A method, comprising: receiving (105, 305), by a device, policy parameters for a policy execution model; training (120, 310), by the device, the policy execution model with the policy parameters to generate a trained policy execution model and to generate policies for the trained policy execution model; receiving (110, 115, 310), by the device, job data and profile data for a forecast object model, wherein the job data identifies data associated with executing jobs by a high-performance computing environment, and wherein the profile data identifies a historical profile associated with the high-performance computing environment; training (125, 320), by the device, the forecast object model with the job data and the profile data to generate a trained forecast object model; receiving (130, 325), by the device, a job request that requests performance of one or more operations by resources of the high-performance computing environment; processing (135, 330), by the device, the job request, with the trained policy execution model, to identify one or more of the policies to apply during execution of the job request in the high-performance computing environment; processing (140, 335), by the device, the job request, with the trained forecast object model, to generate a forecast of resources and licenses required from the high-performance computing environment; processing (145, 340), by the device, the job request, other job requests, the one or more of the policies, and the forecast, with a first model, to determine a schedule for the job request; processing (150, 345), by the device, the schedule and current constraints on the resources and the licenses of the high-performance computing environment, with a second model, to determine an optimized schedule for the job request relative to the schedule; and performing (155, 350) one or more actions based on the optimized schedule, wherein the one or more actions comprise: causing the optimized schedule and the one or more of the policies to be implemented by a job scheduler and a license manager of the high-performance computing environment. receiving (105, 305), by a device, policy parameters for a policy execution model; training (120, 310), by the device, the policy execution model with the policy parameters to generate a trained policy execution model and to generate policies for the trained policy execution model; receiving (110, 115, 310), by the device, job data and profile data for a forecast object model, wherein the job data identifies data associated with executing jobs by a high-performance computing environment, and wherein the profile data identifies a historical profile associated with the high-performance computing environment; wherein the job data identifies data associated with executing jobs by a high-performance computing environment, and wherein the profile data identifies a historical profile associated with the high-performance computing environment; training (125, 320), by the device, the forecast object model with the job data and the profile data to generate a trained forecast object model; receiving (130, 325), by the device, a job request that requests performance of one or more operations by resources of the high-performance computing environment; processing (135, 330), by the device, the job request, with the trained policy execution model, to identify one or more of the policies to apply during execution of the job request in the high-performance computing environment; processing (140, 335), by the device, the job request, with the trained forecast object model, to generate a forecast of resources and licenses required from the high-performance computing environment; processing (145, 340), by the device, the job request, other job requests, the one or more of the policies, and the forecast, with a first model, to determine a schedule for the job request; processing (150, 345), by the device, the schedule and current constraints on the resources and the licenses of the high-performance computing environment, with a second model, to determine an optimized schedule for the job request relative to the schedule; and performing (155, 350) one or more actions based on the optimized schedule, wherein the one or more actions comprise: causing the optimized schedule and the one or more of the policies to be implemented by a job scheduler and a license manager of the high-performance computing environment.