Utilizing Discrete Event Simulation for Schedule Analysis: Processes and Lessons Learned from NASA's GOPD Integrated Timeline Model

Paper #:
  • 2015-01-2397

Published:
  • 2015-09-15
DOI:
  • 10.4271/2015-01-2397
Citation:
Conner, A. and Rabelo, L., "Utilizing Discrete Event Simulation for Schedule Analysis: Processes and Lessons Learned from NASA's GOPD Integrated Timeline Model," SAE Int. J. Aerosp. 8(1):55-59, 2015, https://doi.org/10.4271/2015-01-2397.
Pages:
5
Abstract:
In planning, simulation models create microcosms, small universes that operate based on assumed principles. While this can be powerful, the information it can provide is limited by the assumptions made and the designed operation of the model. When performing schedule planning and analysis, modelers are often provided with timelines representing project tasks, their relationships, and estimates related to durations, resource requirements, etc. These timelines can be created with programs such as Microsoft Excel or Microsoft Project. There are several important attributes these timelines have; they represent a nominal flow (meaning they do not represent stochastic processes), and they are not necessarily governed by dates or subjected to a calendar. Attributes such as these become important in project planning since timelines often serve as the basis for creating schedules. Simulation techniques such as discrete event simulation (DES) provide the opportunity to introduce variability into the timeline tasks, as well as subject the timeline to certain parameters in order to create a broader understanding of timeframes and schedule impacts. NASA utilizes DES to provide analysis for certain program requirements, budgeting activities, and schedule risk. A major tool for these analyses is the Ground Operations Processing Database (GOPD) Integrated Timeline Model. Updates to the GOPD occur on a semi regular basis allowing for a comparison of analyses providing an opportunity for improvements in modeling and rework of planned activities. It was during one of these comparisons that an issue was discovered as it related to the application of a factor to account for shift work assumptions. This paper presents the GOPD modeling process along with lessons learned and solutions to the shift work assumption problem.
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