Operations research emerged during World War II. The allied forces started using mathematical techniques in order to maximize the impact of their available resources. George Dantzig is widely considered the father of operations research. He pioneered the solution techniques in linear programming on which all of OR was subsequently built.
The fundamental technique that all OR practitioners rely on is optimization. And in order to be able to optimize something, the real life problem at hand must first be modeled as a mathematical program. This math model is then solved using one of several optimization techniques. The other techniques used in OR are simulation, decision-tree analysis and Game Theory.
OR today is taught in engineering colleges and in management schools (as management science and decision theory.) Some of the real life problems that operations research can help solve include airline scheduling, factory output sequencing and the optimal transportation routes for goods.
Operations research can be broadly classified into two major types: deterministic or stochastic (probability-related). In stochastic OR, different events are assigned probabilities, and the problems are then solved. The other major classification of operations research is into "linear" and "integer" programming. If the solution being sought is a whole number (example: how many people to hire?), then integer programming is used.
The biggest benefit of OR is that it brings science into decision making. If the solution obtained is a so-called "optimal" solution, then no better solution can exist. Over the years, numerous executives and government policymakers have relied on OR to help them with critical decisions. Ultimately, OR helps corporations maximize profit and minimize cost, while policymakers can use these techniques to maximize social good.