A machine learning framework for explainable knowledge mining and production, maintenance, and quality control optimization in flexible circular manufacturing systems

Georgios K. Koulinas (), Panagiotis D. Paraschos and Dimitrios E. Koulouriotis
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Georgios K. Koulinas: Democritus University of Thrace
Panagiotis D. Paraschos: Democritus University of Thrace
Dimitrios E. Koulouriotis: National Technical University of Athen

Flexible Services and Manufacturing Journal, 2024, vol. 36, issue 3, No 3, 737-759

Abstract: Abstract In the present study, we employed multiple decision tree algorithms to categorize cases and reflect the most efficient policies constructed by a reinforcement learning algorithm. These approaches treated a complex production, maintenance, and quality control optimization problem within a degrading manufacturing and remanufacturing system. The decision trees’ nodes represent the independent variables, while the trees’ leaves represent the set of function values. The reinforcement learning method revealed all optimization parameters and best policies, which were employed as the training sample for the tree algorithms. After constructing every decision tree, each resulting decision rule was used to solve the optimization problem, and its performance was assessed. Additionally, we performed a sensitivity analysis to determine if the pruning level impacts the objective function value and, generally, the effectiveness of the proposed approach.

Keywords: Decision trees; Reinforcement learning; Manufacturing/remanufacturing systems; Data mining (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10696-024-09537-x

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