Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …

Over the past few decades, modeling, simulation, and optimization tools have received
attention for their ability to represent and improve complex systems. The use of …

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

Computer models are commonly used to simulate the functional relationships between
inputs and outputs for quality design in 3D printing. However, the high-dimensional outputs …

In the context of modern industry, optimization emerges as one of the most powerful tools,
allowing decision-makers to allocate their resources more assertively and deal with complex …

In the framework of risk assessment in nuclear accident analysis, best-estimate computer
codes associated with probabilistic modeling of uncertain input variables are used to …

Abstract A pillar of Industry 4.0, Simulation Optimization is a powerful tool used across
several fields, enabling system evaluation under varying conditions, facilitating performance …

The proportion of spot trading and short-term contracts has gradually increased in the
rapidly growing LNG market, leading to more uncertainties in LNG demand and prices that …

Multi-objective stochastic simulation optimization plays an important role in designing
complex engineering systems. To identify optimal solutions via simulations, Bayesian …

In this article, we aim to solve Bayesian Risk Optimization (BRO), which is a recently
proposed framework that formulates simulation optimization under input uncertainty. In order …