Acquisition of business intelligence from human experience in route planning
EntidadUAM. Departamento de Ingeniería Informática
EditorTaylor & Francis
Fecha de edición2015-04
10.1080/17517575.2012.759279Enterprise Information Systems 9.3 (2015): 303-323
ISSN1751-7575 (print); 1751-7583 (online)
Financiado porThis 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 editorhttp://dx.doi.org/10.1080/17517575.2012.759279
MateriasLogistics; Business Intelligence; Route optimization; Case-Based Reasoning; Genetic Algorithms; Applied AI; Information Systems; Informática
NotaThis 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/10.1080/17517575.2012.759279
Derechos© 2015 Taylor & Francis Group
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.
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