We consider the best-k-arm identification problem for multi-armed bandits, where the
objective is to select the exact set of k arms with the highest mean rewards by sequentially …

Given a fixed simulation budget, the problem of selecting the best and top-m alternatives
among a finite set of alternatives have been studied separately in simulation optimization …

Monte Carlo tree search (MCTS) has been gaining increasing popularity, and the success of
AlphaGo has prompted a new trend of incorporating a value network and a policy network …

We consider a simulation optimization problem for context-dependent decision-making,
which aims to determine the top-designs for all contexts. Under a Bayesian framework, we …

We consider pure-exploration problems in the context of stochastic sequential adaptive
experiments with a finite set of alternative options. The goal of the decision-maker is to …

Simulation budget allocation is a widely used technique for evaluating and optimizing
dynamic discrete event stochastic system via efficient sampling. In warehouse management …

We aim to efficiently allocate a fixed simulation budget to identify the top-mc designs for
each context among a finite number of contexts. The performance of each design under a …

Mixed Integer Linear Programs (MILPs) are powerful tools for modeling and solving
combinatorial optimization problems. Solving an MILP is NP-hard due to the integrality …

Ranking and selection (R&S) conventionally aims to select the unique best alternative with
the largest mean performance from a finite set of alternatives. However, for better supporting …

This entry introduces the topic of stochastic simulation optimization of black box functions
that can be only evaluated by means of expensive simulations. Moreover, the results of such …