Efficient Simulation Optimization with Simulation Learning

Travis Goodwin, Jie Xu, Chun Hung Chen, Nurcin Celik

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Simulation optimization has found great success in automation science and engineering, such as the optimization of manufacturing systems, thanks to its capability to fully account for the complexity and uncertainty in systems. However, it remains a challenge to use simulation optimization in applications where decision time window is very short because of computational efficiency challenge. In this paper, a new framework known as Sequential Allocation using Machine-learning Predictions as Light-weight Estimates (SAMPLE) is proposed to address this challenge. SAMPLE utilizes an offline simulation learning phase to train machine learning models using simulation data. When a decision needs to be made, SAMPLE utilizes machine learning predictions under a Bayesian framework to determine optimal allocation of simulation sampling budget. The proposed approach enables fast-time simulation-based decision making for automation systems. SAMPLE is able to work with lightweight machine learning models that may only provide crude approximations but still achieve considerable computational efficiency gain. Numerical experiments with both benchmark test functions and a case study demonstrate the viability of the proposed SAMPLE framework, with significant performance improvement over decision making using only machine learning predictions, or simulations alone.

Original languageEnglish (US)
Title of host publication2021 IEEE 17th International Conference on Automation Science and Engineering, CASE 2021
PublisherIEEE Computer Society
Number of pages6
ISBN (Electronic)9781665418737
StatePublished - Aug 23 2021
Event17th IEEE International Conference on Automation Science and Engineering, CASE 2021 - Lyon, France
Duration: Aug 23 2021Aug 27 2021

Publication series

NameIEEE International Conference on Automation Science and Engineering
ISSN (Print)2161-8070
ISSN (Electronic)2161-8089


Conference17th IEEE International Conference on Automation Science and Engineering, CASE 2021

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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