As these objectives are conflicting in nature, solving the MOOP helps in obtaining the PO or tradeoff solutions among various conflicting objectives.
MOOP has been performed by integrating the validated model with a well-established multi objective optimization routine, real-coded non-dominated sorting genetic algorithm (NSGA II) .
The above three theories were integrated to develop a new planning efficiency evaluation approach that considers the tradeoffs of MOOP
and planning preferences.
Finally, 33 percent of plans had a patient type retail DAW penalty; however, retail MAB, retail MOOP
, and retail deductible were far less common pharmacy benefit design elements.
Since EAs deal with a group of candidate solutions, it seems natural to use them in MOOPs to find a group of optimal solutions.
It was thus concluded that DEMO may be adopted as an alternative for solving MOOPs.