Abstract: Systems and methods to predict and optimize automated recipe selection and item delivery for a customer. A server uses an artificial intelligence model to selects a plurality of predicted recipes for a customer based on customer preferences. Different types of constraints are obtained for the customer to generate a plurality of sets of recipes from the predicted recipes and to optimize those sets of recipes for the customer. A specific set of recipes is selected for the customer based on the optimization, and the filling of the order is initiated for the customer with items associated with the selected set of recipes.

Abstract: Systems and methods to predict and optimize automated recipe selection and item delivery for a customer. An item-selection server selects a plurality of predicted recipes based on customer preferences and order history. Hard and soft constraints are obtained to perform the optimization of the predicted recipes. The hard constraints define parameters that cannot be violated by a recipe or set of recipes, and soft constraints define parameters that expresses a tradeoff value associated with a recipe or set of recipes. Optimization includes selecting multiple sets of predicted recipes based on the hard constraints and scoring each recipe set based on the soft constraints, item inventory, and customer preferences. A specific set of recipes is selected for the order based on the scores, and the filling of the order is initiated for the customer with items associated with the selected set of recipes.