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Modeling and simulation are commonly used in all stages of the design process. This is particularly vital to the success of systems engineering projects where the system under consideration is complex and involves interactions between many interdisciplinary subsystems. In the refining stages of the design process (after concept selection), models and simulations can be used to refine and optimize a system with respect to the decision maker’s objectives. In this paper, a dynamic model of a hydraulic backhoe serves as a test-bed for a large-scale sensitivity analysis and subsequent optimization of the most significant design parameters. The model is optimized under uncertainty with respect to a multi-attribute utility function that includes fuel consumption, cost of the key components, and machine performance.

Often in the early stages of the engineering design process, a decision maker lacks the information needed to represent uncertainty in the input parameters of a performance model. In one particular form of severely deficient information, a nominal estimate is available for an input parameter, but the amount of discrepancy between that estimate and the parameter's true value, as well as the implications of that discrepancy on system performance, are not known. In this paper, the concepts and techniques of information-gap decision theory (IGDT), an established method for making decisions robust to severely deficient information, are examined more closely through application to a design problem with continuous design variables. The uncertain variables in the chosen example problem are parameters of a probability distribution, so the relationship between IGDT and design approaches considering precise and/or imprecise probabilities is explained.

In this paper, the relationship between uncertainty and sets of alternatives in engineering design is investigated. In sequential decision making, each decision alternative actually consists of a set of design alternatives. Consequently, the decision-maker can express his or her preferences only imprecisely as a range of expected utilities for each decision alternative. In addition, the performance of each design alternative can be characterized only imprecisely due to uncertainty from limited data, modeling assumptions, and numerical methods. The approach presented in this paper recognizes the presence of both imprecision and sets in the design process by focusing on incrementally eliminating decision alternatives until a small set of solutions remains. This is a fundamental shift from the current paradigm where the focus is on selecting a single decision alternative in each design decision.