  We develop a method to solve, theoretically and numerically, general optimal
stopping problems. Our general setting allows for multiple exercise rights,
i.e., optimal multiple stopping, for a robust evaluation that accounts for
model uncertainty, and for general reward processes driven by multi-dimensional
jump-diffusions. Our approach relies on first establishing robust martingale
dual representation results for the multiple stopping problem that satisfy
appealing pathwise optimality (i.e., almost sure) properties. Next, we exploit
these theoretical results to develop upper and lower bounds that, as we
formally show, not only converge to the true solution asymptotically, but also
constitute genuine pre-limiting upper and lower bounds. We illustrate the
applicability of our approach in a few examples and analyze the impact of model
uncertainty on optimal multiple stopping strategies.
