Trabajo de grado - Maestría
Automated generation of process simulation scenarios from declarative changes
Fecha
2023-06-22Registro en:
instname:Universidad de los Andes
reponame:Repositorio Institucional Séneca
Autor
Barón Espitia, Daniel Felipe
Institución
Resumen
This thesis addresses the aforementioned limitation by introducing a chatbot-based
solution that allows the creation of simulation scenarios with changes specified at
both the simulation parameter level and the control flow level. The implementation
of changes at the simulation parameter level is achieved through the development of
a chatbot using the Rasa Framework, which provides a user-friendly web interface
for interacting with the chatbot. The proposed chatbot underwent manual validation
using simulation models for three event logs representing different processes. It successfully generated scenarios aligned with the desired modifications. Demonstrated
at the 2022 ICPM conference as a Demo paper, the chatbot received positive feedback and generated significant interest from the audience. The live demonstration
allowed attendees to interact with the chatbot, validating its effectiveness and user
experience. While formal validation using datasets and performance metrics was
not conducted, the positive reception at the conference serves as an initial validation
of its potential value in real-world applications.
On the other hand, to handle changes at the control flow level, the thesis proposes
an innovative approach that enables the specification of such changes declaratively,
without the need for manual modifications to the underlying procedural model.
This approach utilizes a generative deep learning model to generate traces that
resemble the specified process change. Based on these generated traces, a stochastic
process model is derived and utilized as the foundation for constructing a modified
simulation model for further analysis and evaluation. An experimental evaluation of
the proposed approach demonstrates that the generated simulation models achieve
a level of accuracy comparable to manually created models that directly modify the
original process model. This evaluation highlights the effectiveness and reliability
of the proposed solution in generating modified simulation models for hypothetical
analysis. Overall, this research presents a comprehensive and innovative approach
to address the shortcomings in generating simulation scenarios with changes at
both the simulation parameter and control flow levels. The chatbot-based solution
and the utilization of generative deep learning models contribute to the automation
and ease of generating accurate and reliable simulation models, enabling effective
hypothetical analysis for process improvement and optimization.