The main objective of the project was to investigate the influence of the selected priority rules of technological operations on chosen production goals. The paper presents the results of the project. The dynamic scheduling process of operations was simulated on the model of production system. The effect of each priority rule has been studied for different production goals in different stressful situations. The synthesis and the evaluation of results were performed by using mathematical and statistical methods. The multi-objective evaluations scoring method have been chosen by authors.
This article deals with knowledge discovery in databases (abbr. KDD) and methodology of this process. The authors give an identification of production parameters and their influence on a production process. Knowledge discovery in the production databases is minimally used for the process of planning and control. There are many problems that occur in the production process. It is important to indentify the impact of manufacturing parameters on the system for managers. New discovered knowledge from production systems will help make the right decision to fulfill the objectives. Using the KDD in the control of production systems, it can be achieved better understanding of system control and can help predict a future behavior of system. The authors formulated general knowledge for improve parameters of analyzed production process. The objectives, steps and some results of the project are presented in this article
The goal of this work was to use the process of knowledge discovery in planning and control of production processes. This work is focused on the prediction of the system behavior from the data of production process. The classification was used as a task of data mining. Some predictive models were created and the predictions of the production process behavior were realized by varying the input parameters using selected methods and techniques of data mining. It can be confirmed that the selected input parameters will lead to the fulfillment of the declared objectives of the process. The process of knowledge discovery has been implemented in the program STATISTICA Data Miner.
The paper presents the utilization of knowledge discovery process from manufacturing system control by using simulation models. The simulation models of manufacturing systems have been developed to obtain the necessary data about production. Obtained data have to be stored and preprocessed. A data warehouse solution is suitable for this purpose. The various analyses can be performed over stored and preprocessed data by using the process of knowledge discovering in databases. The aim of this process is to obtain new, valid, comprehensible and potentially useful knowledge from the production system
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