This project studies the use of generative deep learning models for business process simulation. The main hypothesis is that it is possible to train generative models from business process execution logs using deep learning techniques, which can replicate the behavior of the business process more accurately than existing business process simulation approaches. In addition to exploring this hypothesis, the project investigates how to use and modify such generative models for “what if” simulation based on business process changes.

The scope of the Intelligent Cubing System project as a contribution to the national logistics sector, fundamentally deals with the transformation of the traditional logistics sector towards an intelligent logistics sector that includes innovative tools to facilitate and improve merchandise loading/unloading operations. Worldwide, many sectors of the economy have chosen to use specialized software that allows them to carry out their operations in an agile and secure manner, complying with the considerations of the system and current regulations of the sector. Additionally, the use of these tools creates the need to contract quality employees in industrial and software development, thus, strengthening the orange economy and principally the 4.0 industry.