  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.
