Abstract: Example embodiments may include: obtaining, from a database, representations of tasks, agents, and limits on how the tasks can be distributed to the agents; based on attributes of the tasks, the agents, and the limits, determining compatibility measures of pairs of the tasks and the agents; based on the compatibility measures, determining a distribution of the tasks and the agents to a plurality of partitions; until a stopping criterion is satisfied, iteratively modifying the distribution to increase a total of the compatibility measures within each of the partitions or across all of the partitions; and generating, by a solver, a plurality of schedules that govern performance of the tasks by the agents respectively within each of the partitions.

Abstract: Example implementations may involve: receiving, from a software application, a request for task grouping; obtaining, from structured data, a policy that identifies controls for the task grouping and a rule; obtaining, from the structured data, the rule, wherein the rule specifies a procedure for obtaining a plurality of tasks associated with the software application that are candidates for the task grouping; based on the rule, obtaining, from the structured data, the plurality of tasks; for each respective task of the plurality of tasks, placing the respective task within one of one or more groups such that the controls are satisfied; providing, to the software application, the one or more groups with the tasks respectively placed within each; and generating, by the software application and based on the one or more groups, a schedule for workers to perform the tasks.

Abstract: A system performs operations including receiving a service appointment request, identifying the one or more skills associated with the service appointment request, referencing service agent profile data stored on a first database, wherein the service agent profile data includes one or more skills possessed by each of a plurality of service agents, referencing service agent schedule data stored on a second database, dynamically populating a calendar of available appointment times, including identifying one or more time slots during which at least one service agent of the plurality of service agents possesses the identified skills is available, receiving an input selecting a first time slot of the one or more time slots, and updating a schedule of a first service agent of the plurality of service agents to reflect that the first service agent has a service appointment scheduled during the first time slot.

Abstract: In accordance with the present approach, a routing algorithm may be implemented to enable an agent to receive an acceptable route between a number of assigned tasks on demand. Particularly, the algorithm may determine the acceptable route by determining a travel cost for each route between an end location and one of the assigned tasks. Then, the algorithm may add in another assigned task and determine a travel cost for each route from the end location, to a first assigned task, and then to a second assigned task. Continuing in this manner, the algorithm constructs routes from the end location and through each possible subset of the assigned tasks, while tracking a permutation of each subset having the lowest travel cost. The algorithm may therefore trace back through the lowest-cost permutations to construct a complete route that has the lowest cost.

Abstract: Embodiments of the present disclosure are directed to providing, via a client instance hosted by an enterprise management data-center, an optimized travel route, including task assignment and scheduling, based at least on user configured criteria. Particularly, the client instance may execute an algorithm, trained via machine learning, to determine the optimized travel route in view of the user configured criteria.

Abstract: In accordance with the present approach, a routing algorithm may be implemented to enable an agent to receive an acceptable route between a number of assigned tasks on demand. Particularly, the algorithm may determine the acceptable route by determining a travel cost for each route between an end location and one of the assigned tasks. Then, the algorithm may add in another assigned task and determine a travel cost for each route from the end location, to a first assigned task, and then to a second assigned task. Continuing in this manner, the algorithm constructs routes from the end location and through each possible subset of the assigned tasks, while tracking a permutation of each subset having the lowest travel cost. The algorithm may therefore trace back through the lowest-cost permutations to construct a complete route that has the lowest cost.

Abstract: Embodiments of the present disclosure are directed to providing, via a client instance hosted by an enterprise management data-center, an optimized travel route, including task assignment and scheduling, based at least on user configured criteria. Particularly, the client instance may execute an algorithm, trained via machine learning, to determine the optimized travel route in view of the user configured criteria.

Abstract: Embodiments of the present disclosure are directed to providing, via a client instance hosted by an enterprise management data-center, an interactive map configured to provide contextual actions on a user interface. Using interaction points of the interactive map, a user may assign tasks, communicate with other users, and search for various elements.

Abstract: A system includes a non-transitory memory and one or more hardware processors configured to read instructions from the non-transitory memory.