Abstract: Systems and methods for constraint-based optimization, comprising: an AI demand forecasting engine, an optimization engine, a user-defined objective, and a user-defined set of constraints. Using historical sales data, the AI demand forecasting engine generates a plurality of entities, each entity defined by a placement of an item in a promotion platform; and forecasts the objective associated with each entity. The optimization engine generates a plurality of plans, each plan consisting of a unique subset of entities. Plans that violate at least one constraint are eliminated by the optimization engine, leaving a set of candidate solutions. An optimum plan is selected from the set of candidate solutions based on maximization of the objective.

Abstract: A system and method for optimizing an objective having discrete constraints using a dataset, the dataset including a plurality of aspects associated with the objective. The method comprising: receiving the dataset, the objective, and constraints, at least one of the constraints comprising discrete values; receiving a seed solution comprising initial values for the at least the constraints; iteratively performing until a predetermined threshold is reached: determining a constraint space for each of the constraints have discrete values using a determination of a constraint satisfaction problem; determining an optimized value of the objective using an optimization model, the optimization model taking as input the dataset and the constraint space; and outputting the optimized objective.