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Proposes and compares two mathematical formulations for robust microgrid sizing and power scheduling under uncertainties, using a local reduction algorithm that achieves high feasibility rates in Monte Carlo simulations.
This paper proposes Mixed Integer Goal Programming (MIGP) for personalized meal optimization, addressing the limitations of fractional servings and infeasibility from hard constraints. It introduces integer variables for practical serving counts and goal programming deviations with inverse-target normalization, achieving 100% feasibility and better solutions than rounding-based approaches, with an open-source Python implementation.