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...

A merchant sells a product over a selling season of T time periods in presence of a limited inventory. The merchant observes new external information at the beginning of each time period and then sets a price for that time period. Initially, the merchant does not know the distribution of the...

This supplementary material for A Closed-form Expansion Approach for Pricing Discretely Monitored Variance Swaps contains (1) more details on the computational results (Section 1), (2) an illustration of expansion formulas (Section 2), and (3) an interpretation of our expansions for the examples...

Rechargeable aqueous zinc-ion batteries (ZIBs) have attracted increasing interest in large-scale energy storage systems due to their low cost, high safety, and high volumetric energy density. Manganese-based materials are one of most promising aqueous zinc-ion battery cathode materials but...

The two roles of a dealer in over-the-counter markets, immediacy provision and matchmaking, create a conflict of interest--the dealer prioritizes inventory turnover for immediacy provision, rather than making matches between customer flows. Compared to a counterfactual scenario without this...

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 develop an analytical framework to appropriately model and adequately analyze A/B tests in presence of nonparametric non-stationarities in the targeted business metrics. A/B tests, also known as online randomized controlled experiments, have been used at scale by data-driven enterprises to...

The problems of online pricing with offline data, or other similar general online decision making with offline data problems, have observed increasing interests especially in operational research. To evaluate pricing policies when offline data are available, the decision maker can either...

This study investigates the machine learning tools that are employed to learn data-to-decision mappings. A data-to-decision mapping, once learnt, adopts relevant operational information as input and effectively outputs a reliable decision variable. The operational data used by the machine...