Machine-learning and combinatorial optimization framework for managing tasks of a dynamic system with limited resources
US11393577B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Jun 28, 2019 |
| Grant date | Jul 19, 2022 |
| Priority date | — |
| Expiry date | Sep 23, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG16H50/20
- WIPO fieldIT methods for management
- WIPO sectorElectrical engineering
Abstract
Techniques are described for managing tasks of a dynamic system with limited resources using a machine-learning and combinatorial optimization framework. In one embodiment, a computer-implemented method is provided that comprises employing, by a system operatively coupled to a processor, one or more first machine learning models to determine a total demand for tasks of a dynamic system within a defined time frame based on state information regarding a current state of the dynamic system, wherein the state information comprises task information regarding currently pending tasks of the tasks. The method further comprises, employing, by the system, one or more second machine learning models to determine turnaround times for completing the tasks based on the state information, and determining, by the system, a prioritization order for performing the currently pending tasks based on the total demand and the turnaround times.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.