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 …
Monte Carlo sampling-based methods for stochastic optimization
T Homem-de-Mello, G Bayraksan - Surveys in Operations Research and …, 2014 - Elsevier
This paper surveys the use of Monte Carlo sampling-based methods for stochastic
optimization problems. Such methods are required when—as it often happens in practice …
optimization problems. Such methods are required when—as it often happens in practice …
[HTML][HTML] Optimal computing budget allocation for the vector evaluated genetic algorithm in multi-objective simulation optimization
Motivated by the vector evaluation genetic algorithm (VEGA), this research develops
simulation budget allocation rules for the VEGA in solving simulation optimization problems …
simulation budget allocation rules for the VEGA in solving simulation optimization problems …
[LLIBRE][B] Introduction to operations research
FS Hillier, GJ Lieberman - 2015 - thuvienso.hoasen.edu.vn
The hallmark features of this edition include clear and comprehensive coverage of the
fundamentals of operations research, an extensive set of interesting problems and cases …
fundamentals of operations research, an extensive set of interesting problems and cases …
[LLIBRE][B] Stochastic simulation optimization: an optimal computing budget allocation
CH Chen, LH Lee - 2011 - books.google.com
With the advance of new computing technology, simulation is becoming very popular for
designing large, complex and stochastic engineering systems, since closed-form analytical …
designing large, complex and stochastic engineering systems, since closed-form analytical …
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 …
Simulation optimization: A review and exploration in the new era of cloud computing and big data
Recent advances in simulation optimization research and explosive growth in computing
power have made it possible to optimize complex stochastic systems that are otherwise …
power have made it possible to optimize complex stochastic systems that are otherwise …
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 …
Efficient simulation budget allocation for selecting an optimal subset
We consider a class of the subset selection problem in ranking and selection. The objective
is to identify the top m out of k designs based on simulated output. Traditional procedures …
is to identify the top m out of k designs based on simulated output. Traditional procedures …
Preference-based online learning with dueling bandits: A survey
In machine learning, the notion of multi-armed bandits refers to a class of online learning
problems, in which an agent is supposed to simultaneously explore and exploit a given set …
problems, in which an agent is supposed to simultaneously explore and exploit a given set …