Many real-life optimization problems frequently contain one or more constraints or objectives for which there are no explicit formulas. If data is however available, these data can be used to learn the constraints. The benefits of this approach are clearly seen, however there is a need for this process to be carried out in a structured manner. This paper therefore provides a framework for Optimization with Constraint Learning (OCL) which we believe will help to formalize and direct the process of learning constraints from data. This framework includes the following steps: (i) setup of the conceptual optimization model, (ii) data gathering and preprocessing, (iii) selection and training of predictive models, (iv) resolution of the optimization model, and (v) verification and improvement of the optimization model. We then review the recent OCL literature in light of this framework, and highlight current trends, as well as areas for future research.

翻译：许多实际生活优化问题往往包含一种或多种没有明确公式的限制或目标,然而,如果有数据,这些数据可以用来了解制约因素。这种方法的好处是显而易见的,但有必要以有条不紊的方式开展这一进程。因此,本文件提供了一个与约束性学习优化化框架,我们认为这将有助于正式确定和引导从数据中学习制约因素的进程。这一框架包括以下步骤:(一) 建立概念优化模型,(二) 数据收集和预处理,(三) 选择和培训预测模型,(四) 解决优化模型,(五) 核实和改进优化模型。然后,我们根据这一框架审查最近关于控制性学习的文献,并着重指出当前趋势以及今后研究的领域。