Systems and methods for managing assignments of tasks for mining equipment using machine learning

A computer-implemented method for training a machine-learning model for managing task assignments for a fleet of haul trucks at a mine site, comprising:

• obtaining, via one or more processors of an assignment engine system, first training state data that includes state information for at least one haul truck of a fleet used to operate a mine site, the state information for the at least one haul truck including haul weight data;
• obtaining via the one or more processors of the assignment engine system, second training state data that includes state information for the mine site, wherein the state information for the mine site is indicative of a plurality of tasks available in the mine site and task material weight data associated with the plurality of tasks;
• assigning, via the one or more processors of the assignment engine system, at least one task from amongst the plurality of tasks to the at least one haul truck of the fleet by inputting the first training state data and the second training state data into a reinforcement-learning model that includes a task assignment policy and a reward function;
• causing, via the one or more processors of the assignment engine system, the at least one haul truck to be operated according to the at least one task assignment;
• obtaining further first training state data and further second training state data from a period of time after the at least one task assignment is complete;
• generating a score using the reward function based on the further first training state data and the further second training state;
• updating the task assignment policy based on the generated score.

A system for operating a mine site, comprising:

• a fleet of haul trucks that are green-house-gas free and autonomous;
• a central controller, including:
• at least one memory storing instructions;
• and
• at least one processor operatively connected to the at least one memory and configured to execute the instructions to implement:
• a production planner configured to determine at least one task assignment available for the fleet at the mine site and task material weight data for the at least one task;
• and
• a dispatcher configured to assign the at least one task assignment to at least one haul truck of the fleet, the dispatcher having:
• a trained reinforcement-learning model that has been trained, based on training first state data that includes state information for the fleet and training second state data that includes state information for the mine site, to learn an assignment policy that optimizes a reward function for the mine site, such that the trained reinforcement-learning model is configured to apply the learned assignment policy to input first state data for the fleet and second state data for the mine site to select at least one task to assign to the fleet, wherein the training first state data includes training haul truck haul weight data and the training second state data includes training task material weight data, such that the learned policy accounts for haul truck performance variance due to changing haul truck haul weight;
• and
• an assignment engine configured to perform operations, including:
• receiving the first state data including haul weight data for at least one haul truck in the fleet;
• receiving the second state data;
• assigning the at least one task to the at least one haul truck by inputting the first state data and the second state data into the trained reinforcement-learning model:
• and
• causing the at least one haul truck to be operated according to the at least one task assignment.