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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. Note to Practitioners-This paper was motivated by the resource planning problem for a complex remanufacturing system, which is very important but in general extremely difficult to deal with in terms of, e.g., problem size and uncertainties. Simulation is probably the only way available to select a good plan among a number of candidates. When there exist complicated constraints, simulation becomes even more difficult and selection through simulation could be very inefficient since many candidates may not be feasible but cannot be excluded before simulation. Determining feasibility beforehand is extremely difficult by analytical or numerical methods. This paper suggests a new method using a machine learning-based approach to predict the plan feasibility required in practical applications and can be considered as the first step for optimization-based planning. By applying the rough set theory, the prediction rules are obtained or learned from a training dataset generated by simulation. Then, an iterative reinforcement process is applied to enhance the confidence of learning and to perform iterative retraining on new datasets by the rough set method to generate new rules to add to the knowledge base until the preset threshold is satisfied. The numerical testing results show that the above method is capable of determining the feasible plans for a remanufacturing sy- stem with good accuracy. The method is efficient and scalable for the large-scale problems. The method developed in the paper is being incorporated in the framework of ordinal optimization, and a new constrained ordinal optimization method has been developed for remanufacturing planning.