Automation of expert decisions in delayed line haul deliveries: an application of the Behavioural Artificial Intelligence Technology

Master Thesis (2022)
Author(s)

J.A. Smeets (TU Delft - Civil Engineering & Geosciences)

Contributor(s)

LA Tavaszzy – Mentor (TU Delft - Transport and Planning)

A.J. van Binsbergen – Mentor (TU Delft - Transport and Planning)

A. Nadi Najafabadi – Mentor (TU Delft - Transport and Planning)

Caspar G. Chorus – Mentor (TU Delft - Industrial Design Engineering)

Faculty
Civil Engineering & Geosciences
Copyright
© 2022 Joost Smeets
More Info
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Publication Year
2022
Language
English
Copyright
© 2022 Joost Smeets
Graduation Date
21-11-2022
Awarding Institution
Delft University of Technology
Programme
['Transport, Infrastructure and Logistics']
Sponsors
Councyl
Faculty
Civil Engineering & Geosciences
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Abstract

Making decisions regarding delayed line haul transport is a very demanding and complex process in E-commerce distribution centres. Automating this process can decrease decision-maker discussion and assessment time and, as a result, allow decision-makers to spend more time on other demanding tasks like sorting and distributing. Automation in this domain increases on-time deliveries and strengthens the E-commerce firms’ competitive position. However, such decisions involve experts’ knowledge, discussions among planners, and complex thought processes. Therefore, it is necessary to involve planners’ inclinations and preferences to automate their decisions. This paper proposes the Behavioural Artificial Intelligence Technology (BAIT) to automate expert decisions in delayed line haul deliveries. BAIT uses fundamental Discrete Choice theory under the hood to capture expert preferences effectively, and incorporates these into a decision-making tool. We use this method in a case study to replicate expert decisions in delayed line haul deliveries at DHL Express. The case study results show that BAIT can accurately replicate expert decisions.

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