Operations Research may assist decision-makers in almost any management function. To illustrate, OR supports the key decision making process, allows to solve urgent problems, can be utilized to design improved multi step operations (processes), setup policies, supports the planning and forecasting steps, and measures actual results. OR can be applied at the non-manager levels as well, as engineers or consumers alike can benefit from the improved and streamlined decision-making process.
When first encountered, the methods commonly utilized in OR may seem obscure. Technical labels such as multi-criteria decision analysis, linear and non-linear programming, discrete-event simulation, queuing and stochastic process modeling, conjoint analysis, or neural networking further foster this general impression. Despite the wealth of labels available in the filed of OR, most projects apply one of three broad groups of methods, which may be described as:
Simulation methods, where the goal is to develop simulators that provide the decision-maker with the ability to conduct sensitivity studies to
When first encountered, the methods commonly utilized in OR may seem obscure. Technical labels such as multi-criteria decision analysis, linear and non-linear programming, discrete-event simulation, queuing and stochastic process modeling, conjoint analysis, or neural networking further foster this general impression. Despite the wealth of labels available in the filed of OR, most projects apply one of three broad groups of methods, which may be described as:
Simulation methods, where the goal is to develop simulators that provide the decision-maker with the ability to conduct sensitivity studies to
- search for improvements, and
- to test and benchmark the improvement ideas that are being made.
Optimization methods, where the goal is to enable the decision maker to search among possible choices in an efficient and effective manner, in environments where thousands or millions of choices may actually be feasible, or where some of the comparing choices are rather complex. The ultimate goal is to identify and locate the very best choice based on certain criteria.
Data-analysis methods, where the goal is to aid the decision-maker in detecting actual patterns and inter-connections in the data set. This method is rather useful in numerous applications including forecasting and data mining based business environments.
Within each of the three basic groups, many probabilistic methods provide the ability to assess risk and uncertainty factors.
Within each of the three basic groups, many probabilistic methods provide the ability to assess risk and uncertainty factors.
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