Optimisation of recovery policies in the era of supply chain disruptions: a system dynamics and reinforcement learning approach-Reference-Cited by-同舟云学术

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Optimisation of recovery policies in the era of supply chain disruptions: a system dynamics and reinforcement learning approach

Published:2024-08-06 Issue: Volume: Page:1-25
ISSN:0020-7543
Container-title:International Journal of Production Research
language:en
Short-container-title:International Journal of Production Research

Author:

Bussieweke Fabian¹^ORCID,Mula Josefa¹^ORCID,Campuzano-Bolarin Francisco²^ORCID

Affiliation:

1. Research Centre on Production Management and Engineering (CIGIP), Universitat Politècnica de València, Alcoy, Spain

2. Department of Business Economics, Universidad Politécnica de Cartagena, Cartagena, Spain

Funder

Valencian Regional Government

MCIN

Publisher

Informa UK Limited

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2024.2383293

Reference53 articles.

1. Alves J. C. and G. R. Mateus. 2022. “Multi-Echelon Supply Chains with Uncertain Seasonal Demands and Lead Times Using Deep Reinforcement Learning.” arXiv preprint arXiv:2201.04651.

2. Combining system dynamics and multi-objective optimization with design space reduction

3. Supply chain design and cost analysis through simulation

4. Supply Chain Simulation

5. Degris T. P. M. Pilarski and R. S. Sutton. 2012. “Model-Free Reinforcement Learning with Continuous Action in Practice.” In 2012 American Control Conference (ACC) 2177–2182. IEEE.

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