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