Smart manufacturing scheduling with Petri nets

Smart Manufacturing requires adequate scheduling models that can be adopted by a wide variety of systems. Traditional scheduling in the academic world has focused on elegant methods but with very limited application. On the other hand, Petri nets have been a powerful tool for modeling and scheduling a wide variety of manufacturing systems. Although, scheduling with Petri nets has been reported with several graph search and metaheuristic methods, there is still a gap for improvement, especially if classical scheduling theory and neighborhood-based metaheuristics are included. In this chapter, we propose a new LSR (Local Search with Restarts) algorithm for Petri net scheduling that incorporates a new decoding scheme based on random keys. The algorithm was tested on a number of instances from the literature. The results show the validity of the approach.