Optimización y control del flujo de materiales en procesos de producción flexibles utilizando aprendizaje profundo
Industry 4.0, currently on the rise, demandsincreasing flexibility and adaptation of production systems tochanging products demands and external factors. The adaptationof the production systems implies frequent and often abruptchanges in the configurations of the shop floors and consequentlythe movement of materials must be re-planned. Materialhandlingis significant in terms of operative costs and times and it doesnot add value to the end products. It is desired to optimize theperformance of the system based onthe degree of movements,buffer usage and waiting times, such that the combinationof these minimizes the impact on the process costs. Machinelearning algorithms incombination with powerful computationalsimulators can be mutually leveraged to give rise to solvethese kinds of real-world problems, typical of smart factories.In this work, for the optimization approach, we develop aclosed-loop decision-making system with a deep reinforcementlearning algorithm based on a discrete-event simulation modelfor material handling. In addition, our proposed approach usesthe communication architecture Simulai, which allows interfacinga computational discrete-event simulator and the proposed deep learning-based decision-making algorithm. The functionality ofour proposal is evidenced through the obtained results and anoptimal solution for the problem stated is reached, proving thatan intelligent agentcan collaborate in making multiple decisionsfor smart factories.