University of Twente Student Theses
Improving real-time decision-making in the last-mile delivery by applying a classification model
Zwienenberg, I.B. (2022) Improving real-time decision-making in the last-mile delivery by applying a classification model.
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| Abstract: | This research proposes a machine learning algorithm that improves the decision making in the realtime monitoring of the parcel delivery and pick-up rides of a large mail- and parcel provider in the Netherlands. By using a Random Forest model that uses events that are received as input data in real-time, the model is able to predict, for each active ride, whether or not it requires a reschedule, based on real-time events that are received until then. In this context, events are occurrences during the rides, like a driver finishing a stop or starting the ride. The predictions enable employees from the Control Room that are responsible for the monitoring of the rides to make quicker and better decisions. |
| Item Type: | Essay (Master) |
| Clients: | Cape Groep |
| Faculty: | BMS: Behavioural, Management and Social Sciences |
| Subject: | 85 business administration, organizational science |
| Programme: | Industrial Engineering and Management MSc (60029) |
| Link to this item: | https://purl.utwente.nl/essays/90582 |
| Export this item as: | BibTeX EndNote HTML Citation Reference Manager |
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