Task scheduling using Markov decision process over supervisory control theory

Abstract This work integrates Markov Decision Process (MDP) framework with Supervisory Control Theory (SCT) to address production planning challenges, especially minimizing makespan. By leveraging MDP’s sequential decision-making framework and SCT’s constraint based framework, the approach ensures online decision-making in dynamic industrial environments. The feasibility of this integration is demonstrated through the formalization of a mapping between the two approaches. The results demonstrate that the proposed approach efficiently solves the tested problems, achieving makespan values close to the reference solution but with significantly lower processing times in most cases.