A machine learning approach to daily capacity planning in e-commerce logistics

Bayram, Barış and Ülkü, Büşra and Aydın, Gözde and Akhavan, Raha and Bozkaya, Burçin (2022) A machine learning approach to daily capacity planning in e-commerce logistics. In: 7th International Conference on Machine Learning, Optimization, and Data Science (LOD), Virtual, Online

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Due to the accelerated activity in e-commerce especially since the COVID-19 outbreak, the congestion in the transportation systems is continually increasing, which affects on-time delivery of regular parcels and groceries. An important constraint is the fact that a given number of delivery drivers have a limited amount of time and daily capacity, leading to the need for effective capacity planning. In this paper, we employ a Gaussian Process Regression (GPR) approach to predict the daily delivery capacity of a fleet starting their routes from a cross-dock depot and for a specific time slot. Each prediction specifies how many deliveries in total the drivers in a given cross-dock can make for a certain time-slot of the day. Our results show that the GPR model outperforms other state-of-the-art regression methods. We also improve our model by updating it daily using shipments delivered within the day, in response to unexpected events during the day, as well as accounting for special occasions like Black Friday or Christmas.
Item Type: Papers in Conference Proceedings
Uncontrolled Keywords: Capacity planning; Continual learning; E-commerce logistics; Gaussian process regression; Transportation
Divisions: Sabancı Business School > Operations Management and Information Systems
Sabancı Business School
Depositing User: Raha Akhavan
Date Deposited: 17 Aug 2022 11:47
Last Modified: 17 Aug 2022 11:48
URI: https://research.sabanciuniv.edu/id/eprint/43242

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