Multi-agent Environment for Decision-Support in Production Systems Using Machine Learning Methods

This paper presents a model and implementation of a multi-agent system to support decisions to optimize a production process in companies. Our goal is to choose the most desirable parameters of the technological process using computer simulation, which will help to avoid or reduce the number of much more expensive trial production processes, using physical production lines. These identified values of production process parameters will be applied later in a real mass production. Decision-making strategies are selected using different machine learning techniques that assist in obtaining products with the required parameters, taking into account sets of historical data. The focus was primarily on the analysis of the quality of prediction of the obtained product parameters for the different algorithms used and different sizes of historical data sets, and therefore different details of information, and secondly on the examination of the times necessary for building decision models for individual algorithms and data-sets. The following algorithms were used: Multilayer Perceptron, Bagging, RandomForest, M5P and Voting. The experiments presented were carried out using data obtained for foundry processes. The JADE platform and the Weka environment were used to implement the multi-agent system.