An Approach to Robust Decision Making under Severe Uncertainty in Life-Cycle Design

Information-Gap Decision Theory (IGDT), an approach to robust decision making under severe uncertainty, is newly considered in the context of a simple life cycle engineering example.  IGDT offers a path to a decision in the class of problems where a nominal estimate of an uncertain life cycle parameter is available, but the amount of the deviation of that estimate from the actual value, as well as the implications of that deviation on performance, are not known.  The decision rule inherent in IGDT entails relaxing one’s demand for optimal performance and choosing designs with maximum immunity, or info-gap robustness, to the effects of deviation from the known estimate.  This tradeoff is analyzed graphically using plots of robustness versus performance demand.  In this paper, an automotive oil filter design example affected by severe uncertainty is formulated and solved using an IDGT approach. The types of life cycle engineering design problems that the approach could be effective towards are discussed, as are potential limitations that could be encountered when solving more complex problems.