Multi-objective vehicle routing with automated negotiation

This paper investigates a problem that lies at the intersection of three research areas, namely automated negotiation, vehicle routing, and multi-objective optimization. Specifically, it investigates the scenario that multiple competing logistics companies aim to cooperate by delivering truck loads...

Descripción completa

Detalles Bibliográficos
Autores: De Jonge, Dave, Bistaffa, Filippo, Levy, Jordi
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/304467
Acceso en línea:http://hdl.handle.net/10261/304467
Access Level:acceso abierto
Palabra clave:Vehicle routing problem
Automated negotiation
Multi-objective optimization
Logistics
Horizontal collaboration
id ES_fb7ea490b1ea0a324300b87f494e152c
oai_identifier_str oai:digital.csic.es:10261/304467
network_acronym_str ES
network_name_str España
repository_id_str
spelling Multi-objective vehicle routing with automated negotiationDe Jonge, DaveBistaffa, FilippoLevy, JordiVehicle routing problemAutomated negotiationMulti-objective optimizationLogisticsHorizontal collaborationThis paper investigates a problem that lies at the intersection of three research areas, namely automated negotiation, vehicle routing, and multi-objective optimization. Specifically, it investigates the scenario that multiple competing logistics companies aim to cooperate by delivering truck loads for one another, in order to improve efficiency and reduce the distance they drive. In order to do so, these companies need to find ways to exchange their truck loads such that each of them individually benefits. We present a new heuristic algorithm that, given one set of orders for each company, tries to find the set of all truck load exchanges that are Pareto-optimal and individually rational. Unlike existing approaches, it does this without relying on any kind of trusted central server, so the companies do not need to disclose their private cost models to anyone. The idea is that the companies can then use automated negotiation techniques to negotiate which of these truck load exchanges will truly be carried out. Furthermore, this paper presents a new, multi-objective, variant of And/Or search that forms part of our approach, and it presents experiments based on real-world data, as well as on the commonly used Li & Lim data set. These experiments show that our algorithm is able to find hundreds of solutions within a matter of minutes. Finally, this paper presents an experiment with several state-of-the-art negotiation algorithms to show that the combination of our search algorithm with automated negotiation is viable.Kluwer Academic PublishersConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]2023202320222023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/304467reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttp://dx.doi.org/10.1007/s10489-022-03329-2Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3044672026-05-22T06:33:51Z
dc.title.none.fl_str_mv Multi-objective vehicle routing with automated negotiation
title Multi-objective vehicle routing with automated negotiation
spellingShingle Multi-objective vehicle routing with automated negotiation
De Jonge, Dave
Vehicle routing problem
Automated negotiation
Multi-objective optimization
Logistics
Horizontal collaboration
title_short Multi-objective vehicle routing with automated negotiation
title_full Multi-objective vehicle routing with automated negotiation
title_fullStr Multi-objective vehicle routing with automated negotiation
title_full_unstemmed Multi-objective vehicle routing with automated negotiation
title_sort Multi-objective vehicle routing with automated negotiation
dc.creator.none.fl_str_mv De Jonge, Dave
Bistaffa, Filippo
Levy, Jordi
author De Jonge, Dave
author_facet De Jonge, Dave
Bistaffa, Filippo
Levy, Jordi
author_role author
author2 Bistaffa, Filippo
Levy, Jordi
author2_role author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Vehicle routing problem
Automated negotiation
Multi-objective optimization
Logistics
Horizontal collaboration
topic Vehicle routing problem
Automated negotiation
Multi-objective optimization
Logistics
Horizontal collaboration
description This paper investigates a problem that lies at the intersection of three research areas, namely automated negotiation, vehicle routing, and multi-objective optimization. Specifically, it investigates the scenario that multiple competing logistics companies aim to cooperate by delivering truck loads for one another, in order to improve efficiency and reduce the distance they drive. In order to do so, these companies need to find ways to exchange their truck loads such that each of them individually benefits. We present a new heuristic algorithm that, given one set of orders for each company, tries to find the set of all truck load exchanges that are Pareto-optimal and individually rational. Unlike existing approaches, it does this without relying on any kind of trusted central server, so the companies do not need to disclose their private cost models to anyone. The idea is that the companies can then use automated negotiation techniques to negotiate which of these truck load exchanges will truly be carried out. Furthermore, this paper presents a new, multi-objective, variant of And/Or search that forms part of our approach, and it presents experiments based on real-world data, as well as on the commonly used Li & Lim data set. These experiments show that our algorithm is able to find hundreds of solutions within a matter of minutes. Finally, this paper presents an experiment with several state-of-the-art negotiation algorithms to show that the combination of our search algorithm with automated negotiation is viable.
publishDate 2022
dc.date.none.fl_str_mv 2022
2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/304467
url http://hdl.handle.net/10261/304467
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv http://dx.doi.org/10.1007/s10489-022-03329-2
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Kluwer Academic Publishers
publisher.none.fl_str_mv Kluwer Academic Publishers
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869425333880487936
score 15,81155

Similares en LA Referencia