Bayesian optimization has recently emerged as a popular method for the sample-efficient
optimization of expensive black-box functions. However, the application to high-dimensional …

Simulation optimization involves optimizing some objective function that can only be
estimated via stochastic simulation. Many important problems can be profitably viewed …

We review simulation optimization methods and discuss how these methods underpin
modern artificial intelligence (AI) techniques. In particular, we focus on three areas …

This paper introduces a major redesign of SimOpt, a testbed of simulation-optimization (SO)
problems and solvers. The testbed promotes the empirical evaluation and comparison of …

Simulation–optimization models are well-suited for real-time decision-support to the control
room for search and interception of fugitives by Police on a road network, due to their ability …

While Bayesian Optimization (BO) has emerged as sample-efficient optimization method for
accelerating drug discovery, it has rarely been applied to the process optimization of …

This paper proposes a simulation-based optimization technique for high-dimensional toll
optimization problems of large-scale road networks. We formulate a novel analytical network …

Bayesian optimization is a class of data efficient model based algorithms typically focused
on global optimization. We consider the more general case where a user is faced with …

We describe major improvements to the testing capabilities of SimOpt, a library of simulation-
optimization problems and solvers. Foremost among these improvements is a transition to …

In this paper, a new randomized solver (called VRDFON) for noisy unconstrained derivative-
free optimization (DFO) problems is discussed. Complexity result in the presence of noise for …