In this paper, we study data-driven chance constrained stochastic programs, or more
specifically, stochastic programs with distributionally robust chance constraints (DCCs) in a …

We study approximations of optimization problems with probabilistic constraints in which the
original distribution of the underlying random vector is replaced with an empirical distribution …

Linear programs with joint probabilistic constraints (PCLP) are difficult to solve because the
feasible region is not convex. We consider a special case of PCLP in which only the right …

We consider convex optimization problems with uncertain, probabilistically described,
constraints. In this context, scenario optimization is a well recognized methodology where a …

We present a new approach for exactly solving chance-constrained mathematical programs
having discrete distributions with finite support and random polyhedral constraints. Such …

When there is parameter uncertainty in the constraints of a convex optimization problem, it is
natural to formulate the problem as a joint chance constrained program (JCCP), which …

Various applications in reliability and risk management give rise to optimization problems
with constraints involving random parameters, which are required to be satisfied with a …

Millions of young people are not immunized in low income (LI) and lower middle income
(LMI) countries because of low vaccine availability resulting from inefficiencies in cold …

We study a class of chance-constrained two-stage stochastic optimization problems where
second-stage feasible recourse decisions incur additional cost. In addition, we propose a …

We propose two new Lagrangian dual problems for chance-constrained stochastic
programs based on relaxing nonanticipativity constraints. We compare the strength of the …