Botond Kádár

TL;DR: In this article, machine learning approaches such as neurodynamic programming and simulated annealing are described for managing changes and disturbances in manufacturing systems, and to decrease the computational costs of the scheduling process.

TL;DR: In this paper, a hierarchical planning decision workflow is introduced to assign the products to dedicated and reconfigurable lines, and to optimize the system configuration and the production plan of the system in an integrated way.

TL;DR: All the related issues, i.e., knowledge-based manufacturing and networked manufacturing, together with the requirements of real-time functioning and cooperativeness are kept in view.

TL;DR: A novel method is introduced for selecting tuning parameters improving accuracy and robustness for multi-model based prediction of manufacturing lead times by inserting the prediction models into a simulation-based decision support system, and prospective simulations anticipating near-future deviations and/or disturbances, could be supported.

TL;DR: A novel approach for integrated production planning and control is introduced, with the description of the mathematical models and solution algorithms, complemented by a discrete-event simulation system to assess solution robustness in case of disturbances.