A hybrid deep reinforcement learning approach for a proactive transshipment of fresh food in the online–offline channel system-Reference-Cited by-同舟云学术

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A hybrid deep reinforcement learning approach for a proactive transshipment of fresh food in the online–offline channel system

Published:2024-07 Issue: Volume:187 Page:103576
ISSN:1366-5545
Container-title:Transportation Research Part E: Logistics and Transportation Review
language:en
Short-container-title:Transportation Research Part E: Logistics and Transportation Review

Author:

Lee Junhyeok,Shin Youngchul,Moon Ilkyeong^ORCID

Funder

National Research Foundation of Korea

Ministry of Science, ICT and Future Planning

Publisher

Elsevier BV

Reference55 articles.

1. Optimal joint replenishment and transshipment policies in a multi-period inventory system with lost sales;Abouee-Mehrizi;Oper. Res.,2015

2. Dynamic Programming and Optimal Control: Volume I;Bertsekas,2012

3. Deep reinforcement learning for inventory control: A roadmap;Boute;European J. Oper. Res.,2021

4. Joint inventory and transshipment control for perishable products of a two-period lifetime;Cheong;Int. J. Adv. Manuf. Technol.,2013

5. Soft actor-critic for discrete action settings;Christodoulou,2019

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. The Logistics of Volkswagen Development Center Applies Operations Research to Optimize Transshipments;Applied Sciences;2024-06-05

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