Abstract: A device may receive a request for a schedule and scheduling constraints to utilize when generating the schedule, and may process, based on the request, a first portion of the scheduling constraints and first optimization variables, with a first optimization solver model, to generate capacity data for the schedule. The device may process the capacity data, a second portion of the scheduling constraints, and second optimization variables, with a second optimization solver model, to generate shift assignment data for the schedule, and may process the shift assignment data, a third portion of the scheduling constraints, and third optimization variables, with a third optimization solver model, to generate skill and task assignment data for the schedule. The device may generate the schedule based on the capacity data, the shift assignment data, and the skill and task assignment data, and may perform one or more actions based on the schedule.

Abstract: A device may receive invoice data related to multiple invoices, requisition data related to multiple requisitions, or project data related to multiple projects. The device may process the data using a feature extraction engine to identify features of the data. The device may process the data using a transformation engine to reduce a size of the data. The device may process the data using a set of machine learning models. The device may generate a set of recommendations related to at least one of: categorizing each of the multiple invoices, each of the multiple requisitions, or each of the multiple projects into one or more of multiple categories, identifying a set of possible suppliers for each of the multiple requisitions or each of the multiple projects, or identifying a set of similar projects for each of the multiple projects. The device may perform one or more actions.