Stochastic simulation is an invaluable tool for operations-research practitioners for the
performance evaluation of systems with random behavior and mathematically intractable …

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 …

ASTRO-DF is a prominent trust-region method using adaptive sampling for stochastic
derivative-free optimization of nonconvex problems. Its salient feature is an easy-to …

Stochastic simulation requires input probability distributions to model systems with random
dynamic behavior. Given the input distributions, random behavior is simulated using Monte …

Structural health monitoring (SHM) is of significant importance in the operation of
engineering systems to ensure the durability and reliability. In this article, we introduce a …

Aimed at sufficiently utilizing available data and prior distribution information, we introduce a
data-driven Bayesian-type approach to solve multi-stage convex stochastic optimization …

This article addresses the challenge of parameter calibration in stochastic models where the
likelihood function is not analytically available. We propose a gradient-based simulated …

Risk management is a popular and important problem in academia and industry. From a
small-scale system, such as city logistics, to a large-scale system, such as the supply chain …

Smart systems are systems embedded with various types of sensors and smart components
(eg, a robot) for superior systems performance and optimal utilization of resources. Due to …

Bayesian optimization is a framework for global optimization of objective functions that are
expensive or time-consuming to evaluate. It has succeeded in a broad range of application …