AI training and automatic scheduler for scheduling multiple work items with shared resources and multiple scheduling targets
A system for generating a task schedule using an electronic device, the system comprising: a processor comprising a neural network; a memory coupled to the processor; a scheduler coupled to the processor, the scheduler configured to: receive the following: a total work database configured to contain...
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| creator | MADAVARAM KARTHIK KUMAR, GOPAL, UTTAM BODLA KARTHIK KUMAR KIRAN, TAHIR, RAMA, U MEGHANI KAPIL KUMAR VERMA JANU |
| description | A system for generating a task schedule using an electronic device, the system comprising: a processor comprising a neural network; a memory coupled to the processor; a scheduler coupled to the processor, the scheduler configured to: receive the following: a total work database configured to contain entries representing work packets; a resource database configured to contain entries representing resources required to satisfy the entries in the work package; a constraint database configured to contain entries representing that constraints of entries in the work package are satisfied; and a scheduling target database configured to specify a primary target to be implemented by the optimal task scheduling; providing a trained reinforcement learning engine for optimizing task scheduling based on input from the database; and using the trained reinforcement learning engine to generate an optimal work package schedule to order the work packages, wherein the optimal work package schedule maximizes the one or more prim |
| format | Patent |
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| subjects | CALCULATING COMPUTING COUNTING DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FORADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORYOR FORECASTING PURPOSES PHYSICS SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE,COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTINGPURPOSES, NOT OTHERWISE PROVIDED FOR |
| title | AI training and automatic scheduler for scheduling multiple work items with shared resources and multiple scheduling targets |
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