1. Proceedings
  2. I3M
  3. 2022
  4. MAS
  5. 10.46354/i3m.2022.mas.005

A generic framework for simulation-based optimization using high-level architecture

  • a Mostafa Ali,
  • b Emad Mohamed,
  • c Lingzi Wu,
  • d Simaan AbouRizk
  • a,b,c,d  University of Alberta, Department of Civil & Environmental Engineering, 7th Floor Donadeo Innovation Centre
    for Engineering, 9211 116 Street NW, Edmonton, Alberta, T6G 1H9, Canada
Cite as
 Ali M., , Mohamed E., , Wu L., and Abourizk S., (2022).,A generic framework for simulation-based optimization using high-level architecture. Proceedings of the 21st International Conference on Modelling and Applied Simulation MAS 2022). , 005 . DOI: https://doi.org/10.46354/i3m.2022.mas.005

Abstract

Simulation optimization has been the focus of numerous studies in the area of operations research, with many studies integrating optimization algorithms with simulation models to improve decision-making. Although useful, these studies target particular applications and are built for specific problems. As such, their interoperability and reusability remain limited, with the incorporation of new optimization components or simulation models requiring a complete redesign of the entire simulation optimization system. To enhance system flexibility, a novel, generic framework that can be easily applied to any simulation model and can accompany any optimization algorithm is proposed. Using high-level architecture (HLA), the proposed framework is able to provide a communication channel between a simulation model and an optimization algorithm, facilitating the reuse of system components for different problems. A case study was used to demonstrate the functionality of the proposed framework.
Flexibility of the system was demonstrated by combining a simulation model (discrete-event simulation) with an optimization algorithm (genetic algorithm).

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