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An enterprise must make up-front investments in applied research and development (R&D) to mature technologies to a point where they can be successfully deployed. These decisions are based on expected return from the investment and the cost, schedule and technical risks facing each R&D project. Periodically, the decisions are revisited to see if changes in project performance and cost/schedule/risk estimates warrant a change in project funding. Common approaches to this problem involve either Monte Carlo simulation of outcomes for a decision policy, or optimization of decision variables based on some objective. This paper outlines an approach to R&D project selection where both simulation and optimization are used to determine an investment strategy, given project interdependencies and uncertainties in utility, cost, schedule and technical performance. The problem is formulated as a discrete sequential stochastic decision problem and uses heuristics to narrow the solution space and find a reasonable (though non-optimal) solution.

Traffic signal systems serve as one of the most powerful control tools available to improve the efficiency of surface transportation travel. A large number of signal systems currently operate using the time-of-day (TOD) approach. In TOD systems, a day is segmented into a number of intervals in which a different timing plan is used. Thus, the challenge in operating a TOD system effectively is to (1) identify appropriate TOD intervals, and (2) develop optimal timing plans for each interval. The existing procedures used by traffic engineers to address these challenges are time consuming and use relatively small sets of data. This research effort developed a new timing plan development methodology that takes advantage of the large sets of archived traffic data (volume and occupancy) that modern systems are equipped to compile. Based upon statistical cluster analysis, this methodology (1) automates the identification of TOD intervals using a high-resolution definition of system state, and (2) provides representative volumes for plan optimization based on the set of archived data. The results of a case study reported in this paper demonstrate that the methodology supports the development of a TOD system that provides benefits when considering performance measures such as delay, when compared to currently used techniques.

In this paper, we propose the use of cases and instructional modules to teach invention, engineering design, and elements of technology management. One way to learn is to study and reflect upon the experience of others. Such experience may be captured in a case. Cases promote active learning by requiring students to assume the roles of participants in the decision making process. Cases are also a vehicle for raising business issues and human resources concerns not usually considered in traditional engineering courses. Real world design and engineering involves risk and uncertainty, tradeoffs and priorities, ethical issues, human elements, and impact assessment. Cases expose students to open ended, ill defined problems whose solution often depends on making assessments, judgments, and decisions about the technical competencies of the organization.

We describe the senior capstone design course in Systems Engineering at the University of Virginia. The capstone course is a major design experience for the students that involves the students in real design problems while working with real clients in order to expose the students to the practice of systems engineering. Working in groups with five or six members, the students apply the systems engineering aspects of systems and decision science, systems management and systems methodology toward open-ended design problems. Our experience with the program for the first three years has led to considerable success, with numerous capstone groups receiving commendations from clients and the Department of Systems Engineering receiving ABET's first Award for Educational Innovation.