Job Shop Scheduling Rule Mining Based on Optimal Sample and Optimal Attribute Combination
The job shop scheduling problem can be solved using scheduling rules.To discover efficient and accurate dispatc-hing rules,a near-optimal scheduling data and attributes based Decision Tree-Genetic Algorithm(NDTGA)framework was proposed based on the core idea of optimal training samples and attribute combinations.This framework used pair-wise compari-son when constructing training data.Multiple combinations of attribute original values,differences,and comparison values were used when constructing attribute combinations.Decision tree was called to mine new scheduling rules in each optimization process of the genetic algorithm;Finally,the optimal training sample and optimal attribute combination were obtained,and based on this,the optimal scheduling rules were obtained.The superiority of the NDTGA framework in mining scheduling rules was dem-onstrated through comparative experiments with classical dispatching rules and other machine learning algorithms.
万方数据
维普
