Optimisation of remanufacturing supply chain with dual recycling channels under improved deep reinforcement learning algorithm-Reference-Cited by-同舟云学术

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Optimisation of remanufacturing supply chain with dual recycling channels under improved deep reinforcement learning algorithm

Published:2024-08-28 Issue:1 Volume:11 Page:
ISSN:2330-2674
Container-title:International Journal of Systems Science: Operations & Logistics
language:en
Short-container-title:International Journal of Systems Science: Operations & Logistics

Author:

Wang Zhen¹^ORCID,Ye Chunming¹,Guo Jianquan¹^ORCID

Affiliation:

1. School of Management, University of Shanghai for Science and Technology, Shanghai, People’s Republic of China

Funder

Shanghai Planning Office of Philosophy and Social Science under Grants

National Natural Science Foundation of China

Scientific and Innovative Action Plan of Shanghai under Grants

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/23302674.2024.2396432

Reference80 articles.

1. Interactive Pythagorean-hesitant fuzzy computational algorithm for multiobjective transportation problem under uncertainty

2. Robust neutrosophic programming approach for solving intuitionistic fuzzy multiobjective optimization problems

3. Ahmad F. Adhami A. Y. & Smarandache F. (2020). Modified neutrosophic fuzzy optimization model for optimal closed-loop supply chain management under uncertainty. Optimization Theory Based on Neutrosophic and Plithogenic Sets. Academic Press 343–403.

4. Supply chain behaviour under carbon regulations: an experimental study with system dynamic simulation

5. Reverse Supply Chain Network with Return Products Quality Consideration

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