Amazon Technologies, Inc.

Accenture Global Services Gmbh

Navitaire LLC

Ge Aviation Systems Taleris Ltd

Ge Flight Efficiency Services, Inc

Julian Enrique Pachon

Marc Joseph Anderson

Benjamin Glover Thengvall

Bhargav Kunkulagunta

Beryl Tomay

Amandeep Singh

Rohit Malshe

Dipal Patel Gupta

Stuart H Smith

Nader M Kabbani

Mayur Rajendra Giri

Daniel Esteban Merchan Duenas

Shalini Vangala

Raj Kumar Jayapalan

Andre Cornelis Joseph Snoeck

Devender Kumar Gunti Venkata

Julie Marie Paule Poullet

Daniel Esteban Merchan

Jatin Arora

Karthik Charan Konduri

David Staheli

Laura Lynn Housley

Howard Preston Smith, Ii

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

Techniques for dividing a geographical area into districts are described herein. Geospatial vector data, barrier geospatial vector data, road infrastructure data, and historical delivery demand data for a geographical area may be obtained. A plurality of clusters from a stratified sampling of data points for the delivery demand data and barrier penalties from a barrier-aware road graph are generated. A first set of polygons for the plurality of clusters may be generated using a concave hull algorithm. A second set of polygons may be generated using a barrier constrained network Voronoi algorithm that uses the barrier-aware road graph and the first set of polygons as seeds. The second set of polygons may be modified using a bounded Voronoi algorithm that uses a raster cost allocation based on barrier penalties. Coordinates for each polygon of the modified second set of polygons are determined that divide the geographical area.

Systems and methods for road network aware geographical unit generation

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

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

The subject matter of this specification can be embodied in, among other things, a method that includes generating a first graph including possible flight segments between airline stations for an airline and determining a set of permissible crew pairings based on a traversal of the first graph. The method also includes generating a second graph comprising the determined set of permissible crew pairings and determining a set of permissible aircraft routings based on a traversal of the second graph. The method includes generating a set of optimized aircraft routings using an integer-programming algorithm that accepts the determined set of permissible aircraft routings as input, and outputting the set of optimized aircraft routings for use in a flight schedule.

Single step flight schedule optimization

An automated optimization system for generating multiple alternative training and transition plans for airline pilots which include a recall of furloughed pilots, and limits to deviation of pilot start bid periods for training assignments from a specific bid period, wherein such plans are cost optimized to track payroll costs of pilots recalled from furlough, and the combined time for solving an MIP Model representation and generating such plans therefrom is less than one hour.

Integrated decision support system for optimizing the training and transition of airline pilots

An automated optimization system for generating training plans for airline pilots covering both pilot transitions and assignment to training classes for pilots who have received a system bid award, wherein such training plans are generated in less than one hour by first solving a MIP Model of the training/transition problem with an LP relaxation to determine a first level of feasibility, and then solving the MIP Model for an optimized feasible solution from which the training plan is derived.

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Austin, TX, United States of America

Julian Enrique Pachon