Optimization with constraint learning: A framework and survey

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: European Journal of Operational Research

سال: 2023

ISSN: ['1872-6860', '0377-2217']

DOI: https://doi.org/10.1016/j.ejor.2023.04.041