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Optimization with constraint learning: A framework and survey

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  • Fajemisin, Adejuyigbe O.
  • Maragno, Donato
  • den Hertog, Dick

Abstract

Many real-life optimization problems frequently contain one or more constraints or objectives for which there are no explicit formulae. If however data on feasible and/or infeasible states are 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.

Suggested Citation

  • Fajemisin, Adejuyigbe O. & Maragno, Donato & den Hertog, Dick, 2024. "Optimization with constraint learning: A framework and survey," European Journal of Operational Research, Elsevier, vol. 314(1), pages 1-14.
    • Handle: RePEc:eee:ejores:v:314:y:2024:i:1:p:1-14
    DOI: 10.1016/j.ejor.2023.04.041
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    as
    1. Pawlak, Tomasz P. & Litwiniuk, Bartosz, 2021. "Ellipsoidal one-class constraint acquisition for quadratically constrained programming," European Journal of Operational Research, Elsevier, vol. 293(1), pages 36-49.
    2. den Hertog, Dick & Stehouwer, Peter, 2002. "Optimizing color picture tubes by high-cost nonlinear programming," European Journal of Operational Research, Elsevier, vol. 140(2), pages 197-211, July.
    3. Han, Biao & Shang, Chao & Huang, Dexian, 2021. "Multiple kernel learning-aided robust optimization: Learning algorithm, computational tractability, and usage in multi-stage decision-making," European Journal of Operational Research, Elsevier, vol. 292(3), pages 1004-1018.
    4. Václavík, Roman & Novák, Antonín & Šůcha, Přemysl & Hanzálek, Zdeněk, 2018. "Accelerating the Branch-and-Price Algorithm Using Machine Learning," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1055-1069.
    5. Dimitris Bertsimas & Nathan Kallus, 2020. "From Predictive to Prescriptive Analytics," Management Science, INFORMS, vol. 66(3), pages 1025-1044, March.
    6. Jiménez-Cordero, Asunción & Morales, Juan Miguel & Pineda, Salvador, 2021. "A novel embedded min-max approach for feature selection in nonlinear Support Vector Machine classification," European Journal of Operational Research, Elsevier, vol. 293(1), pages 24-35.
    7. repec:inm:orijoo:v:3:y:2021:i:2:p:200-226 is not listed on IDEAS
    8. Pawlak, Tomasz P. & Krawiec, Krzysztof, 2017. "Automatic synthesis of constraints from examples using mixed integer linear programming," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1141-1157.
    9. Bengio, Yoshua & Lodi, Andrea & Prouvost, Antoine, 2021. "Machine learning for combinatorial optimization: A methodological tour d’horizon," European Journal of Operational Research, Elsevier, vol. 290(2), pages 405-421.
    10. Baldomero-Naranjo, Marta & Martínez-Merino, Luisa I. & Rodríguez-Chía, Antonio M., 2020. "Tightening big Ms in integer programming formulations for support vector machines with ramp loss," European Journal of Operational Research, Elsevier, vol. 286(1), pages 84-100.
    11. Chi, Hoi-Ming & Ersoy, Okan K. & Moskowitz, Herbert & Ward, Jim, 2007. "Modeling and optimizing a vendor managed replenishment system using machine learning and genetic algorithms," European Journal of Operational Research, Elsevier, vol. 180(1), pages 174-193, July.
    12. Balaji Padmanabhan & Alexander Tuzhilin, 2003. "On the Use of Optimization for Data Mining: Theoretical Interactions and eCRM Opportunities," Management Science, INFORMS, vol. 49(10), pages 1327-1343, October.
    13. Stinstra, Erwin & den Hertog, Dick, 2008. "Robust optimization using computer experiments," European Journal of Operational Research, Elsevier, vol. 191(3), pages 816-837, December.
    14. Álinson S. Xavier & Feng Qiu & Shabbir Ahmed, 2021. "Learning to Solve Large-Scale Security-Constrained Unit Commitment Problems," INFORMS Journal on Computing, INFORMS, vol. 33(2), pages 739-756, May.
    15. David Bergman & Teng Huang & Philip Brooks & Andrea Lodi & Arvind U. Raghunathan, 2022. "JANOS: An Integrated Predictive and Prescriptive Modeling Framework," INFORMS Journal on Computing, INFORMS, vol. 34(2), pages 807-816, March.
    16. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2017. "Enhancing two-stage modelling methodology for loss given default with support vector machines," European Journal of Operational Research, Elsevier, vol. 263(2), pages 679-689.
    17. Dimitris Bertsimas & Allison O’Hair & Stephen Relyea & John Silberholz, 2016. "An Analytics Approach to Designing Combination Chemotherapy Regimens for Cancer," Management Science, INFORMS, vol. 62(5), pages 1511-1531, May.
    18. Artur M. Schweidtmann & Alexander Mitsos, 2019. "Deterministic Global Optimization with Artificial Neural Networks Embedded," Journal of Optimization Theory and Applications, Springer, vol. 180(3), pages 925-948, March.
    19. Emilio Carrizosa & Cristina Molero-Río & Dolores Romero Morales, 2021. "Mathematical optimization in classification and regression trees," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(1), pages 5-33, April.
    20. De Angelis, Vanda & Felici, Giovanni & Impelluso, Paolo, 2003. "Integrating simulation and optimisation in health care centre management," European Journal of Operational Research, Elsevier, vol. 150(1), pages 101-114, October.
    21. Hoffmann, A.L. & Siem, A.Y.D. & den Hertog, D. & Kaanders, J.H.A.M. & Huizenga, H., 2008. "Convex reformulation of biologically-based multi-crtiteria intensity-modulated radiation therapy optimization including fractionation effects," Other publications TiSEM a5430d1f-6b88-43ba-af32-6, Tilburg University, School of Economics and Management.
    22. Crombecq, K. & Laermans, E. & Dhaene, T., 2011. "Efficient space-filling and non-collapsing sequential design strategies for simulation-based modeling," European Journal of Operational Research, Elsevier, vol. 214(3), pages 683-696, November.
    23. Karimi-Mamaghan, Maryam & Mohammadi, Mehrdad & Meyer, Patrick & Karimi-Mamaghan, Amir Mohammad & Talbi, El-Ghazali, 2022. "Machine learning at the service of meta-heuristics for solving combinatorial optimization problems: A state-of-the-art," European Journal of Operational Research, Elsevier, vol. 296(2), pages 393-422.
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