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Human-Centric Parcel Delivery at Deutsche Post with Operations Research and Machine Learning

Author

Listed:
  • Uğur Arıkan

    (DHL Data & Analytics, Deutsche Post DHL Group, 53113 Bonn, Germany)

  • Thorsten Kranz

    (DHL Data & Analytics, Deutsche Post DHL Group, 53113 Bonn, Germany)

  • Baris Cem Sal

    (DHL Data & Analytics, Deutsche Post DHL Group, 53113 Bonn, Germany)

  • Severin Schmitt

    (DHL Data & Analytics, Deutsche Post DHL Group, 53113 Bonn, Germany)

  • Jonas Witt

    (DHL Data & Analytics, Deutsche Post DHL Group, 53113 Bonn, Germany)

Abstract

Features such as estimated delivery time windows and live tracking of shipments play a key role in improving the customer experience in last-mile delivery. The building blocks for enabling these features are reliable knowledge about the expected order of deliveries in a tour and precise delivery time window predictions. For Deutsche Post’s parcel delivery service in Germany, we developed a courier-centric routing algorithm and a corresponding state-of-the-art machine learning model for delivery time window predictions. The routing algorithm combines operations research with statistics and machine learning to implicitly gather and use the tacit knowledge of our experienced couriers within the tour generation. This is achieved by deducing and selecting appropriate precedence constraints from historical delivery data. This novel combination of optimization with data-driven constraints enabled us to provide custom routes to the individual couriers. It proved to be a main driver allowing us to provide accurate delivery time window predictions and live tracking of shipments. Our solution is used by Deutsche Post to plan the daily routes of couriers to the approximately 13,000 parcel delivery districts in Germany as well as to provide live tracking and estimated delivery time windows for 1.6 million parcels each day.

Suggested Citation

  • Uğur Arıkan & Thorsten Kranz & Baris Cem Sal & Severin Schmitt & Jonas Witt, 2023. "Human-Centric Parcel Delivery at Deutsche Post with Operations Research and Machine Learning," Interfaces, INFORMS, vol. 53(5), pages 359-371, September.
    • Handle: RePEc:inm:orinte:v:53:y:2023:i:5:p:359-371
    DOI: 10.1287/inte.2023.0031
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    References listed on IDEAS

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    1. Quirion-Blais, Olivier & Chen, Lu, 2021. "A case-based reasoning approach to solve the vehicle routing problem with time windows and drivers’ experience," Omega, Elsevier, vol. 102(C).
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    Cited by:

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    2. Rosanne Larocque & Anne-Marie Boulé & Quentin Cappart, 2025. "Estimating Road Construction Costs with Explainable Machine Learning," Interfaces, INFORMS, vol. 55(2), pages 137-153, March.

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