Acquisition of business intelligence from human experience in route planning
Entidad
UAM. Departamento de Ingeniería InformáticaEditor
Taylor & FrancisFecha de edición
2015-04Cita
10.1080/17517575.2012.759279
Enterprise Information Systems 9.3 (2015): 303-323
ISSN
1751-7575 (print); 1751-7583 (online)DOI
10.1080/17517575.2012.759279Financiado por
This work has been partially supported by the SpanishMinistry of Science and Innovation under the projects ABANT (TIN 2010-19872) and by Jobssy.com company under Project FUAM-076913.Versión del editor
http://dx.doi.org/10.1080/17517575.2012.759279Materias
Logistics; Business Intelligence; Route optimization; Case-Based Reasoning; Genetic Algorithms; Applied AI; Information Systems; InformáticaNota
This is an Accepted Manuscript of an article published by Taylor & Francis Group in Enterprise Information Systems on 2015, available online at:http://www.tandfonline.com/10.1080/17517575.2012.759279Derechos
© 2015 Taylor & Francis GroupResumen
The logistic sector raises a number of highly challenging problems. Probably
one of the most important ones is the shipping planning, i.e., plan the routes
that the shippers have to follow to deliver the goods. In this paper we present
an AI-based solution that has been designed to help a logistic company to improve
its routes planning process. In order to achieve this goal, the solution uses
the knowledge acquired by the company drivers to propose optimized routes.
Hence, the proposed solution gathers the experience of the drivers, processes it
and optimizes the delivery process. The solution uses Data Mining to extract
knowledge from the company information systems and prepares it for analysis
with a Case-Based Reasoning (CBR) algorithm. The CBR obtains critical
business intelligence knowledge from the drivers experience that is needed by
the planner. The design of the routes is done by a Genetic Algorithm (GA)
that, given the processed information, optimizes the routes following several
objectives, such as minimize the distance or time. Experimentation shows that
the proposed approach is able to find routes that improve, in average, the
routes made by the human experts.
Lista de ficheros
Google Scholar:Bello Orgaz, Gema
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Barrero, David F.
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R-Moreno, María Dolores
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Camacho, David
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