Simulation Optimization With Non-Stationary Streaming Input Data

Simulation optimization has become an emerging tool to design and analysis of real-world systems. In stochastic simulation, input distribution is a main driving force to account for system randomness. Most existing works on input modeling focus on stationary input distributions. In reality, however, input distributions could experience sudden disruptive changes due to external factors. In this work, we consider input modeling through non-stationary streaming input data, where the input data arrive sequentially across different decision stages. Both the parameters of the input distributions and the disruptive change points are unknown. We use a Markov Switching Model to estimate the non-stationary input distributions, and design a metamodel-based approach to solve the following optimization problem. The proposed metamodel and optimization algorithm can utilize the simulation results from all the past stages. A numerical study on an inventory system shows that our algorithm can solve the problem more efficiently compared to common approaches.