On-Time Delivery in Crowdshipping Systems: An Agent-Based Approach Using Streaming Data

In parcel delivery, the "last mile" from the parcel hub to the customer is costly, especially for time-sensitive delivery tasks that have to be completed within hours after arrival. Recently, crowdshipping has attracted increased attention as a new alternative to traditional delivery modes...

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Detalles Bibliográficos
Autores: Dötterl, Jeremias, Bruns, Ralf, Dunkel, Jürgen, Ossowski, Sascha
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad Rey Juan Carlos
Repositorio:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
OAI Identifier:oai:burjcdigital.urjc.es:10115/26984
Acceso en línea:https://hdl.handle.net/10115/26984
Access Level:acceso abierto
Palabra clave:crowdsourcing
data stream learning
multiagent systems
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spelling On-Time Delivery in Crowdshipping Systems: An Agent-Based Approach Using Streaming DataDötterl, JeremiasBruns, RalfDunkel, JürgenOssowski, Saschacrowdsourcingdata stream learningmultiagent systemsIn parcel delivery, the "last mile" from the parcel hub to the customer is costly, especially for time-sensitive delivery tasks that have to be completed within hours after arrival. Recently, crowdshipping has attracted increased attention as a new alternative to traditional delivery modes. In crowdshipping, private citizens ("the crowd") perform short detours in their daily lives to contribute to parcel delivery in exchange for small incentives. However, achieving desirable crowd behavior is challenging as the crowd is highly dynamic and consists of autonomous, self-interested individuals. Leveraging crowdshipping for time-sensitive deliveries remains an open challenge. In this paper, we present an agent-based approach to on-time parcel delivery with crowds. Our system performs data stream processing on the couriers' smartphone sensor data to predict delivery delays. Whenever a delay is predicted, the system attempts to forge an agreement for transferring the parcel from the current deliverer to a more promising courier nearby. Our experiments show that through accurate delay predictions and purposeful task transfers many delays can be prevented that would occur without our approach.IOS Press202320232020info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/10115/26984reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlosinstname:Universidad Rey Juan CarlosInglésAttribution-NonCommercial 4.0 Internationalhttps://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccessoai:burjcdigital.urjc.es:10115/269842026-06-24T12:48:17Z
dc.title.none.fl_str_mv On-Time Delivery in Crowdshipping Systems: An Agent-Based Approach Using Streaming Data
title On-Time Delivery in Crowdshipping Systems: An Agent-Based Approach Using Streaming Data
spellingShingle On-Time Delivery in Crowdshipping Systems: An Agent-Based Approach Using Streaming Data
Dötterl, Jeremias
crowdsourcing
data stream learning
multiagent systems
title_short On-Time Delivery in Crowdshipping Systems: An Agent-Based Approach Using Streaming Data
title_full On-Time Delivery in Crowdshipping Systems: An Agent-Based Approach Using Streaming Data
title_fullStr On-Time Delivery in Crowdshipping Systems: An Agent-Based Approach Using Streaming Data
title_full_unstemmed On-Time Delivery in Crowdshipping Systems: An Agent-Based Approach Using Streaming Data
title_sort On-Time Delivery in Crowdshipping Systems: An Agent-Based Approach Using Streaming Data
dc.creator.none.fl_str_mv Dötterl, Jeremias
Bruns, Ralf
Dunkel, Jürgen
Ossowski, Sascha
author Dötterl, Jeremias
author_facet Dötterl, Jeremias
Bruns, Ralf
Dunkel, Jürgen
Ossowski, Sascha
author_role author
author2 Bruns, Ralf
Dunkel, Jürgen
Ossowski, Sascha
author2_role author
author
author
dc.subject.none.fl_str_mv crowdsourcing
data stream learning
multiagent systems
topic crowdsourcing
data stream learning
multiagent systems
description In parcel delivery, the "last mile" from the parcel hub to the customer is costly, especially for time-sensitive delivery tasks that have to be completed within hours after arrival. Recently, crowdshipping has attracted increased attention as a new alternative to traditional delivery modes. In crowdshipping, private citizens ("the crowd") perform short detours in their daily lives to contribute to parcel delivery in exchange for small incentives. However, achieving desirable crowd behavior is challenging as the crowd is highly dynamic and consists of autonomous, self-interested individuals. Leveraging crowdshipping for time-sensitive deliveries remains an open challenge. In this paper, we present an agent-based approach to on-time parcel delivery with crowds. Our system performs data stream processing on the couriers' smartphone sensor data to predict delivery delays. Whenever a delay is predicted, the system attempts to forge an agreement for transferring the parcel from the current deliverer to a more promising courier nearby. Our experiments show that through accurate delay predictions and purposeful task transfers many delays can be prevented that would occur without our approach.
publishDate 2020
dc.date.none.fl_str_mv 2020
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/10115/26984
url https://hdl.handle.net/10115/26984
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv Attribution-NonCommercial 4.0 International
https://creativecommons.org/licenses/by-nc/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution-NonCommercial 4.0 International
https://creativecommons.org/licenses/by-nc/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv IOS Press
publisher.none.fl_str_mv IOS Press
dc.source.none.fl_str_mv reponame:BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
instname:Universidad Rey Juan Carlos
instname_str Universidad Rey Juan Carlos
reponame_str BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
collection BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,811543

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