Determining a topology for distribution of services using demand aggregation

A computing system, comprising:

• one or more processors;
• and
• a memory storing program instructions that, when executed by the one or more processors, cause the one or more processors to at least:
• generate a first plurality of training data that includes a plurality of information relating to at least one of locations, communities, event types, or trends for provisioning of a service;
• train, using the first plurality of training data, a machine learning model to determine a demand estimation model that specifies a plurality of locations and a plurality of predicted demands for each of the plurality of locations;
• generate a user interface configured to obtain a plurality of parameters associated with the service to be provided to a plurality of demands distributed across a geographic area;
• cause the user interface to be presented on a client device;
• receive, via an interaction with the user interface, the plurality of parameters;
• process, using the trained machine learning model, the plurality of parameters to generate a predicted demand estimation model;
• determine, based at least in part on the predicted demand estimation model, a plurality of locations, at which to provide the service to the plurality of demands, and a plurality of estimated demands for the plurality of locations based at least in part on a rate of change of consumption of the service relative to a threshold value;
• determine, based at least in part on the plurality of parameters, a cost function representing a total cost for providing the service to the plurality of demands at the plurality of locations;
• optimize the cost function to determine an optimized cost function presenting a lowest relative total cost for providing the service to the plurality of demands;
• generate a topology associated with the optimized cost function defining a manner in which the service is provided to the plurality of demands distributed across the plurality of locations;
• generate a second user interface presenting a representation of the topology, wherein the representation of the topology specifies a plurality of topology information associated with the topology and the plurality of topology information includes at least one of a capacity associated with the plurality of locations, a facility type associated with the plurality of locations, or an event type associated with the plurality of locations;
• cause the second user interface to be presented on the client device;
• compare a plurality of actual demands associated with the plurality of locations to the plurality of estimated demands;
• generate a second plurality of training data based at least in part on the comparison of the plurality of actual demands associated with the plurality of locations to the plurality of estimated demands;
• and
• update the trained machine learning model using the second plurality of training data.

A computer-implemented method, comprising:

• generating, by one or more processors, a first plurality of training data that includes a plurality of information relating to at least one of locations, communities, event types, or trends for provisioning of a service;
• training, using the first plurality of training data by the one or more processors, a machine learning model to determine a demand estimation model that specifies a plurality of locations and a plurality of predicted demands for each of the plurality of locations;
• presenting, on a client device, a user interface providing a plurality of user-selectable parameters relating to providing of the service to a plurality of demands distributed across a geographic area and configured to allow a user to specify selections corresponding to the plurality of user-selectable parameters;
• receiving, via the client device, an interaction with the user interface indicating a plurality of selections corresponding to the plurality of user-selectable parameters;
• processing, using the trained machine learning model, the plurality of selections to generate a predicted demand estimation model;
• determining, by the one or more processors and based at least in part on the predicted demand estimation model, the plurality of selections, and a rate of change of consumption of the service relative to a threshold value, a first plurality of locations at which to provide the service to the plurality of demands and a plurality of estimated demands at each of the first plurality of locations;
• processing, by the one or more processors, the plurality of selections to generate a cost function representing a total cost to provide the service to the plurality of demands distributed across the first plurality of locations;
• optimizing, by the one or more processors, the cost function to determine an optimized cost function presenting a lowest relative total cost for providing the service to the plurality of demands;
• generating, by the one or more processors, a topology associated with the optimized cost function specifying a plurality of service events to be hosted at a second plurality of locations from the first plurality of locations and a corresponding location from the second plurality of locations where each of the plurality of demands is to obtain the service;
• generating, by the one or more processors, a second user interface presenting a representation of the topology, wherein the representation of the topology specifies a plurality of topology information associated with the topology and the plurality of topology information includes at least one of a capacity associated with the first plurality of locations, a facility type associated with the first plurality of locations, or an event type associated with the first plurality of locations;
• causing, by the one or more processors, the second user interface to be presented on the client device;
• comparing, by the one or more processors, a plurality of actual demands associated with the first plurality of locations to the plurality of estimated demands;
• generating, by the one or more processors, a second plurality of training data based at least in part on the comparison of the actual demand associated with the at least one location to the estimated demand forecast;
• and
• updating, by the one or more processors, the trained machine learning model using the second plurality of training data.