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Real-time planning and scheduling in a shop floor are not easy to accomplish due to the concurrent flow of various parts as well as sharing of different types of resources. Multi-pass scheduling is a well-known method for solving the aforementioned problem. Its success depends largely on selecting the best decision-making rule fast and effectively. Although many efforts have been made in the past, a way to minimize the computational load of rule evaluation and selection has yet to appear. The objective of the paper is to apply a nested partitioning (NP) method and an optimal computing budget allocation (OCBA) method to reduce the computational load without the loss of the performance of multi-pass scheduling. The experimental design and analysis was performed to validate that NP and OCBA can be successfully applied to multi-pass scheduling in order to enhance the performance of multi-pass scheduling.