Deep reinforcement learning‐based balancing and sequencing approach for mixed model assembly lines-Reference-Cited by-同舟云学术

Deep reinforcement learning‐based balancing and sequencing approach for mixed model assembly lines

Published:2022-08-31 Issue:3 Volume:4 Page:181-193
ISSN:2516-8398
Container-title:IET Collaborative Intelligent Manufacturing
language:en
Short-container-title:IET Collab Intel Manufact

Author:

Lv Youlong¹²,Tan Yuanliang³^ORCID,Zhong Ray⁴,Zhang Peng¹²,Wang Junliang¹²,Zhang Jie¹²^ORCID

Affiliation:

1. Institute of Artificial Intelligence Donghua University Shanghai China

2. Shanghai Engineering Research Center of Industrial Big Data and Intelligent System Shanghai China

3. College of Mechanical Engineering Donghua University Shanghai China

4. Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong Hong Kong China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Artificial Intelligence,Industrial and Manufacturing Engineering,Computer Science Applications,Hardware and Architecture

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/cim2.12061

Reference27 articles.

1. Master production scheduling and sequencing at mixed-model assembly lines in the automotive industry

2. Workforce minimization for a mixed-model assembly line in the automotive industry

3. A multi‐objective cuckoo search algorithm based on the record matrix for a mixed‐model assembly line car‐sequencing problem;Wang H.;IEEE Access,2020

4. Modeling and solving the mixed-model sequencing problem to improve productivity

5. Integrating ergonomic risks evaluation through OCRA index and balancing/sequencing decisions for mixed model stochastic asynchronous assembly lines

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