In thinking back over the highs and the lows of the 2012 U.S. Presidential campaign season, I remembered the national conversation that began after President Obama and Governor Romney’s debate discussion about the optimal mix of ships and weaponry needed to protect the interests of the country.
They didn’t say it during that debate, but what they were actually talking about is operational research (OR), a decision science that began with British military scientists inventing radar in the 1930s.
Called Operations Research (OR) in the USA and Operational Research in the UK, what we more recently call Decision Science began with British military scientists inventing radar in the 1930s. British leadership recognized the importance of this invention and asked Royal Air Force officers and civilian scientists from several disciplines to come together to figure out how to maximize this early warning so as to quickly calculate the optimal scenario of deploying their defenses.
The Americans and the British formed OR groups throughout the war trying to solve complex military operations such as logistics, combat modeling and force deployment planning. After the war, the lessons learned from applying scientific effort to make decisions in wartime translated into using and extending this knowledge in peacetime for both the government and business needs.
So OR research and development continued at a rapid pace and is defined as “the application of scientific principles to business management, providing a quantitative basis for complex decisions.”
Modern OR is not just a method but a wide range of research methods; a spectrum of research modeling and solution techniques including methods for mathematical modeling, control of optimization, simulation and scheduling processes.
This array of available methodologies enables manufacturers to identify and apply the best answers to complex planning problems.
With the software tools and packaged solutions for specific business problems available today, manufacturers are able to build models interactively, modifying constraints or variables and experiment with the effects of changes to underlying data.
For example, in mathematical optimization, a specialized modeling language enables you to work transparently and directly with symbolic problem formulations, and an appropriate solution method for the current problem can be automatically chosen. This allows problems to be formulated and solved intuitively and efficiently whether they are linear, nonlinear or quadratic.
