Servicenow, Inc.

Venkitesh Subramanian

Devinder Narang

Wenhua Li

Sanjay Kumar Gupta

Audrey Chen

Rahul Guha

Xianfa Deng

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.

Systems and methods for comprehensive routing

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.

Systems and methods for customizable route optimization

A computing system and method dynamic multi-factor ranking for prioritizing tasks, and in particular, tasks relating to servicing of problem reports in a managed network, is disclosed. The computing system may be disposed within a remote network management platform and be configured for management of problem report sent from reporting stations in the managed network. The computing system may thus receive a plurality of problem report messages from one or more reporting stations, and may then compute a multi-factor ranking of the messages based on dynamically selected factors associated with each message. The multi-factor ranking may then form the basis of a prioritized list of the messages, and the prioritized list may be displayed on a display device of the computing system. The prioritized list may thus provide a basis for prioritizing tasks related to servicing the reported problems.

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Fremont, CA, United States of America

Venkitesh Subramanian