Process scheduling problems have been largely studied in the literature, and a large number of methods and approaches are available and capable of solving them. However, the selection of the best method fitting to a real practical problem at hand, and the limited number of experts in optimization and operations research within industrial environments seriously limit the practical application of the theoretical methods. This work proposes a supporting framework based on a multi-label classification strategy, for selecting those mathematical scheduling models that are more suitable to solve a certain scheduling problem definition. We have decomposed the problem description into binary classification problems, in order to analyze the convenience of each scheduling model for a certain definition. As a result, a systematic approach to scheduling model selection is achieved, facilitating the bridge between theoretical developments and industrial practice.

在文献中已经对过程调度问题进行了大量研究，并且有大量方法和方法可以解决这些问题。然而，选择适合当前实际实际问题的最佳方法，以及工业环境中的优化和运筹学专家数量有限，严重限制了理论方法的实际应用。这项工作提出了一个基于多标签分类策略的支持框架，用于选择那些更适合解决某个调度问题定义的数学调度模型。为了将每个调度模型的确定性进行分析，我们将问题描述分解为二进制分类问题。因此，