Systems and methods for optimizing parallel task completion

A system comprising:

• a non-transitory computer readable medium having instructions stored thereon;
• and
• a data processing apparatus configured to execute the instructions to perform operations comprising:
• collecting task completion data for a plurality of completed tasks, wherein the data for each completed task comprises respective task type, task duration, and attributes of a previous customer for whom the task was performed;
• training a predictive model using the task completion data, the predictive model configured to determine weights for a plurality of tasks, wherein the weight for a task comprises at least one of a predicted amount of time for completing the task and a predicted complexity of the task, given a task type and attributes of a customer associated with the task;
• assigning respective weights to a plurality of uncompleted tasks associated with a first product, the weight for each uncompleted task determined by the predictive model based on input data comprising a task type of the uncompleted task and attributes of a current customer associated with the first product;
• providing a plurality of queues for performing the uncompleted tasks;
• calculating a workload for each queue in the plurality of queues, wherein the workload for each queue is based on the weight of one or more tasks assigned to the queue;
• assigning each task in the plurality of uncompleted tasks to a respective queue from the plurality of queues, based on the calculated workload of the queue, at least one capability of the queue, and the weight for the uncompleted task, wherein assigning each task to the respective queue comprises balancing workloads associated with the plurality of queues.

A computer program product stored in one or more non-transitory storage media for controlling a processing mode of a data processing apparatus, the computer program product being executable by the data processing apparatus to cause the data processing apparatus to perform operations comprising:

• collecting task completion data for a plurality of completed tasks, wherein the data for each completed task comprises respective task type, task duration, and attributes of a previous customer for whom the task was performed;
• training a predictive model using the task completion data, the predictive model configured to determine weights for a plurality of tasks, wherein the weight for a task comprises at least one of a predicted amount of time for completing the task and a predicted complexity of the task, given a task type and attributes of a customer associated with the task;
• assigning respective weights to a plurality of uncompleted tasks associated with a first product, the weight for each uncompleted task determined by the predictive model based on input data comprising a task type of the uncompleted task and attributes of a current customer associated with the first product;
• providing a plurality of queues for performing the uncompleted tasks;
• calculating a workload for each queue in the plurality of queues, wherein the workload for each queue is based on the weight of one or more tasks assigned to the queue;
• assigning each task in the plurality of uncompleted tasks to a respective queue from the plurality of queues, based on the calculated workload of the queue, at least one capability of the queue, and the weight for the uncompleted task, wherein assigning each task to the respective queue comprises balancing workloads associated with the plurality of queues.