A Machine Learning Approach to Daily Capacity Planning in E-Commerce Logistics-Reference-Cited by-同舟云学术

A Machine Learning Approach to Daily Capacity Planning in E-Commerce Logistics

Published:2022 Issue: Volume: Page:45-50
ISSN:0302-9743
Container-title:Machine Learning, Optimization, and Data Science
language:
Short-container-title:

Author:

Bayram Barış^ORCID,Ülkü Büşra^ORCID,Aydın Gözde^ORCID,Akhavan-Tabatabaei Raha^ORCID,Bozkaya Burcin^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-030-95470-3_4

Reference9 articles.

1. Viu-Roig, M., Alvarez-Palau, E.J.: The impact of E-commerce-related last-mile logistics on cities: a systematic literature review. Sustainability 12(16), 6492 (2020)

2. Ahamed, T., et al.: Deep Reinforcement Learning for Crowdsourced Urban Delivery: System States Characterization, Heuristics-guided Action Choice, and Rule-Interposing Integration. arXiv preprint arxiv:2011.14430 (2020)

3. Yildiz, B., Savelsbergh, M.: Service and capacity planning in crowd-sourced delivery. Transp. Res. Part C Emerg. Technol. 100, 177–199 (2019)

4. Moghe, R.P., Rathee, S., Nayak, B., Adusumilli, K.M.: Machine learning based batching prediction system for food delivery. In: 8th ACM IKDD CODS and 26th COMAD, pp. 316–322 (2021)

5. Fabusuyi, T., et al.: Estimating small area demand for online package delivery. J. Transp. Geogr. 88, 102864 (2020)

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