Operations Research (OR) is distinguished by its use of quantitative methods (mathematics, statistics, and computing) to aid in rational decision making. Operations Research has been successfully applied to a wide range of problems arising in business and government, such as locating industrial plants, allocating emergency facilities, planning capital investments, designing communication systems, and scheduling production in factories. A common element of these decision problems is the need to allocate scarce resources (such as money, time, or space) while attempting to meet conflicting objectives (such as minimizing cost or maximizing production).

- W. P. Adams: Mathematical programming, optimization
- B. Fralix: Queueing theory, applied probability
- A. Gupte: Mixed integer optimization, nonconvex optimization, polyhedral combinatorics
- P. C. Kiessler: Stochastic processes, queueing theory
- X. Liu: Queueing theory, stochastic processes, stochastic modeling
- Y. Ouyang: Nonlinear optimization, stochastic approximation, algorithm design for big data analytics
- M. J. Saltzman: Computational operations research, mathematical programming
- M. Wiecek: Optimization, multicriteria decision making
- B. Yang: Conic optimization, non-convex quadratically constrained quadratic programming