Stochastic chance-constrained optimization has a wide range of real-world applications. In some real-world applications, the decision-maker has to formulate the problem as a fractional model where some or all of the coefficients are random variables with joint probability distribution....

This paper establishes a general analytical framework for the impulse controls of the diffusion processes driven by multidimensional G-Brownian motion. We propose new G-quasi-variational inequalities (G-QVI) and we provide a verification theorem to link a classical (smooth) solution of the G-QVI...

Uncertainties involved in decision making often impose challenges to our ability of modeling and analyzing the reality. The purpose of this paper is to demonstrate how one can go beyond the conventional optimization thinking, via taking a stochastic viewpoint, to push the boundaries of the...

We perform a thorough investigation on the analytical solvability of general stochastic volatility (SV) models with Levy jumps and propose a unified, accurate, and efficient almost exact simulation method to price various financial derivatives. Our theoretical results lay a foundation for a...

We review academic research on dual-sourcing (or dual-mode) dynamic stochastic inventory models, which study the replenishment of inventory from two sources (or two transportation modes) in the presence of demand uncertainty. The classic assumption, also adopted here, is that the two sources...

Existing compartmental-logistics models in epidemics control are limited in terms of optimizing the allocation of vaccines and treatment resources under a risk-averse objective. In this paper, we present a data-driven, mean-risk, multi-stage, stochastic epidemics-vaccination-logistics model that...

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 a Coordinated clinic and surgery Appointment Scheduling (CAS) problem for advance scheduling of surgical patients. Our models seek to provide timely access to care by coordinating clinic and surgery appointments to ensure that patients can see a surgeon in the clinic and (if needed)...