Using Markov Decision Process over Local Modular Supervisors for Planning Problems

This paper presents a novel algorithm that employs the Markov Decision Process approach to efficiently determine sequences that minimize makespan for planning problems. The approach employs local modular supervisors, enhancing flexibility and computational capability. The algorithm achieves optimal makespan values for the evaluated case study, demonstrating its effectiveness. Moreover, the method empowers real-time decision-making, outperforming existing approaches by significantly reducing computation time.