Machine learning approach for determining feasible plans of a remanufacturing system

Resource planning for a complex remanufacturing system is in general extremely difficult in terms of, e.g., problem size and uncertainties. In many cases, simulation is the only way to select a good plan among a great number of candidates. When there exist complicated constraints, direct selection could be very inefficient since many candidates may not be feasible but cannot be excluded beforehand. To meet the challenge, a machine learning method is introduced in this paper to perform feasibility analysis. The rough set theory is first applied to establish the relationship between a plan and its feasibility and an iterative reinforcement process is applied to enhance confidence. The numerical testing results show that this method is promising and scalable for the large-scale problems. The research lays a basis for developing an efficient simulation-based optimization method with complicated constraints.