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Found 2 papers, 1 papers with code
Self-Labeling the Job Shop Scheduling Problem
Inspired by Semi- and Self-Supervised learning, we show that it is possible to easily train generative models by sampling multiple solutions and using the best one according to the problem objective as a pseudo-label.
Learning the Quality of Machine Permutations in Job Shop Scheduling
Finally, we empirically demonstrate the value of predicting the quality of machine permutations by enhancing the performance of a simple Tabu Search algorithm inspired by the works in the literature.
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