Abstract:
The paper considers a multi-stage processing system including sets of identical (parallel) machines and a
set of dedicated machines processing different operations of the given jobs in any sectors of economy. Based on the
weighted Mixed Neuro graph model, the paper proposes adaptive algorithms for solving this problem via appropriate
Mixed Neuro graph transformations. The main novelty is (1) low demands on the source data-unlike classical machine
learning algorithms, the approach can offer stable interpretable results even with a short dataset size; (2) the number of
new matrix multiplication operations that make up the main load when training models increases linearly with the number
of new data from 0 to 999 time periods; (3) the results of the model are repeatable due to the stability of the coefficients
of the model. These algorithms are able to solve (exactly or heuristically) the tested instances with N jobs and W types
of parallel identical machines within on the personal computer. The gradient boosting result is in interval 5.9677410-
3.4982093.
