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A computer-implemented method for site-wide prediction optimization includes training a plurality of a mixed type of regression models with a mixed type of control variables for identifying control set-points of a site-wide operation. A decision tree regression model is trained to predict a status of the plurality of initial set-points for non-linear regression functions. The decision tree regression model is reformu lated into a mixed-integer linear program (MILP) and solved by an MILP solver to find a global solution. An MILP surrogate is determined for a nonlinear optimization prob lem to provide a best solution for one or more of the non-linear regression functions using the best solution as a starting point for solving non-linear regression functions, and a set-point of the mixed control variables is recom mended to control a throughput of the site-wide operation by executing a decomposition operation or a federated learning …