TOWARD A MODEL-DRIVEN ENGINEERING FRAMEWORK FOR REPRODUCIBLE SIMULATION EXPERIMENT LIFECYCLE MANAGEMENT

Date

2014-12-10

Author

Teran-Somohano, Alejandro
Dayibas, Orcun
Yılmaz, Levent
Smith, Alice

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Goal-directed reproducible experimentation with simulation models is still a significant challenge. The underutilization of design of experiments, limited transparency in the collection and analysis of results, and ad-hoc adaptation of experiments as learning takes place continue to hamper reproducibility and hence cause a credibility gap. In this study, we propose a strategy that leverages the synergies between model-driven engineering, intelligent agent technology, and variability modeling to support the management of the lifecycle of a simulation experiment. Experiment design and workflow models are introduced for configurable experiment synthesis and execution. Feature-based variability modeling is used to design a family of experiments, which can be leveraged by ontology-driven software agents to configure, execute, and reproduce experiments. Online experiment adaptation is proposed as a strategy to facilitate dynamic experiment model updating as objectives shift from validation to variable screening, understanding, and optimization.

Subject Keywords

Design

URI

https://hdl.handle.net/11511/62879

Collections

Department of Chemical Engineering, Conference / Seminar