In this work, we propose a simple but e‚ective method to interpret black-box machine learning models globally. Œat is, we use a compact binary tree, the interpretation tree, to explicitly represent the most important decision rules that are implicitly contained in the black-box machine learning models. Œis tree is learned from the contribution matrix which consists of the contributions of input...

The learning effects are widely studied in scheduling problems recently. Biskup [1] classified the learning effect models into two diverse approaches, namely the position -based and the sum-of-processing-time-based learning effect models. He further claimed that the position-based learning effect model seems to be a realistic assumption for the case that the actual processing of the job is main...