Chance-constrained optimization under limited distributional information: A review of reformulations based on sampling and distributional robustness
S Küçükyavuz, R Jiang - EURO Journal on Computational Optimization, 2022 - Elsevier
Chance-constrained programming (CCP) is one of the most difficult classes of optimization
problems that has attracted the attention of researchers since the 1950s. In this survey, we …
problems that has attracted the attention of researchers since the 1950s. In this survey, we …
[HTML][HTML] A meta-heuristic optimization for a novel mathematical model for minimizing costs and maximizing donor satisfaction in blood supply chains with finite capacity …
Blood and blood banks play an essential role in patient care. This study proposes a novel
mathematical model for Blood Supply Chains (BSCs), which minimizes the total costs and …
mathematical model for Blood Supply Chains (BSCs), which minimizes the total costs and …
Bayesian optimization of risk measures
S Cakmak, R Astudillo Marban… - Advances in Neural …, 2020 - proceedings.neurips.cc
We consider Bayesian optimization of objective functions of the form $\rho [F (x, W)] $, where
$ F $ is a black-box expensive-to-evaluate function and $\rho $ denotes either the VaR or …
$ F $ is a black-box expensive-to-evaluate function and $\rho $ denotes either the VaR or …
Multi-model Markov decision processes
Markov decision processes (MDPs) have found success in many application areas that
involve sequential decision making under uncertainty, including the evaluation and design …
involve sequential decision making under uncertainty, including the evaluation and design …
[PDF][PDF] Chance-constrained optimization: A review of mixed-integer conic formulations and applications
S Küçükyavuz, R Jiang - arXiv preprint arXiv:2101.08746, 2021 - researchgate.net
Chance-constrained programming (CCP) is one of the most difficult classes of optimization
problems that has attracted the attention of researchers since the 1950s. In this survey, we …
problems that has attracted the attention of researchers since the 1950s. In this survey, we …
Distributionally robust chance-constrained programs with right-hand side uncertainty under Wasserstein ambiguity
We consider exact deterministic mixed-integer programming (MIP) reformulations of
distributionally robust chance-constrained programs (DR-CCP) with random right-hand …
distributionally robust chance-constrained programs (DR-CCP) with random right-hand …
Heat-related knowledge, risk perception, and precautionary behavior among Indonesian forestry workers and farmers: Implications for occupational health promotion …
Forestry workers play a crucial role in implementing forest management programs, but their
outdoor work exposes them to rising temperatures caused by global climate change, which …
outdoor work exposes them to rising temperatures caused by global climate change, which …
Interpretable policies and the price of interpretability in hypertension treatment planning
Problem definition: Effective hypertension management is critical to reducing the
consequences of atherosclerotic cardiovascular disease, a leading cause of death in the …
consequences of atherosclerotic cardiovascular disease, a leading cause of death in the …
Optimizing diesel fuel supply chain operations to mitigate power outages for hurricane relief
Hurricanes can cause severe property damage and casualties in coastal regions. Diesel fuel
plays a crucial role in hurricane disaster relief. It is important to optimize fuel supply chain …
plays a crucial role in hurricane disaster relief. It is important to optimize fuel supply chain …
Soft-robust algorithms for batch reinforcement learning
In reinforcement learning, robust policies for high-stakes decision-making problems with
limited data are usually computed by optimizing the percentile criterion, which minimizes the …
limited data are usually computed by optimizing the percentile criterion, which minimizes the …