Motivated by the vector evaluation genetic algorithm (VEGA), this research develops
simulation budget allocation rules for the VEGA in solving simulation optimization problems …

In healthcare, the planning and the management of resources are challenging as there are
always many complex and stochastic factors in both demand and supply. Simulation …

Top two algorithms arose as an adaptation of Thompson sampling to best arm identification
in multi-armed bandit models for parametric families of arms. They select the next arm to …

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 …

Offline reinforcement learning (RL), which refers to decision-making from a previously-
collected dataset of interactions, has received significant attention over the past years. Much …

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 …

Simulation optimization is a widely used tool in the analysis and optimization of complex
stochastic systems. The majority of the previous works on simulation optimization rely …

Stochastic optimization problems exist widely in many manufacturing and service systems.
Due to the stochastic nature, these problems usually have no analytical solutions and are …

We consider the fixed-budget best arm identification problem where the goal is to find the
arm of the largest mean with a fixed number of samples. It is known that the probability of …

The problem of identifying the best arm among a collection of items having Gaussian
rewards distribution is well understood when the variances are known. Despite its practical …