Abstract
Nowadays, transportation is a critical sector of our lives, not only for the movement of people, but also to be capable to move goods around the world. Although providing such services can be seen as a very tiny problem in our society, behind it, there is a complex sector that requires sophisticated models and specific software to analyse a vast amount of information coming from different sources in order to provide a sustainable and efficient service. Given such a complex field, various issues have come up like the search of optimised routes, efficient assignment of vehicles, reduction of gas emissions, cost optimization problems, etc. In most cases, the provided approaches are focused on addressing the optimization problem considering fleets with identical features. In this work, we present HVSRec, a heterogeneous fleet semantic recommender system that integrates mechanisms to manage vehicles of different nature and characteristics efficiently. The platform is aimed to connect customers that request a transportation of a certain good with drivers that are offering transportation services with their own vehicle.
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Acknowledgments
Work partially supported by the Autonomous Region of Madrid (grant “MOSI-AGIL-CM” (S2013/ICE-3019) co-funded by EU Structural Funds FSE and FEDER), project “SURF” (TIN2015-65515-C4-4-R (MINECO/FEDER)) funded by the Spanish Ministry of Economy and Competitiveness, and through the Excellence Research Group GES2ME (Ref. 30VCPIGI05) co-funded by URJC and Santander Bank.
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Rodríguez-García, M.Á., Fernández, A., Billhardt, H. (2018). Provider Recommendation in Heterogeneous Transportation Fleets. In: Bajo, J., et al. Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. PAAMS 2018. Communications in Computer and Information Science, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-319-94779-2_36
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