Amazon Technologies, Inc.

General Electric Company

Taleris Global Llp

Ge Aviation Systems Taleris Ltd

Ge Flight Efficiency Services, Inc

Marc Joseph Anderson

Julian Enrique Pachon

Jon David Petersen

Jonathan Mark Dunsdon

Michael Arguello

Beryl Tomay

Amandeep Singh

Nader M Kabbani

Bhargav Kunkulagunta

Shalini Vangala

David Staheli

Mayur Rajendra Giri

Karthik Charan Konduri

Raj Kumar Jayapalan

Julie Marie Paule Poullet

Daniel Esteban Merchan Duenas

Howard Preston Smith, Ii

Devender Kumar Gunti Venkata

Laura Lynn Housley

Andre Cornelis Joseph Snoeck

A method for graph-based modeling to optimize geographic regions using a resource-constrained objective-maximal path may include a first geographic area as a first path between a first origin location and a first destination location; encoding a second geographic area as a second path between a second origin location and a second destination location; applying a set of constraints to the first geographic area and the second geographic area to cover the jurisdiction with geographic areas, and to map respective entities to a single respective geographic area; generating a first objective-maximal path between the first origin location and the first destination location; and generating a second objective-maximal path between the second origin location and the second destination location.

Enhanced graph-based modeling for geographic region optimization

Described are systems and methods directed to determining a topology for the efficient allocation and distribution of a service to customers that are distributed over a geographic area. For example, the topology can facilitate the planning, scheduling, and allocation of resources for the efficient provisioning of services to customers at locations distributed over the geographic area. Certain input parameters and/or constraints associated with the provisioning of the service may be processed to define a cost function that may represent a total cost of providing the service while ensuring that none of the constraints are exceeded. The cost function may be optimized to determine a lowest relative cost for providing the services to the customers using the various mechanisms and/or types of resources, and a topology associated with the optimized cost function can be generated to specify how the service should be provided to satisfy the demand.

Determining a topology for distribution of services using demand aggregation

Disclosed are various embodiments for generating a sort zone assignment plan for packages expected to arrive at a facility during a predetermined period of time. Prior to arrival of the packages at the facility, a sort zone assignment plan is generated that optimizes distribution of the plurality of packages to a plurality of sort zones within the facility for a given sorting cycle. Once a package arrives at the facility, the package is scanned and the sort zone assignment is determined according to the sort zone assignment plan. The package is then transferred to the assigned sort zone.

Dynamically generating a sort zone assignment plan

Systems and methods for rescheduling passengers are provided. One example aspect of the present disclosure is directed to a method for rescheduling passengers. The method includes receiving an indication that an event has disrupted a plurality of flights. The method includes identifying a plurality of itineraries. The method includes determining a plurality of sets for the itineraries. The method includes determining a plurality of operations plan alternatives based on the plurality of sets. The method includes determining one or more constraints from the plurality of sets of itineraries. The method includes applying the one or more constraints to the plurality of operations plan alternatives to determine an impact of the one or more constraints to the plurality of operations plan alternatives on the sets of itineraries. The method includes determining an operations plan comprising a schedule for the plurality of flights.

Systems and methods for rescheduling airline passengers

An airline operations system, controller and method reschedules flights affected by a disruption that precludes a planned schedule for the flights. The method includes obtaining data related to a scheduled origination and a scheduled destination for each of a set of passengers scheduled on the flights, generating a passenger connection network of connections between the scheduled origination and the scheduled destination for a subset of the set of passengers, applying at least one criterion to the passenger connection network and rescheduling at least one connecting flight based on the criteria. The system and controller solves a network flow problem to reschedule a subset of connecting flights to have a delayed projected departure time. Resulting output can include the set of delayed flights along with modified projected departure times, a set of flight cancellations and a set of passengers with missed connections.

Method for rescheduling flights affected by a disruption and an airline operations control system and controller

Techniques for efficiently managing resources are described. In an example, a computer system may access a forecast for demand associated with utilizing a service during a time period, where the service is available from service sources grouped in clusters. The computer system may identify resources scheduled to facilitate the service. Based on a scheduled start time for utilizing a resource, the forecast, and an allocation of remaining resources to the clusters, the computer system may allocate the resource to a first cluster and may provide a notification about the allocation to an associated client device. At a subsequent time during the time period, the computer system may re-allocate the resource to a second cluster based on a current utilization of the resource, an update to the forecast, and the allocation of the remaining resources. A respective notification may be provided to the client device.

Allocating dynamic resources to service clusters

Generally, an airline operations computing system accomplishes rule checking in an efficient and useful manner and provides functionality that allows multiple different users to accomplish schedule changes in a manner that minimizes conflicts between different schedulers. In addition, an airline operations computing system includes a common rules engine that is used by multiple different optimizer and decision support systems of the airline operations computing system.

Multiple user resource scheduling

Growing community of inventors

Austin, TX, United States of America

Marc Joseph Anderson