Solving joint chance constrained problems using regularization and Benders’ decomposition-Reference-Cited by-同舟云学术

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Solving joint chance constrained problems using regularization and Benders’ decomposition

Published:2018-11-08 Issue:2 Volume:292 Page:683-709
ISSN:0254-5330
Container-title:Annals of Operations Research
language:en
Short-container-title:Ann Oper Res

Author:

Adam Lukáš^ORCID,Branda Martin,Heitsch Holger,Henrion René

Funder

Grant Agency of the Czech Republic

Ministry of Science and Technology of China

National Natural Science Foundation of China

Science and Technology Innovation Committee Foundation of Shenzhen

Deutsche Forschungsgemeinschaft

Publisher

Springer Science and Business Media LLC

Subject

Management Science and Operations Research,General Decision Sciences

Link

http://link.springer.com/content/pdf/10.1007/s10479-018-3091-9.pdf

Reference61 articles.

1. Adam, L., & Branda, M. (2016). Nonlinear chance constrained problems: Optimality conditions, regularization and solvers. Journal of Optimization Theory and Applications, 170(2), 419–436.

2. Ahmed, S. (2014). Convex relaxations of chance constrained optimization problems. Optimization Letters, 8(1), 1–12.

3. Allevi, E., Bertocchi, M. I., Vespucci, M. T., & Innorta, M. (2007). A mixed integer nonlinear optimization model for gas sale company. Optimization Letters, 1(1), 61–69.

4. Barrera, J., Homem-de Mello, T., Moreno, E., Pagnoncelli, B. K., & Canessa, G. (2016). Chance-constrained problems and rare events: an importance sampling approach. Mathematical Programming, 157(1), 153–189.

5. Ben-Tal, A., & Nemirovski, A. (1998). Robust convex optimization. Mathematics of Operations Research, 23(4), 769–805.

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