Patent ID: 11948107
Assignee: HEXAGON TECHNOLOGY CENTER GMBH
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A system for generating task schedules for a plurality of large-scale capital work projects using an electronic device, the plurality of large-scale capital work projects requiring access to a given shared equipment resource having predetermined availability constraints including how many of the given equipment resource is available and dates and the durations of time each given equipment resource is available, the system comprising:
at least one processor coupled to at least one memory storing computer program instructions which, when run on the at least one processor, causes the system to perform computer processes comprising:
training an artificial intelligence reinforcement learning engine through a reinforcement learning training process based on a plurality of data sets representing a plurality of different large-scale capital work projects including at least one of simulated work projects or actual completed work projects to produce a first artificial intelligence reinforcement engine model;
generating, by the trained artificial intelligence reinforcement learning engine using the first artificial intelligence reinforcement engine model, a task schedule for each of the plurality of large-scale capital work projects with access to the number of available shared equipment resources coordinated across the plurality of task schedules pursuant to the dates and durations of time each given equipment resource is available with the task schedules optimized to maximize resource utilization through reinforcement learning with a reward function based on total number of waste days across the task schedules;
receiving human expert feedback responsive to at least one of the task schedules;
re-training the artificial intelligence reinforcement learning engine through an incremental improvement process based on the expert human feedback to produce a second artificial intelligence reinforcement engine model; and
automatically updating, by the trained artificial intelligence reinforcement learning engine, uncompleted tasks of at least one of the task schedules based on the second artificial intelligence reinforcement engine model,
wherein the reward function defines a reward for each of a plurality of time steps=SUM (Rp/Ra), where Rp is the planned time for each resource and Ra is the available time for each resource, with a total reward calculated by summing of the individual rewards for the plurality of time steps.