Evolutionary operation research technique (EORT) involves very systematic small changes in process variables during the operation of the process. The results of previous small changes may be used to suggest further changes, so as to approach the optimum operating conditions in a series of small steps. Care is taken to ensure that these individual changes in process parameters do not upset the process nor produce any undesirable outcomes.

EORT may be used to identify the combination of multiple variables to enhance the response surface of any operation, thereby improving the operational objectives and also the productivity of the complex system considerably.

The basic concept underlying EORT is that a smooth response surface exists for a set of variables, which ultimately tend to converge at a single optimum. This is the principle of advanced control system (ACS), in which the impact of minor variations of process parameters is used to vary other parameters in order to maximize or minimize the objective function (e.g., maximize energy efficiency of the equipment/total system). EORT models are based on actual process variables, which are dynamic in nature and fluctuate owing to exogenous or endogenous factors.

Since these manipulable variables effectively form the inputs to the model, the outcomes will naturally be far more precise and accurate. EORT models may be used for carrying out the most-needed sensitivity analysis of the dynamic system, to identify the output trend for a set of new variables and/or operating conditions. EORT models...