  In this paper, we introduce a technique to enhance the computational
efficiency of solution algorithms for high-dimensional discrete
simulation-based optimization problems. The technique is based on innovative
adaptive partitioning strategies that partition the feasible region using
solutions that has already been simulated as well as prior knowledge of the
problem of interesting. We integrate the proposed strategies with the Empirical
Stochastic Branch-and-Bound framework proposed by Xu and Nelson (2013). This
combination leads to a general-purpose discrete simulation-based optimization
algorithm that is both globally convergent and has good small sample
(finite-time) performance. The proposed general-purpose discrete
simulation-based optimization algorithm is validated on a synthetic discrete
simulation-based optimization problem and is then used to address a real-world
car-sharing fleet assignment problem. Experiment results show that the proposed
strategy can increase the algorithm efficiency significantly.
