  We approach a class of discrete event simulation-based optimization problems
using optimality in probability, an approach which yields what is termed a
"champion solution". Compared to the traditional optimality in expectation,
this approach favors the solution whose actual performance is more likely
better than that of any other solution; this is an effective alternative to the
traditional optimality sense, especially when facing a dynamic and
nonstationary environment. Moreover, using optimality in probability is
computationally promising for a class of discrete event simulation-based
optimization problems, since it can reduce computational complexity by orders
of magnitude compared to general simulation-based optimization methods using
optimality in expectation. Accordingly, we have developed an "Omega Median
Algorithm" in order to effectively obtain the champion solution and to fully
utilize the efficiency of well-developed off-line algorithms to further
facilitate timely decision making. An inventory control problem with
nonstationary demand is included to illustrate and interpret the use of the
Omega Median Algorithm, whose performance is tested using simulations.
