An adjustable sample average approximation algorithm for the stochastic production-inventory-routing problem
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10773/24196 |
Resumo: | We consider a stochastic single item production-inventory-routing problem with a single producer, multiple clients, and multiple vehicles. At the clients, demand is allowed to be backlogged incurring a penalty cost. Demands are considered uncertain. A recourse model is presented, and valid inequalities are introduced to enhance the model. A new general approach that explores the sample average approximation (SAA) method is introduced. In the sample average approximation method, several sample sets are generated and solved independently in order to obtain a set of candidate solutions. Then, the candidate solutions are tested on a larger sample, and the best solution is selected among the candidates. In contrast to this approach, called static, we propose an adjustable approach that explores the candidate solutions in order to identify common structures. Using that information, part of the first-stage decision variables is fixed, and the resulting restricted problem is solved for a larger size sample. Several heuristic algorithms based on the mathematical model are considered within each approach. Computational tests based on randomly generated instances are conducted to test several variants of the two approaches. The results show that the new adjustable SAA heuristic performs better than the static one for most of the instances. |
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7160 |
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An adjustable sample average approximation algorithm for the stochastic production-inventory-routing problemInventory routingStochastic programmingSample average approximation algorithmHybrid heuristicDemand uncertaintyIterated local searchAdaptive heuristicWe consider a stochastic single item production-inventory-routing problem with a single producer, multiple clients, and multiple vehicles. At the clients, demand is allowed to be backlogged incurring a penalty cost. Demands are considered uncertain. A recourse model is presented, and valid inequalities are introduced to enhance the model. A new general approach that explores the sample average approximation (SAA) method is introduced. In the sample average approximation method, several sample sets are generated and solved independently in order to obtain a set of candidate solutions. Then, the candidate solutions are tested on a larger sample, and the best solution is selected among the candidates. In contrast to this approach, called static, we propose an adjustable approach that explores the candidate solutions in order to identify common structures. Using that information, part of the first-stage decision variables is fixed, and the resulting restricted problem is solved for a larger size sample. Several heuristic algorithms based on the mathematical model are considered within each approach. Computational tests based on randomly generated instances are conducted to test several variants of the two approaches. The results show that the new adjustable SAA heuristic performs better than the static one for most of the instances.Wiley2018-072018-07-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10773/24196eng0028-304510.1002/net.21796Agra, AgostinhoRequejo, CristinaRodrigues, Filipeinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-02-22T11:47:32Zoai:ria.ua.pt:10773/24196Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T02:57:57.022145Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
An adjustable sample average approximation algorithm for the stochastic production-inventory-routing problem |
title |
An adjustable sample average approximation algorithm for the stochastic production-inventory-routing problem |
spellingShingle |
An adjustable sample average approximation algorithm for the stochastic production-inventory-routing problem Agra, Agostinho Inventory routing Stochastic programming Sample average approximation algorithm Hybrid heuristic Demand uncertainty Iterated local search Adaptive heuristic |
title_short |
An adjustable sample average approximation algorithm for the stochastic production-inventory-routing problem |
title_full |
An adjustable sample average approximation algorithm for the stochastic production-inventory-routing problem |
title_fullStr |
An adjustable sample average approximation algorithm for the stochastic production-inventory-routing problem |
title_full_unstemmed |
An adjustable sample average approximation algorithm for the stochastic production-inventory-routing problem |
title_sort |
An adjustable sample average approximation algorithm for the stochastic production-inventory-routing problem |
author |
Agra, Agostinho |
author_facet |
Agra, Agostinho Requejo, Cristina Rodrigues, Filipe |
author_role |
author |
author2 |
Requejo, Cristina Rodrigues, Filipe |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Agra, Agostinho Requejo, Cristina Rodrigues, Filipe |
dc.subject.por.fl_str_mv |
Inventory routing Stochastic programming Sample average approximation algorithm Hybrid heuristic Demand uncertainty Iterated local search Adaptive heuristic |
topic |
Inventory routing Stochastic programming Sample average approximation algorithm Hybrid heuristic Demand uncertainty Iterated local search Adaptive heuristic |
description |
We consider a stochastic single item production-inventory-routing problem with a single producer, multiple clients, and multiple vehicles. At the clients, demand is allowed to be backlogged incurring a penalty cost. Demands are considered uncertain. A recourse model is presented, and valid inequalities are introduced to enhance the model. A new general approach that explores the sample average approximation (SAA) method is introduced. In the sample average approximation method, several sample sets are generated and solved independently in order to obtain a set of candidate solutions. Then, the candidate solutions are tested on a larger sample, and the best solution is selected among the candidates. In contrast to this approach, called static, we propose an adjustable approach that explores the candidate solutions in order to identify common structures. Using that information, part of the first-stage decision variables is fixed, and the resulting restricted problem is solved for a larger size sample. Several heuristic algorithms based on the mathematical model are considered within each approach. Computational tests based on randomly generated instances are conducted to test several variants of the two approaches. The results show that the new adjustable SAA heuristic performs better than the static one for most of the instances. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-07 2018-07-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10773/24196 |
url |
http://hdl.handle.net/10773/24196 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
0028-3045 10.1002/net.21796 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Wiley |
publisher.none.fl_str_mv |
Wiley |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
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1799137633887059968 |