The aim is to get the most general possible solution for the multistage scheduling problem using an innovative and modern approach. The goal is to optimize, not only the makespan, but other aspects of the schedule as the production costs. The proposed solution will take the information of the plant (machines, formula for each product, orders, etc...) and give a feasible schedule that minimizes time, cost and other aspects. Moreover, the machines will have maintenance/cleaning needs that must be respected. Orders will have a due date associated that should be fulfilled. And, the most important feature that the program must have is adaptability. It has to be able to describe and solve a large pool of different plants, all of them with its own particularities. This has to be achieved with an open source constraint satisfaction solver relying heavily on pre- and post-processing along with possible artificial intelligence techniques. The scheduling tool is developed with Python and should be integrated as a service inside an API, this will ease the integration inside the industry 4.0 platform. This way of proceeding adapts the traditional scheduling problem to the new software developing philosophy with modern architecture
