-
公开(公告)号：US20230102494A1
公开(公告)日：2023-03-30
申请号：US17483966
申请日：2021-09-24
发明人： Krishan Kumar Meghani , Gona Uday Kumar , Kiran Kumar Bodla , Tanna Rahul Udaya Kiran , Kalyan Madhavaram
摘要： A system for generating task schedules using an electronic device includes: a processor, the processor comprising neural networks; a memory coupled to the processor; a scheduler coupled to the processor, the scheduler is configured to: receive: a total work database configured to contain items representing work packages; a resources database configured to contain items representing resources required to fulfill items in the work packages; a constraints database configured to contain items representing constraints to fulfilling items in the work packages; and a scheduling objective database configured to designate a prime objective that is to be achieved by the optimum task schedule; provide a trained reinforcement learning engine for optimizing the task schedule based on inputs from the databases; and generate an optimum work package schedule to sequence the work packages using the trained reinforcement learning engine, wherein the optimum work package schedule maximizes the one or more prime objectives.
-
公开(公告)号：US20230095600A1
公开(公告)日：2023-03-30
申请号：US17484029
申请日：2021-09-24
发明人： Krishan Kumar Meghani , Gona Uday Kumar , Kiran Kumar Bodla , Tanna Rahul Udaya Kiran , Kalyan Madhavaram , Jyotirmoy Verma
摘要： A system for generating task schedules using an electronic device includes: a processor, the processor comprising neural networks; a memory coupled to the processor; a scheduler coupled to the processor, the scheduler is configured to: receive: a total work database configured to contain items representing work packages; a resources database configured to contain items representing resources required to fulfill items in the work packages; a constraints database configured to contain items representing constraints to fulfilling items in the work packages; and a scheduling objective database configured to designate a prime objective that is to be achieved by the optimum task schedule; provide a trained reinforcement learning engine for optimizing the task schedule based on inputs from the databases; and generate an optimum work package schedule to sequence the work packages using the trained reinforcement learning engine, wherein the optimum work package schedule maximizes the one or more prime objectives.
-
公开(公告)号：US11948107B2
公开(公告)日：2024-04-02
申请号：US17484029
申请日：2021-09-24
发明人： Krishan Kumar Meghani , Gona Uday Kumar , Kiran Kumar Bodla , Tanna Rahul Udaya Kiran , Kalyan Madhavaram , Jyotirmoy Verma
IPC分类号： G06Q10/06 , G06N3/08 , G06Q10/0631
CPC分类号： G06Q10/06312 , G06N3/08 , G06Q10/06311
摘要： A system for generating task schedules using an electronic device includes: a processor, the processor comprising neural networks; a memory coupled to the processor; a scheduler coupled to the processor, the scheduler is configured to: receive: a total work database configured to contain items representing work packages; a resources database configured to contain items representing resources required to fulfill items in the work packages; a constraints database configured to contain items representing constraints to fulfilling items in the work packages; and a scheduling objective database configured to designate a prime objective that is to be achieved by the optimum task schedule; provide a trained reinforcement learning engine for optimizing the task schedule based on inputs from the databases; and generate an optimum work package schedule to sequence the work packages using the trained reinforcement learning engine, wherein the optimum work package schedule maximizes the one or more prime objectives.
-
公开(公告)号：US20230096811A1
公开(公告)日：2023-03-30
申请号：US17483986
申请日：2021-09-24
发明人： Krishan Kumar Meghani , Gona Uday Kumar , Kiran Kumar Bodla , Tanna Rahul Udaya Kiran , Kalyan Madhavaram , Jyotirmoy Verma
摘要： A system for generating task schedules using an electronic device includes: a processor, the processor comprising neural networks; a memory coupled to the processor; a scheduler coupled to the processor, the scheduler is configured to: receive: a total work database configured to contain items representing work packages; a resources database configured to contain items representing resources required to fulfill items in the work packages; a constraints database configured to contain items representing constraints to fulfilling items in the work packages; and a scheduling objective database configured to designate a prime objective that is to be achieved by the optimum task schedule; provide a trained reinforcement learning engine for optimizing the task schedule based on inputs from the databases; and generate an optimum work package schedule to sequence the work packages using the trained reinforcement learning engine, wherein the optimum work package schedule maximizes the one or more prime objectives.
-
公开(公告)号：US20230094381A1
公开(公告)日：2023-03-30
申请号：US17483941
申请日：2021-09-24
发明人： Krishan Kumar Meghani , Gona Uday Kumar , Kiran Kumar Bodla , Tanna Rahul Udaya Kiran , Kalyan Madhavaram
摘要： A system for generating task schedules using an electronic device includes: a processor, the processor comprising neural networks; a memory coupled to the processor; a scheduler coupled to the processor, the scheduler is configured to: receive: a total work database configured to contain items representing work packages; a resources database configured to contain items representing resources required to fulfill items in the work packages; a constraints database configured to contain items representing constraints to fulfilling items in the work packages; and a scheduling objective database configured to designate a prime objective that is to be achieved by the optimum task schedule; provide a trained reinforcement learning engine for optimizing the task schedule based on inputs from the databases; and generate an optimum work package schedule to sequence the work packages using the trained reinforcement learning engine, wherein the optimum work package schedule maximizes the one or more prime objectives.
-
-
-
-
搜索结果
5 条记录 (耗时 0.017 秒)
