Abstract: A method includes using templates to identify constraints and terms of at least one objective function associated with at least a portion of one or more processing targets At least one of the templates is based on a resource-task network (RTN) representation of resource nodes and task nodes associated with at least the portion of the one or more processing targets. The method also includes generating one or more optimization problems, where the constraints and the at least one objective function represent at least part of the one or more optimization problems. The method further includes generating at least one candidate production schedule for at least the portion of the one or more processing targets using the one or more optimization problems.

Abstract: A method includes curating CRM data by employing a type system of a model-driven architecture and selecting an AI CRM application from a group of applications. Each CRM application may generate one or more use case insights with one or more objectives. The method also includes obtaining one or more data models including an industry-specific data model from the curated CRM data and orchestrating a plurality of machine learning models for the selected CRM application with the obtained data model(s) to determine one or more machine learning models effective for at least one objective of the selected CRM application. The method further includes applying the determined machine learning model(s) and the obtained data model(s) to predict probabilities that optimize the at least one objective and using the predicted probabilities to apply at least one of the one or more use case insights that optimizes the at least one objective.