Calibration of a decision-making process in a simulation model by a bicriteria optimization problem

In a previous paper, we developed an accurate simulation model of an Intensive Care Unit to study bed occupancy level (BOL). By means of accurate statistical analysis we were able to fit models to arrivals and length-of-stay of patients. We model doctors' patient discharge decisions and define a set of rules to determine the conditions for earlier or delayed discharge of certain patients, according to BOL. For the calibration of the rule parameters, we proposed a nonlinear stochastic optimization problem aimed at matching the model outputs with the real system outputs. In this paper, we improve the calibration of the rule parameters by including the principle of "minimum medical intervention" as a second objective function. We replace the previous objective function with a satisficing matching, in order to gain more degrees of freedom in the search for better rules according to the new objective.