I wrote a profit maximization LP with inventory, component usage, production, and machine hours constraints. When I optimize the model, it solves as expected. When applied towards a business case, however, the end user will generally look to make a handful of production changes rather than the several hundred the model suggests at once. I would like to modify the LP so that when a handful of recommendations from the optimal solution are applied to the baseline (i.e. increase maximum production qty of 5 products), the model only makes changes to the decision variables required to solve feasibly (i.e. reduction in production of a low or negative margin product, reduction in production to free up machine hours, etc.), rather than solving everything as optimal and making hundreds of changes.
Is there a way to incentivize the model to maintain baseline conditions and to only make the changes needed to solve feasibly?
Here's the objective: