A unified framework for stochastic optimization
WB Powell - European Journal of Operational Research, 2019 - Elsevier
Stochastic optimization is an umbrella term that includes over a dozen fragmented
communities, using a patchwork of sometimes overlap** notational systems with …
communities, using a patchwork of sometimes overlap** notational systems with …
Review on ranking and selection: A new perspective
In this paper, we briefly review the development of ranking and selection (R&S) in the past
70 years, especially the theoretical achievements and practical applications in the past 20 …
70 years, especially the theoretical achievements and practical applications in the past 20 …
Simple bayesian algorithms for best arm identification
D Russo - Conference on Learning Theory, 2016 - proceedings.mlr.press
This paper considers the optimal adaptive allocation of measurement effort for identifying the
best among a finite set of options or designs. An experimenter sequentially chooses designs …
best among a finite set of options or designs. An experimenter sequentially chooses designs …
Customer acquisition via display advertising using multi-armed bandit experiments
Firms using online advertising regularly run experiments with multiple versions of their ads
since they are uncertain about which ones are most effective. During a campaign, firms try to …
since they are uncertain about which ones are most effective. During a campaign, firms try to …
A knowledge-gradient policy for sequential information collection
In a sequential Bayesian ranking and selection problem with independent normal
populations and common known variance, we study a previously introduced measurement …
populations and common known variance, we study a previously introduced measurement …
The knowledge-gradient policy for correlated normal beliefs
We consider a Bayesian ranking and selection problem with independent normal rewards
and a correlated multivariate normal belief on the mean values of these rewards. Because …
and a correlated multivariate normal belief on the mean values of these rewards. Because …
Ranking and selection for pairwise comparison
H **ao, Y Zhang, G Kou, S Zhang… - Naval Research …, 2023 - Wiley Online Library
In many real‐world applications, designs can only be evaluated pairwise, relative to each
other. Nevertheless, in the simulation literature, almost all the ranking and selection …
other. Nevertheless, in the simulation literature, almost all the ranking and selection …
Real-time digital twin-based optimization with predictive simulation learning
Digital twinning presents an exciting opportunity enabling real-time optimization of the
control and operations of cyber-physical systems (CPS) with data-driven simulations, while …
control and operations of cyber-physical systems (CPS) with data-driven simulations, while …
The knowledge gradient algorithm for a general class of online learning problems
We derive a one-period look-ahead policy for finite-and infinite-horizon online optimal
learning problems with Gaussian rewards. Our approach is able to handle the case where …
learning problems with Gaussian rewards. Our approach is able to handle the case where …
The knowledge-gradient algorithm for sequencing experiments in drug discovery
We present a new technique for adaptively choosing the sequence of molecular compounds
to test in drug discovery. Beginning with a base compound, we consider the problem of …
to test in drug discovery. Beginning with a base compound, we consider the problem of …