Department of Mathematical Sciences

Operations Research

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). 

Faculty

  • W. P. Adams : Mathematical programming, optimization
  • B. Fralix : Queueing theory, applied probability
  • A. Gupte : Mathematical progamming, Mixed integer (nonlinear) programming, Convex optimization
  • P. C. Kiessler : Stochastic processes, queueing theory
  • X. Liu : Queueing theory, stochastic processes, stochastic modeling
  • E. Nasrabadi : Mathematical optimization, in particular robust optimization and network flows
  • M. J. Saltzman : Computational operations research, mathematical programming
  • M. Wiecek : Optimization, multicriteria decision making

Curriculum

Operations Research often approaches a particular problem from several modeling perspectives and uses various analytical techniques. Because of the diversity and broad scope of decision problems, the successful OR practitioner requires training in a number of mathematical concepts and techniques. Areas in the mathematical sciences that relate directly to OR are optimization (linear, nonlinear, integer, network programming, calculus of variations, control theory); applied probability (stochastic processes, queueing, reliability); and applied statistics (simulation, econometrics, time series). Computational mathematics also plays an important role in the effective application of OR because of the need to structure and analyze vast amounts of data and to solve large-scale problems efficiently. Other areas of the mathematical sciences related to OR are combinatorics, graph theory, financial mathematics, and dynamical systems.

Courses (Course Descriptions)

  • Probability (800)
  • Stochastic Processes (803)
  • Mathematical Programming (810)
  • Nonlinear Programming (811)
  • Discrete Optimization (812)
  • Advanced Linear Programming (813)
  • Network Flow Programming (814)
  • Network Algorithms and Data Structures (816)
  • Stochastic Models in OR I (817)
  • Stochastic Models in OR II (818)
  • Multicriteria Optimization (819)

Course Substitution Policy  

Sample Curricula

  • Sample Program for M.S. Concentration in Optimization

    • Fall:  800, 810, 853
    • Spring:  805, 821, 860
    • Summer:  803
    • Fall:  812/819, 814, 817
    • Spring:  811, 813, 988, 892
  • Sample Program for M.S. Concentration in Stochastics

    • Fall:  800, 810, 853
    • Spring:  805, 821, 860
    • Summer:  803
    • Fall:  817, 901, 988/simulation
    • Spring:  811, 809, 818, 892

Additional OR Links