American Airlines, Inc.

Amazon Technologies, Inc.

Timothy Lee Jacobs

Hadi W Purnomo

Sumit Batra

Kathleen Dege

Markus A Wegner

JonCarlo Gulbranson

Shau-Shiang Sunny Ja

Raymond Scott Chandler

Steven Simons Armato

Ivan Borges Oliveira

Mehul Jain

Russell John Allgor

Philip Mark Kaminsky

Ian Ward

Manik Kumar

David John Michael

Manish Jyoti

Hoda Parvin

Akhand Pratap Singh

Ruchit Aswin Shah

Georgios Patsakis

Karan Kumar

Chetan Mahendru

Vighnesh Naik

Ozlem Bilginer

Samyukta Sethuraman

Quico Pepjin Spaen

Setareh Mardan

Suresh Rangaswamy

Ankur Bansal

Agrim Jain

Wesley Herman

Hrushikesh Gandhi

Brent M Cervenka

Golbarg Kazemi Tutunchi

Richard Till

Sendhil Nathan

Owen D Perrine

Craig Endres

Chiranjive Mittal

Sorabh Kumar

Mahesh Kumar Sharma

Transportation handoffs for increased network density are described herein. In an example, a computer system generates first data indicating a first candidate network path associated with transporting a first load of items between an origin and a destination within a network. The first candidate network path directly connects the origin and the destination. The computer system generates second data indicating a second candidate network path indirectly connecting the origin and the destination. The computer system generates a first input including the first data and the second data to an optimization model and a second input about a second load of items to the optimization model. The computer system determines an output of the optimization model indicating, for the first load, a selected network path a resource assignment per segment of the selected network path. The computer system causes, based on the output of the optimization model, utilization of transportation resources.

Transportation handoffs for increased network density

Techniques for generating an item transmission portal are described herein. First information for a plurality of carriers may be maintained which includes a capacity for each carrier. Historical capacity requirements for geographic locations may be maintained. For each geographic location a forecast of future capacity requirements, for each week of a plurality of weeks, may be generated based on the historical capacity requirements. A plurality of documents may be generated for each geographic location based on the forecast and the first information. A value may be determined for each document based on a time and capacity commitment for each document, the historical capacity requirements, and the forecast. A user interface may be generated that includes the plurality of documents and corresponding determined values. The user interface may be updated to reflect acceptance of a specific document by a carrier in response to input received via the user interface.

Item transmission portal

A crew planning system includes a demand forecasting module and an optimization module. The system forecasts anticipated reserve demand levels, and determines suitable reserve staffing approaches to meet anticipated reserve demand. The forecasting is based upon probabilistic distribution models which take into account variability associated with reserve demand for a particular day. Via use of the crew planning system, reserve staffing expenses may be reduced and/or reserve demand may be met with a higher degree of probability.

Risk variables associated with reserve airline staffing levels

Managing the capacity of delivery lockers are disclosed herein. For example, by utilizing machine learning techniques, the capacity management system described herein may determine portions of a delivery locker to reserve for packages delivered at one or more delivery speeds. For example, the system may train one or more machine learning models to determine factors associated with a delivery demand, dwell time probability, and optimized capacity reservation of the delivery locker. Utilizing these factors, the system may determine a portion of the delivery locker to reserve for packages delivered at each available delivery speed.

Capacity management system for delivery lockers

A system and method of generating an transparent optimized shift schedule for a plurality of crew members that includes receiving a plurality of shift bids from the plurality of crew members, with each of the shift bids to be either classified as awarded or rejected, optimizing the transparent optimized shift schedule; and presenting, to each crewmember from the plurality of crewmembers who submitted a shift bid classified as a rejected shift bid, the reason code associated with his or her rejected shift bid; wherein presenting, to each crewmember from the plurality of crewmembers who submitted a shift bid classified as a rejected shift bid, his or her shift assignment from the optimized shift schedule and a reason code associated with his or her rejected shift bid provides transparency as to why each rejected shift bid was rejected.

Employee scheduling system providing transparency

Features related to systems and methods for network planning based on optimization modeling of equipment and item transmissions. The network planning may include a model to plan for transmission of items over a network to maximize overall efficiencies over alternative transmission modes. The model may produce a network plan that schedules transmissions, determines the number and types of equipment needed to support the transmissions, assigns equipment to specific locations, and optimally routes items throughout the network.

Network planning and optimization

Reserve airline staffing levels

Reserve forecasting systems and methods for airline crew planning and staffing

A system and method for real-time revenue management is provided. Data is received from a scheduling system pertaining to a network comprising a plurality of legs. A sub-network within the network may be identified, and optimization of the sub-network is triggered. A current inventory information value attributable to the identified sub-network is pro-rated, and an expected traffic value for the identified sub-network is calculated using a forecast of the expected demand, a standard deviation of the demand, a gamma distribution equation, and an unsatisfied demand value. An updated inventory information value for the identified sub-network is generated using the pro-rated inventory information value and an optimization equation, wherein the optimization equation generates a maximum revenue value by adjusting the expected traffic, and the updated inventory information value is transmitted to the scheduling system.

System and method for real-time revenue management

Growing community of inventors

Tempe, AZ, United States of America

Timothy Lee Jacobs