Sopt: ontology for simulation optimization for scientific experiments

  • Authors:
  • Jun Han;John A. Miller;Gregory A. Silver

  • Affiliations:
  • University of Georgia, Athens, GA;University of Georgia, Athens, GA;Anderson University, Anderson, SC

  • Venue:
  • Proceedings of the Winter Simulation Conference
  • Year:
  • 2011

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Abstract

Simulation optimization is attracting increasing research interest from the modeling and simulation community. Although there is much research on how to apply various simulation optimization techniques to solve numerous practical and research problems, researchers find that existing optimization routines are difficult to extend or integrate and often require one to develop their own optimization methods because the existing ones are problem-specific and not designed for reuse. In order to facilitate reuse of the available optimization routines and better capture the essence of different simulation optimization techniques, an ontology for simulation optimization (SoPT) is devised. SoPT includes concepts from both conventional optimization/mathematical programming and simulation optimization. Represented in ontological form, optimization routines can also be transformed into actual executable application code (e.g., targeting JSIM or ScalaTion). As illustrative examples, SoPT is being applied to real scientific computational problems.