Adaptive Partitioning Strategy for High-Dimensional Discrete Simulation-based Optimization Problems

TL;DR

This paper presents an adaptive partitioning strategy that improves the efficiency of algorithms solving high-dimensional discrete simulation-based optimization problems, demonstrating significant performance gains in synthetic and real-world applications.

Contribution

It introduces a novel adaptive partitioning technique integrated with an existing framework, enhancing convergence and efficiency for high-dimensional discrete optimization problems.

Findings

Significant efficiency improvements in synthetic tests.

Effective application to a real-world car-sharing problem.

Enhanced convergence and finite-time performance.

Abstract

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.