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An Intelligent Decision Support System for Road Freight Transport

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Intelligent Data Engineering and Automated Learning – IDEAL 2022 (IDEAL 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13756))

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Abstract

This paper presents an Intelligent Decision Support System (IDSS) to optimize transport and logistics activities in a set of Portuguese companies currently operating in the freight transport sector. This IDSS comprises three main modules that can be used individually or chained together, dedicated to: a geographic clustering detection of transport services; a transport driver suggestion; and a route and truckload optimization. The IDSS was entirely designed and developed to support real-time data and it consists of an end-to-end solution (E2ES), given that it covers all the main transport and logistics processes since the registration in the database to the optimized transport plan. The entire set of functionalities inserted in the IDSS was designed and validated by freight transport sector experts from the different companies that will use the proposed system.

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Notes

  1. 1.

    https://scikit-learn.org/stable/index.html.

  2. 2.

    https://developer.myptv.com/.

  3. 3.

    Publicly available https://github.com/hugodscarvalho/rancoord.

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Acknowledgment

The authors would like to express the most significant recognition to the project on which this IDSS has arisen, “aDyTrans - Dynamic Transportations Platform” reference NORTE-01-0247-FEDER-045174, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).

Author information

Authors and Affiliations

  1. EPMQ, CCG ZGDV Institute, Guimarães, Portugal

    Hugo Silva Carvalho, André Pilastri & Arthur Matta

  2. ABMN - Business Solutions, Azurém, Guimarães, Portugal Guimarães, Portugal

    Rui Novais

  3. ALGORITMI Research Centre/LASI, Department of Information Systems, University of Minho, Guimarães, Portugal

    Luís Miguel Matos & Paulo Cortez

Authors
  1. Hugo Silva Carvalho
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  2. André Pilastri
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  3. Arthur Matta
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  4. Luís Miguel Matos
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  5. Rui Novais
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  6. Paulo Cortez
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Corresponding author

Correspondence to Paulo Cortez .

Editor information

Editors and Affiliations

  1. University of Manchester, Manchester, UK

    Hujun Yin

  2. Technical University of Madrid, Madrid, Spain

    David Camacho

  3. University of Birmingham, Birmingham, UK

    Peter Tino

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Carvalho, H.S., Pilastri, A., Matta, A., Matos, L.M., Novais, R., Cortez, P. (2022). An Intelligent Decision Support System for Road Freight Transport. In: Yin, H., Camacho, D., Tino, P. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2022. IDEAL 2022. Lecture Notes in Computer Science, vol 13756. Springer, Cham. https://doi.org/10.1007/978-3-031-21753-1_15

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  • DOI: https://doi.org/10.1007/978-3-031-21753-1_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-21752-4

  • Online ISBN: 978-3-031-21753-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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