We propose a set of new algorithms based on stochastic localization methods for large-scale discrete simulation optimization problems with convexity structure. All proposed algorithms, with the general idea of "localizing" potential good solutions to an adaptively shrinking subset, are...

We propose a new framework named DS-WGAN that integrates the doubly stochastic (DS) structure and the Wasserstein generative adversarial networks (WGAN) to model, estimate, and simulate a wide class of arrival processes with general non-stationary and random arrival rates. Regarding statistical...

We propose gradient-based simulation-optimization algorithms to optimize systems that have complicated stochastic structure. The presence of complicated stochastic structure, such as the involvement of infinite-dimensional continuous-time stochastic processes, may cause the exact simulation of...

We study the problem of simulating a class of nonstationary spatio-temporal Poisson processes. The Poisson intensity function is non-stationary and piecewise linear in both the time dimension and the spatial location dimensions. We propose an exact simulation algorithm based on the inversion...

We propose estimators and analyze their large-sample distribution for two problems, data-oriented expected performance evaluation and data-oriented stochastic optimization, in presence of a specific class of non-stationarities in the observed data. The considered non-stationarities reflect...