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It is important for engineering firms to be able to develop forecasts of recommended courses of action based on available information. In particular, engineering firms must be able to assess the benefit of performing information-gathering actions. For example, an automobile manufacturer may use a computer simulation of a hydraulic motor and pump in the design of a new vehicle. The model may contain random variables that can be more accurately determined through expensive information-gathering actions, e.g., physical experiments, surveys, etc. To decide whether to perform these information-gathering actions, the automobile manufacturer must be able to quantify the expected value to the firm of conducting them. However, the cost of computing the expected value of information (through optimization, Monte Carlo sampling, etc.) grows exponentially with the amount of information that is to be gathered and can often exceed the cost of actually gathering the information.

A major decision to make in design projects is the selection of the best technology to provide some needed system functionality. In making this decision, the designer must consider the range of technologies available and the performance of each. During the useful life of the product, the technologies composing the product evolve as research and development efforts continue. The performance evolution rate of one technology may be such that even though it is not initially a preferably technology, it becomes a superior technology after a few years. Quantifying the evolution of these technologies complicates the technology selection decision. The selection of energy storage technology in the design of an electric car is one example of a difficult decision involving evolving technologies.

Tradeoff studies are a common part of engineering practice. Designers conduct tradeoff studies in order to improve their understanding of how various design considerations relate to one another and to make decisions. Generally a tradeoff study involves a systematic multi-criteria evaluation of various alternatives for a particular system or subsystem. After evaluating these alternatives, designers eliminate those that perform poorly under the given criteria and explore more carefully those that remain. One limitation of current practice is that designers cannot combine the results of preexisting tradeoff studies under uncertainty. For deterministic problems, designers can use the Pareto dominance criterion to eliminate inferior designs. Prior work also exists on composing tradeoff studies performed under certainty using an extension of this criterion, called parameterized Pareto dominance.

Decisions made during conceptual design can have a major impact on the success of a design project. However, the inherently imprecise nature of design is a major source of uncertainty and risk in conceptual design decisions. A single concept relates to a large set of specific design implementations, each of which has a different level of desirability based on the tradeoffs designers are willing to make. It therefore is beneficial for designers to have an understanding of the various tradeoffs they can achieve by implementing a concept. In this paper, we describe an approach to modeling design concepts under uncertainty based on a tradeoff space representation. We use the principles of decision making to develop a useful interpretation of a tradeoff space for decisions under uncertainty and to identify criteria useful for eliminating undesirable tradeoffs from consideration. We illustrate our approach to modeling and decision making on an example for the conceptual design of a gearbox.