Robert Smith, NSF OR program director, spoke about the NSF Engineering directorate at the conference. He gave some details about the OR program’s mission (I will come that later) but first, I want to talk about “engineering research themes.” To support fundamental research and education, the engineering directorate has identified cross-cutting research and education themes. These themes evolve over time and adapt to the needs of emerging research. The current engineering themes are:
- Cognitive engineering: Intersection of engineering and cognitive sciences
- Competitive manufacturing and service enterprises
- Complexity in engineered and natural systems
- Energy, water, and the environment
- Systems nanotechnology
From a personal perspective, I think theme #1 is a facsinating topic. After all, our brains (and bodies) can be viewed as facsinating ‘engineered systems’. An example of research in this theme is devices that augment the senses. Theme #2 is close to most of us that work in the OM/OR/IE area as well as theme #4 (energy, water and environment); see for instance, the GreenOR blog. Many in the OR community also works with complex engineered and natural systems (theme #3), one such complex system that comes to mind is the air traffic system. All in all, it looks like operations research has a lot to offer. . .
. . .which brings me to the OR program’s mission statement and research areas. I am grateful to Robert Smith for allowing me to share this with the community.
Mission: To support fundamental research leading to the creation of innovative mathematical models, analysis, and algorithms for optimal or near optimal decision-making in the design and operation of manufacturing, service and other complex systems.
Traditional Areas of Research in the field include the following (and there is still a lot more to contribute to these areas):
- Discrete and Continuous Optimization,
- Stochastic Modeling and Analysis
Most of the funding goes to the traditional areas but several emerging research thrusts include:
- Intelligent Transportation Systems (OR, SES, and MES Programs)
- Oracle Based Optimization Algorithms (e.g., optimization based on simulations of complex systems)
- Self-Optimizing Systems (observe, learn, adapt)
The last emerging area is especially interesting to me. As Prof. Smith was explaining, it is not only data-driven optimization models that adapt to changing data but also models that adapt themselves as well.