Order dispatching for an ultra-fast delivery service via deep reinforcement learning

Eray Mert Kavuk*, Ayse Tosun, Mucahit Cevik, Aysun Bozanta, Sibel B. Sonuç, Mehmetcan Tutuncu, Bilgin Kosucu, Ayse Basar

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Dergiye katkıMakalebilirkişi

6 Atıf (Scopus)

Özet

This paper proposes a real-life application of deep reinforcement learning to address the order dispatching problem of a Turkish ultra-fast delivery company, Getir. Before applying off-the-shelf reinforcement learning methods, we define the specific problem at Getir and one of the solutions the company has implemented. We discuss the novel aspects of Getir’s problem compared to the state-of-the-art order dispatching studies and highlight the limitations of Getir’s solution. The overall aim of the company is to deliver to as many customers as possible within 10 minutes. The orders arrive throughout the day, and centralized warehouses in the regions decide whether an incoming order should be served or canceled depending on their couriers’ shifts and status. We use Deep Q-networks to learn the actions of warehouses, i.e., accepting or canceling an order, directly from state dimensions using reinforcement learning. We design the networks with two different rewards. We conduct empirical analyses using real-life data provided by Getir to generate training samples and to assess the models’ performance during a selected 30-day period with a total of 9880 orders. The results indicate that our proposed models are able to generate policies that outperform the rule-based heuristic employed in practice.

Orijinal dilİngilizce
Sayfa (başlangıç-bitiş)4274-4299
Sayfa sayısı26
DergiApplied Intelligence
Hacim52
Basın numarası4
DOI'lar
Yayın durumuYayınlandı - Mar 2022

Bibliyografik not

Publisher Copyright:
© 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Finansman

This research is partly funded by Getir Perakende Lojistik A.S., Istanbul, Turkey.

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