Heuristic Model Implementation for Automation in Smart Factories Using Fog Computing

Due to the constant development of manufacturing systems, smart factories have become common ensuring efficient production through technology application. One of the biggest problems in such environments is to combine automation systems with up-to-date processing and decisionmaking systems. The following is a heuristic model implementation to apply automation in smart factories based on the concept of fog computing. Fog computing is an extension of the fog and cloud computing that enables data processing near the network periphery, thereby reducing system delay. The heuristic model for production planning proposed above is aimed at determining the most efficient use of various resources including the computational power of the computer networks and the bandwidth of a company's networks based on the current demands and requirements of a production line. The use of heuristic algorithms enables the model to quickly adapt to changes that may be occurring in the factory environment in order to maximise performance and reduce the time that the system may be out of order. This is evidenced by the described applicability of the model in smart factories whereby the automation efficiency, decision-making time, and energy use levels are enhanced. Also, it can effectively handle the integration of the Internet of Things (IoT) devices they can monitor, therefore allow real and easy management of devices for predictive maintenance and operational intelligence. Thus, the analysis of the experiment's results enables the evaluation of the heuristic model's feasibility and effectiveness in a smart factory simulation environment and provides guidance for further improvements in automation and fog computing implementation in factories.