Operations Research

Operations research (also referred to as decision science, or management science) is an interdisciplinary mathematical science that focuses on the effective use of technology by organizations. In contrast, many other science and engineering disciplines focus on technology giving secondary considerations to its use.

Employing techniques from other mathematical sciences, such as mathematical modeling, statistical analysis, and mathematical optimization, operations research arrives at optimal or near-optimal solutions to complex decision-making problems. Because of its emphasis on human-technology interaction and because of its focus on practical applications, operations research has overlap with other disciplines, notably industrial engineering and operations management, and draws on psychology and organization science. Operations Research is often concerned with determining the maximum (of profit, performance, or yield) or minimum (of loss, risk, or cost) of some real-world objective. Originating in military efforts before World War II, its techniques have grown to concern problems in a variety of industries.

Operational research (OR) encompasses a wide range of problem-solving techniques and methods applied in the pursuit of improved decision-making and efficiency. Some of the tools used by operational researchers are statistics, optimization, probability theory, queuing theory, game theory, graph theory, decision analysis, mathematical modeling andsimulation. Because of the computational nature of these fields, OR also has strong ties to computer science and analytics. Operational researchers faced with a new problem must determine which of these techniques ard most appropriate given the nature of the system, the goals for improvement, and constraints on time and computing power.

Work in operational research and management science may be characterized as one of three categories:

ñ    Fundamental or foundational work takes place in three mathematical disciplines: probability theory, mathematical optimization, and dynamical systems theory.

ñ    Modeling work is concerned with the construction of models, analysing them mathematically, implementing them on computers, solving them using software tools, and assessing their effectiveness with data. This level is mainly instrumental, and driven mainly by statistics and econometrics.

ñ    Application work in operational research, like other engineering and economics disciplines, attempts to use models to make a practical impact on real-world problems.

The major subdisciplines in modern operational research, as identified by the journal Operations Research, are:

ñ    Computing and information technologies

ñ    Decision analysis

ñ    Environment, energy, and natural resources

ñ   Financial engineering

ñ    Manufacturing, service sciences, and supply chain management

ñ    Marketing Engineering

ñ    Policy modeling and public sector work

ñ    Revenue management

ñ   Simulation

ñ    Stochastic models

ñ    Transportation