  This paper is focused on a stochastic quasi-variational inequality (SQVI)
problem with a continuous and strongly-monotone mapping over a closed and
convex set where the projection onto the constraint set may not be easy to
compute. We present an inexact variance reduced stochastic scheme to solve SQVI
problems and analyzed its convergence rate and oracle complexity. A linear rate
of convergence is obtained by progressively increasing sample-size and
approximating the projection operator. Moreover, we show how a competition
among blood donation organizations can be modeled as an SQVI and we provide
some preliminary simulation results to validate our findings.
