Monte Carlo methods for value-at-risk and conditional value-at-risk: a review
Value-at-risk (VaR) and conditional value-at-risk (CVaR) are two widely used risk measures
of large losses and are employed in the financial industry for risk management purposes. In …
of large losses and are employed in the financial industry for risk management purposes. In …
Solving chance-constrained problems via a smooth sample-based nonlinear approximation
We introduce a new method for solving nonlinear continuous optimization problems with
chance constraints. Our method is based on a reformulation of the probabilistic constraint as …
chance constraints. Our method is based on a reformulation of the probabilistic constraint as …
An inner-outer approximation approach to chance constrained optimization
A Geletu, A Hoffmann, M Kloppel, P Li - SIAM Journal on Optimization, 2017 - SIAM
Nonlinear chance constrained optimization (CCOPT) problems are known to be difficult to
solve. This work proposes a smooth approximation approach consisting of an inner and an …
solve. This work proposes a smooth approximation approach consisting of an inner and an …
Learning-based robust optimization: Procedures and statistical guarantees
Robust optimization (RO) is a common approach to tractably obtain safeguarding solutions
for optimization problems with uncertain constraints. In this paper, we study a statistical …
for optimization problems with uncertain constraints. In this paper, we study a statistical …
Optimal stochastic coordinated beamforming for wireless cooperative networks with CSI uncertainty
Transmit optimization and resource allocation for wireless cooperative networks with
channel state information (CSI) uncertainty are important but challenging problems in terms …
channel state information (CSI) uncertainty are important but challenging problems in terms …
Flow shop scheduling with human–robot collaboration: a joint chance-constrained programming approach
D Wang, J Zhang - International Journal of Production Research, 2024 - Taylor & Francis
Human–robot collaboration has been incorporated into production and assembly processes
to promote system flexibility, changeability and adaptability. However, it poses new …
to promote system flexibility, changeability and adaptability. However, it poses new …
Chance-constrained multiple bin packing problem with an application to operating room planning
We study the chance-constrained bin packing problem, with an application to hospital
operating room planning. The bin packing problem allocates items of random sizes that …
operating room planning. The bin packing problem allocates items of random sizes that …
A stochastic approximation method for approximating the efficient frontier of chance-constrained nonlinear programs
We propose a stochastic approximation method for approximating the efficient frontier of
chance-constrained nonlinear programs. Our approach is based on a bi-objective viewpoint …
chance-constrained nonlinear programs. Our approach is based on a bi-objective viewpoint …
A novel multi-objective mutation flower pollination algorithm for the optimization of industrial enterprise R&D investment allocation
Y Song, K Zhang, X Hong, X Li - Applied Soft Computing, 2021 - Elsevier
Industrial enterprises are the main body of national scientific and technological innovation
activities, and the improvement of their research and development (R&D) output capacity …
activities, and the improvement of their research and development (R&D) output capacity …
An augmented Lagrangian decomposition method for chance-constrained optimization problems
X Bai, J Sun, X Zheng - INFORMS Journal on Computing, 2021 - pubsonline.informs.org
Joint chance-constrained optimization problems under discrete distributions arise frequently
in financial management and business operations. These problems can be reformulated as …
in financial management and business operations. These problems can be reformulated as …