A Machine Learning Approach to Daily Capacity Planning in E-Commerce Logistics
7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021
; 13164 LNCS:45-50, 2022.
Article
in English
| Scopus | ID: covidwho-1729252
ABSTRACT
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. © 2022, Springer Nature Switzerland AG.
Capacity planning; Continual learning; E-commerce logistics; Gaussian process regression; Transportation; Docks; Electronic commerce; Gaussian distribution; Intelligent systems; Machine learning; E- commerces; E-commerce logistic; Effective capacity; Machine learning approaches; On-time delivery; Timeslots; Transportation system; Gaussian noise (electronic)
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
7th International Conference on Machine Learning, Optimization, and Data Science, LOD 2021
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS