  Operations research practitioners frequently want to model complicated
functions that are are difficult to encode in their underlying optimisation
framework. A common approach is to solve an approximate model, and to use a
simulation to evaluate the true objective value of one or more solutions. We
propose a new approach to integrating simulation into the optimisation model
itself. The idea is to run the simulation at each incumbent solution to the
master problem. The simulation data is then used to guide the trajectory of the
optimisation model itself using logic-based Benders cuts. We test the approach
on a class of stochastic resource allocation problems with monotonic
performance measures. We derive strong, novel Benders cuts that are provably
valid for all problems of the given form. We consider two concrete examples: a
nursing home shift scheduling problem, and an airport check in counter
allocation problem. While previous papers on these applications could only
approximately solve realistic instances, we are able to solve them exactly
within a reasonable amount of time. Moreover, while those papers account for
the inherent variance of the problem by including estimates of the underlying
random variables as model parameters, we are able to compute sample average
approximations to optimality with up to 100 scenarios.
