Multi-objective reinforcement learning-based framework for solving selective maintenance problems in reconfigurable cyber-physical manufacturing systems-Reference-Cited by-同舟云学术

Multi-objective reinforcement learning-based framework for solving selective maintenance problems in reconfigurable cyber-physical manufacturing systems

Published:2023-07-26 Issue: Volume: Page:1-23
ISSN:0020-7543
Container-title:International Journal of Production Research
language:en
Short-container-title:International Journal of Production Research

Author:

Achamrah Fatima Ezzahra¹,Attajer Ali²

Affiliation:

1. Centre for management science (CGS), i3 UMR9217 CNRS, Mines Paris, PSL University, Paris, France

2. LAMIH, CNRS, UMR 8201, Univ. Polytechnique Hauts-de-France, Valenciennes, France

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Strategy and Management

Link

https://www.tandfonline.com/doi/pdf/10.1080/00207543.2023.2240433

Reference59 articles.

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2. An Artificial-Immune-System-Based Algorithm Enhanced with Deep Reinforcement Learning for Solving Returnable Transport Item Problems

3. Branch-and-price algorithms for large-scale mission-oriented maintenance planning problems

4. Selective maintenance optimization: a condensed critical review and future research directions

5. Attajer, A., S. Darmoul, S. Chaabane, F. Riane, and Y. Sallez. 2021. “Benchmarking Simulation Software Capabilities Against Distributed Control Requirements: Flexsim vs Anylogic.” In Service Oriented, Holonic and Multi-agent Manufacturing Systems for Industry of the Future: Proceedings of SOHOMA 2020, 520–531. Springer.

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