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Machine Learning Applications in Route Planning for Attended Home Delivery

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Artificial Intelligence and Machine Learning (BNAIC/Benelearn 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2187))

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Abstract

In the attended home delivery industry, customers order products online, which are then delivered to their home within a time slot of their choice. Given the nature of the products, e.g., groceries or appliances, the customer must be at home to receive the goods. Therefore, on-time delivery is of key importance. Due to the inherent variability of various planning factors, such as uncertain travel times and time to deliver an order, gaps appear between route planning and execution of the delivery routes. Additionally, during the planning process additional customers may order and choose new time slots. This fuels the need for data-driven approaches that can account for these uncertainties. In this paper, we present an overview of ORTEC applications that use machine learning combined with route planning, to create accurate, yet efficient and robust route plans. To validate the impact of the proposed approaches in practice, the machine learning models are trained and tested on real-world data. Simulation experiments highlight the impact and value created by such approaches for home delivery companies.

Wouter Merkx and Ruggiero Seccia — no longer work for ORTEC, Work done while at ORTEC.

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Authors and Affiliations

  1. ORTEC B.V., Houtsingel 5, 2719 EA, Zoetermeer, The Netherlands

    Thomas R. Visser, Ruggiero Seccia, Wouter Merkx, Wouter Kool & Leendert Kok

Authors
  1. Thomas R. Visser
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  2. Ruggiero Seccia
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  3. Wouter Merkx
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  4. Wouter Kool
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  5. Leendert Kok
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Corresponding author

Correspondence to Thomas R. Visser .

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Editors and Affiliations

  1. Delft University of Technology, Delft, The Netherlands

    Frans A. Oliehoek

  2. Delft University of Technology, Delft, The Netherlands

    Manon Kok

  3. Delft University of Technology, Delft, The Netherlands

    Sicco Verwer

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Disclosure of Interests

We acknowledge that all authors of this article are affiliated with ORTEC, which might introduce a potential conflict of interest. We have made every effort to ensure the objectivity and integrity of the research conducted and here reported. We want to highlight that most of the research reported was conducted in collaboration with universities, further ensuring the high quality and impartiality of the results reported.

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Visser, T.R., Seccia, R., Merkx, W., Kool, W., Kok, L. (2025). Machine Learning Applications in Route Planning for Attended Home Delivery. In: Oliehoek, F.A., Kok, M., Verwer, S. (eds) Artificial Intelligence and Machine Learning. BNAIC/Benelearn 2023. Communications in Computer and Information Science, vol 2187. Springer, Cham. https://doi.org/10.1007/978-3-031-74650-5_7

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  • DOI: https://doi.org/10.1007/978-3-031-74650-5_7

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  • Online ISBN: 978-3-031-74650-5

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