Information regarding flight schedules, available aircraft, and maintenance information is received from one or more systems. A graph database is generated based on the received information. A graph traversal algorithm is applied to the graph database to determine an assignment of the available aircraft to the flight schedules based on reducing a risk of aircraft-on-ground at a remote location, reducing actual costs, or reducing a time cost.

System and method comprising: collecting, using automated data collection, data about a plurality of opportunities that include historic opportunities and at least one current opportunity; performing matrix factorization to compute, based on the collected data, an approximated interaction matrix that includes predictions for a set of dynamic variables for the current opportunity; and outputting a recommendation of one or more actions for achieving the milestone for the current opportunity based on the predictions for the set of the dynamic variables for the current opportunity.

A multi-objective scheduling advisor for generating a multi-stop visitation schedule includes generating, by a computer, a road network map corresponding to a predetermined area including a plurality of tasks locations. A task to be performed is assigned to each of the plurality of task locations. The computer calculates a business value for each task location using at least one of a calculation, a selected business rule applied to a delay duration, and an input value received from a client. A duration of a respective task is calculated using historical data on task durations associated with a staff of operators over different predetermined areas to determine an average task duration for every operator in the staff. Finally, using a metaheuristic binary optimization algorithm, the computer chooses different candidate tasks for the multi-stop visitation schedule to visit multiple assets in a single trip within the predetermined area.

In a system and method of the present disclosure, clients select potential matches through an application, such as a browser-based application executed on a computing device, or a mobile application executed on a mobile computing device such as a smart phone. Through the use of automation, first human and then automated (i.e., computer-implemented) matchmakers simultaneously reach out to the potential match to vet on behalf of the clients. The system and method also uses data to suggest other matches that have a high likelihood of success for being liked by the client will like, based on their historical selections and interactions through the application.

A system and method for advanced inventory management using deep neural networks. The system may be disposed in a business establishment or it may be a cloud-based network comprising a one or more databases to store and retrieve including patron data, recipe data, business data, and inventory data, mobile and compute devices, staff and suppliers, one or more gateways for vendors and staff to interface with other third-party business, and an inventory analysis server. Taken together or in part, optimize organizational operations by predicting and optimizing key inventory decisions using artificial intelligence or other computerized methods around inventory management based upon a large amount of variables associated with the enterprise.

As described herein, a system, method, and computer program are provided for network experience optimization using a residential network router. In use, an electronic calendar is accessed by a residential network router. Additionally, the residential network router determines a plurality of events saved to the electronic calendar, wherein the plurality of events require, at least in part, simultaneous network resource (e.g. bandwidth) usage. Further, the plurality of events are prioritized, using the residential network router. Still yet, available network resources are assigned among the plurality of events, using the residential network router, based on the prioritization.

A system and method for a restaurant-as-a-service system, comprising one or more database(s), a cluster manager, and a platform which provides restaurants automated, multiple-domain spanning, intelligent business predictions and optimizations leveraging modular, highly integrable microservices which perform various domain-specific functions and tasks to enhance restaurant operations and patron experience. The cluster manager may intercept data requests and algorithmically forward requests to the appropriate microservice operating on an available cluster of computing devices.

A task management platform generates an interactive display tasks based on multi-team activity data of different geographic locations across a plurality of distributed guided user interfaces (GUIs). Additionally the task management platform uses a distributed machine-learning based system to determine a suggested task item for a remote team based on multi-team activity data of different geographic locations.

The present disclosure relates to a machine control method, a machine control system, a computer-readable storage medium, and an electronic device. The machine control method includes: matching, based on input information of a machine, a plurality of corresponding sites that the machine needs to pass through and expected pass-through time of the machine in each of the plurality of corresponding sites; determining, based on actual time at which the machine arrives at a current site and the expected pass-through time of the machine in each of the plurality of corresponding sites, expected time at which the machine arrives at a subsequent site; and sending the expected time at which the machine arrives at the subsequent site.

Systems and methods are disclosed for enterprise work sharing between source and processing locations. For example, an enterprise work share engine assigns shared work requests to a processing pharmacy for prescription verification tasks that are prerequisites for prescription fulfillment at a source pharmacy. The assigned prescription verification task is completed by a pharmacy resource at the processing pharmacy and the disposition is sent to the source pharmacy. The prescription fulfillment task is completed by a pharmacy resource at the source pharmacy in response to receiving the disposition of the prescription verification task.