Data-driven feasibility analysis for the integration of planning and scheduling problems

A framework for the integration of planning and scheduling using data-driven methodologies is proposed. First, the constraints at the planning level related to the scheduling problem are identified. This includes the feasibility of production targets assigned to each planning period (which are equivalent to scheduling horizons). Then, classification methods are used to identify feasible regions from large amounts of scheduling data, and an algebraic equation for the predictor is obtained. The predictor is incorporated in the planning problem, and the integrated problem is solved to optimality. Computational studies are presented to demonstrate the performance of the proposed framework, and results show that the approach is more efficient than current practices in the integration of planning and scheduling problems.