Robust simulation-based scheduling methodology to reduce the impact of manual installation tasks on low-volume aerospace production flows

This work focuses on the development, implementation, and evaluation of a robust simulation-based planning and optimization methodology. This methodology attempts to reduce the risk of schedule over-runs by identifying plans that are robust to various process delays and disruptions. A representative case study is used to test the methodology's capability to model a real production environment and search for improved scheduling options. The case study involves planning manual sensor installation processes within a provided production schedule to reduce the impact of the sensor installations and the risk of production delays. Properly planning sensor installations is becoming more important as new aerospace vehicles continue to incorporate more sensors to monitor system health, performance, safety, reliability, etc. Traditional scheduling techniques provide a strong framework to plan and optimize, at a medium level of detail, the completion of primary production processes (e.g. structural assembly, system integration, etc.). However, fully defining the interactions and logic required to evaluate the impact resulting from the sensor installation tasks in this scheduling framework is challenging. The discrete-event simulation paradigm simplifies the definition of these production rules and constraints; however, DES models commonly require too much detail, modeling effort, and optimization time/resources to be useful during pre-production planning. The developed methodology addresses this challenge by integrating the process optimization strengths of scheduling with the modeling flexibility of simulation. This enables the fast generation of a limited fidelity simulation that can evaluate the impact of sensor installations to support simulation-based schedule optimization. Throughout this work, the methodology is built up by leveraging various scheduling, simulation, and optimization capabilities. The methodology is then implemented to explore its ability to model and optimize the case study's problem. Finally, results from optimization runs with and without considering schedule robustness are compared to investigate how the methodology utilizes information about schedule risk to improve the resulting plan. (C) 2017 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.