Artificial intelligence constraint-based optimization for generating and selecting candidate solutions for promotional platform

A non-transitory computer-readable storage medium, the computer-readable storage medium including instructions that when executed by a computer, cause the computer to:

• receive, by an AI demand forecasting engine, historical data related to sales of items and promotion design information;
• transform, by a feature engineering pipeline, the promotion design information into numeric features for use in training predictions for item demand;
• train, by the AI demand forecasting engine to predict independent baseline demands and independent promotion demands, based on the historical data and the numeric features;
• generate, by the AI demand forecasting engine, a plurality of entities, each entity defined by a placement of a sale item in a promotional platform;
• forecast, by the AI demand forecasting engine, a baseline forecast associated with each entity and a promotional forecast associated with each entity;
• forecast, by the AI demand forecasting engine, an objective associated with each entity based on the baseline forecast and the promotional forecast;
• generate by an optimization engine encoded with a mix of real integers and Boolean conditions, a plurality of plans, each plan consisting of a unique subset of entities;
• generate, by the optimization engine, one or more candidate solutions based on each of the plurality of plans and by eliminating plans that violate at least one constraint encoded as at least one Boolean variable of the Boolean conditions;
• and
• select, by the optimization engine, an optimum plan from the one or more candidate solutions based on optimization of the objective.

A computer-implemented method comprising:

• receiving, by an AI demand forecasting engine, historical data related to sales of items and promotion design information;
• transforming, by a feature engineering pipeline, the promotion design information into numeric features for use in training predictions for item demand;
• training, by the AI demand forecasting engine to predict independent baseline demands and independent promotion demands, based on the historical data and the numeric features;
• generating, by the AI demand forecasting engine, a plurality of entities, each entity defined by a placement of a sale item in a promotional platform;
• forecasting, by the AI demand forecasting engine, a baseline forecast associated with each entity and a promotional forecast associated with each entity;
• forecasting, by the AI demand forecasting engine, an objective associated with each entity based on the baseline forecast and the promotional forecast;
• generating by an optimization engine encoded with a mix of real integers and Boolean conditions, a plurality of plans, each plan consisting of a unique subset of entities;
• generating, by the optimization engine, one or more candidate solutions based on each of the plurality of plans and by eliminating plans that violate at least one constraint encoded as at least one Boolean variable of the Boolean conditions;
• and
• selecting, by the optimization engine, an optimum plan from the one or more candidate solutions based on optimization of the objective.