Operations research is a vast branch of mathematics which encompasses many diverse areas of minimization and optimization. Thousands of books have been written worldwide on the subject of operations research.
The central objective of operations research is optimization, i.e., "to do things best under the given circumstances." This general concept
has great many applications, for instance, in agricultural planning, biotechnology,
data analysis, distribution of goods and resources, emergency and rescue operations,
engineering systems design, environmental management, financial planning, health
care management, inventory control, manpower and resource allocation, manufacturing
of goods, military operations, production process control, risk management, sequencing
and scheduling of tasks, telecommunications, and traffic control.
Closely related disciplines (with significant overlaps among these) include decision analysis, systems analysis, management science, control
theory, game theory, optimization
theory, constraint logic programming, artificial intelligence, fuzzy decision-making,
multi-criteria analysis, and so on. All these disciplines share the objective of
improving a quantitative decision making procedure. The same comment applies to operations
research-related business applications such as supply-chain management, enterprise
resource planning, total quality management, just-in-time production and inventory
management, and materials requirements planning.
Following the general optimization paradigm, when applying operations research, a decision-maker selects the key decision variables that will influence the overall
quality of decisions. This quality is expressed by the objective
function that is maximized (profit, product quality, speed of service or job
completion, and so on), or minimized (cost, loss, risk of some undesirable event,
etc.). In addition to the objective function,
a set of (physical, technical, economic, environmental, legal, societal, etc.) constraints
is also considered. Then, by systematically adjusting the values of all decision
variables, a "good" (feasible) or "very best" (optimal) solution
is selected. Of course, feasibility and optimality can only be defined in the context
of the given problem (model) formulation.