Mission engineering is a growing field with exciting opportunities and challenges. The goal of mission engineering is to increase system effectiveness, reduce lifecycle costs, and aid in communicating system capabilities to key stakeholders. Optimizing system designs for the mission context in which they will be used is important to achieving these goals. However, system optimization is generally done using multiple key performance indicators (KPIs), which are not always directly representative of, nor easily translatable to, mission success. This paper introduces and motivates the concept of mission-level optimization (MLO), where the objective is to design systems that maximize the probability of mission success over the system life cycle. This builds on previous literature related to mission engineering, modeling, and analysis, as well as optimization under uncertainty. MLO problems are unique in their high levels of design, operational, and environmental uncertainty, as well as the single binary objective representing mission success or failure. By optimizing for mission success, designers can account for large numbers of KPIs and external factors when choosing the best possible system design.


Mission-Level Optimization: Complex Systems Design for Highly Stochastic Life Cycle Use Case Scenarios

Division / Sponsors: 
Design Engineering Division and Computers & Information In Engineering Division


Paper Proceedings: https://asmedigitalcollection.asme.org/IDETC-CIE/IDETC-CIE2020/volume/83907

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