAÇÃO Leonardo de Almeida e Bueno.pdfapplication/pdf2745993https://repositorio.ufpe.br/bitstream/123456789/31914/1/DISSERTA%c3%87%c3%83O%20Leonardo%20de%20Almeida%20e%20Bueno.pdf5093a380e99b9e2467fbc26b0834a911MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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dc.title.pt_BR.fl_str_mv |
Biased random-key genetic algorithm for warehouse reshuffling |
title |
Biased random-key genetic algorithm for warehouse reshuffling |
spellingShingle |
Biased random-key genetic algorithm for warehouse reshuffling BUENO, Leonardo de Almeida e Ciência da computação Pesquisa operacional Otimização |
title_short |
Biased random-key genetic algorithm for warehouse reshuffling |
title_full |
Biased random-key genetic algorithm for warehouse reshuffling |
title_fullStr |
Biased random-key genetic algorithm for warehouse reshuffling |
title_full_unstemmed |
Biased random-key genetic algorithm for warehouse reshuffling |
title_sort |
Biased random-key genetic algorithm for warehouse reshuffling |
author |
BUENO, Leonardo de Almeida e |
author_facet |
BUENO, Leonardo de Almeida e |
author_role |
author |
dc.contributor.authorLattes.pt_BR.fl_str_mv |
http://lattes.cnpq.br/5177365240267092 |
dc.contributor.advisorLattes.pt_BR.fl_str_mv |
http://lattes.cnpq.br/6234141909588262 |
dc.contributor.author.fl_str_mv |
BUENO, Leonardo de Almeida e |
dc.contributor.advisor1.fl_str_mv |
SILVA, Ricardo Martins de Abreu |
contributor_str_mv |
SILVA, Ricardo Martins de Abreu |
dc.subject.por.fl_str_mv |
Ciência da computação Pesquisa operacional Otimização |
topic |
Ciência da computação Pesquisa operacional Otimização |
description |
Due to its strategical importance, the efficient stock management in a warehouse presents several challenges that can be approached using optimization methods. In this universe, frequently explored problems are ambient dimensioning, department organization and layout, stock organization and layout, pilling design, product storage and recovery methodology. Design and operation imprecisions and failures can result in large delays in the product delivery or even in missing items in final client stocks. Among the main causes of missing items in inventories, there are the incongruity between storage capacity and refilling frequency; infrequency, delay, or nonexistence of product restitution in shelves; inexact or wrong inventories; storages with the inadequate organization, package disruption and scarce availability; poor storage layout and inefficient operational services. To determine the optimized product stocking is a problem frequently approached in the literature throughout the decades. However, the increasing need or changes in the storage, increase the importance of other problem: the sequence of movement to obtain a particular stock organization, given the current organization of the items. This problem is known as stock rearrangement, stock shuffling, or stock reshuffling. The optimization of package reshuffling in large warehouses directly impacts the profits. Large warehouses need, very frequently, to reorganize stock because of: seasonality, market changes, logistics, and other factors. Certain types of products have higher demand during specific periods of the year. Products on sale may leave the stock faster, new products may have higher output. All these are examples that justify a frequent stock reshuffling. Warehouse stock reshuffling consists of repositioning items by moving them sequentially. Several studies aim to solve reshuffling problems by applying exact methods. However, due to the complexity of the problem, only heuristics result in practical solutions. This study investigates how to optimize unit-load warehouse reshuffling in multiple empty locations scenarios. Traditional heuristics are reviewed and an evolutionary programming approach is proposed for the unit-load warehouse reshuffling problem. Experimental results indicate the proposed heuristic perform satisfactorily in terms of computational time and is able to improve solution quality upon benchmark heuristics. |
publishDate |
2018 |
dc.date.issued.fl_str_mv |
2018-08-08 |
dc.date.accessioned.fl_str_mv |
2019-08-19T18:21:45Z |
dc.date.available.fl_str_mv |
2019-08-19T18:21:45Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufpe.br/handle/123456789/31914 |
url |
https://repositorio.ufpe.br/handle/123456789/31914 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivs 3.0 Brazil http://creativecommons.org/licenses/by-nc-nd/3.0/br/ |
eu_rights_str_mv |
openAccess |
dc.publisher.none.fl_str_mv |
Universidade Federal de Pernambuco |
dc.publisher.program.fl_str_mv |
Programa de Pos Graduacao em Ciencia da Computacao |
dc.publisher.initials.fl_str_mv |
UFPE |
dc.publisher.country.fl_str_mv |
Brasil |
publisher.none.fl_str_mv |
Universidade Federal de Pernambuco |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional da UFPE instname:Universidade Federal de Pernambuco (UFPE) instacron:UFPE |
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