Using districting and a data driven TSP to improve last mile delivery
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Tipo de documento: | Dissertação |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.22/23877 |
Resumo: | This dissertation considers how a parcel delivery company can improve last mile delivery services’ performance using historical data. To tackle this challenge, we start by proposing a framework for data cleaning, in order to produce reliable data for vehicle routing problems. Data on the historical geographical location of clients is used to model a hierarchical districting problem, mid-level districts (named micro districts) are limited to an eight hour shift, representing a daily route. Using the districting solution as a procedure for package to driver/vehicle assignment, it is possible to achieve a 14% decrease in the number of vehicles needed, while keeping daily routes more balanced in terms of working times. Using a transition probabilities based TSP to sequence nano zones (the lower-level districts), the preferences of drivers are used as a cost function. The transition probabilities based TSP produces solutions with a total distance 12% higher, comparing with a distance based TSP. Moreover, sequencing the nano zones using the maximum likelihood routing enables the incorporation of the driver’s tacit knowledge. |
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Using districting and a data driven TSP to improve last mile deliveryDistrictingData DrivenTSPMaximum Likelihood RoutingThis dissertation considers how a parcel delivery company can improve last mile delivery services’ performance using historical data. To tackle this challenge, we start by proposing a framework for data cleaning, in order to produce reliable data for vehicle routing problems. Data on the historical geographical location of clients is used to model a hierarchical districting problem, mid-level districts (named micro districts) are limited to an eight hour shift, representing a daily route. Using the districting solution as a procedure for package to driver/vehicle assignment, it is possible to achieve a 14% decrease in the number of vehicles needed, while keeping daily routes more balanced in terms of working times. Using a transition probabilities based TSP to sequence nano zones (the lower-level districts), the preferences of drivers are used as a cost function. The transition probabilities based TSP produces solutions with a total distance 12% higher, comparing with a distance based TSP. Moreover, sequencing the nano zones using the maximum likelihood routing enables the incorporation of the driver’s tacit knowledge.Ramos, António José GalrãoRepositório Científico do Instituto Politécnico do PortoSantos, Beatriz Barbosa dos20232024-10-10T00:00:00Z2023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.22/23877TID:203380304enginfo:eu-repo/semantics/embargoedAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-15T01:47:50Zoai:recipp.ipp.pt:10400.22/23877Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:42:34.067400Repositório 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 |
Using districting and a data driven TSP to improve last mile delivery |
title |
Using districting and a data driven TSP to improve last mile delivery |
spellingShingle |
Using districting and a data driven TSP to improve last mile delivery Santos, Beatriz Barbosa dos Districting Data Driven TSP Maximum Likelihood Routing |
title_short |
Using districting and a data driven TSP to improve last mile delivery |
title_full |
Using districting and a data driven TSP to improve last mile delivery |
title_fullStr |
Using districting and a data driven TSP to improve last mile delivery |
title_full_unstemmed |
Using districting and a data driven TSP to improve last mile delivery |
title_sort |
Using districting and a data driven TSP to improve last mile delivery |
author |
Santos, Beatriz Barbosa dos |
author_facet |
Santos, Beatriz Barbosa dos |
author_role |
author |
dc.contributor.none.fl_str_mv |
Ramos, António José Galrão Repositório Científico do Instituto Politécnico do Porto |
dc.contributor.author.fl_str_mv |
Santos, Beatriz Barbosa dos |
dc.subject.por.fl_str_mv |
Districting Data Driven TSP Maximum Likelihood Routing |
topic |
Districting Data Driven TSP Maximum Likelihood Routing |
description |
This dissertation considers how a parcel delivery company can improve last mile delivery services’ performance using historical data. To tackle this challenge, we start by proposing a framework for data cleaning, in order to produce reliable data for vehicle routing problems. Data on the historical geographical location of clients is used to model a hierarchical districting problem, mid-level districts (named micro districts) are limited to an eight hour shift, representing a daily route. Using the districting solution as a procedure for package to driver/vehicle assignment, it is possible to achieve a 14% decrease in the number of vehicles needed, while keeping daily routes more balanced in terms of working times. Using a transition probabilities based TSP to sequence nano zones (the lower-level districts), the preferences of drivers are used as a cost function. The transition probabilities based TSP produces solutions with a total distance 12% higher, comparing with a distance based TSP. Moreover, sequencing the nano zones using the maximum likelihood routing enables the incorporation of the driver’s tacit knowledge. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023 2023-01-01T00:00:00Z 2024-10-10T00:00:00Z |
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 |
http://hdl.handle.net/10400.22/23877 TID:203380304 |
url |
http://hdl.handle.net/10400.22/23877 |
identifier_str_mv |
TID:203380304 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/embargoedAccess |
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embargoedAccess |
dc.format.none.fl_str_mv |
application/pdf |
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 |
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1799134990828568576 |