Optimizing Automated Manufacturing Processes Using a Hybrid BRKGA Algorithm: A Case Study on Flexible Job-Shop Scheduling
Procedia Computer Science, ISSN: 1877-0509, Vol: 242, Page: 714-721
2024
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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Article Description
In the modern era, industrial automation ofers significant competitive advantages. Replacing human labor with robots allows processes to be carried out much faster and more efficiently, with lower waste rates and the possibility of scheduling production 24/7. However, this also presents new challenges and opportunities in optimizing these automated processes. This paper proposes a hybrid algorithm BRKGA (Biased Random Key Genetic Algorithm) to optimize fexible job shop scheduling (FJSSP) problems in automated manufacturing systems. The algorithm is applied to a real-world case study in the Cinvestav Intelligent Manufacturing Laboratory, seeking to optimize the production of a four-piece product. The results demonstrate that the proposed algorithm outperforms previous results in terms of both, solution quality and computational efficiency.
Bibliographic Details
Elsevier BV
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