Amazon Technologies, Inc.

Philip Mark Kaminsky

Ivan Borges Oliveira

Manik Kumar

Phillip Oliver Kriett

Ruchit Aswin Shah

Timothy Lee Jacobs

Steven Simons Armato

Karthik Ramakrishnan

Manish Jyoti

Georgios Patsakis

Akhand Pratap Singh

Wesley Herman

Hrushikesh Gandhi

Andrew V Goldberg

Quico Pepijn Spaen

Diwakar Tiwari

Chiranjive Mittal

Niels Krohn

Brent M Cervenka

Subhadeep Gupta

Vishal Verma

Sorabh Kumar

Agrim Jain

Ankur Gupta

Mahesh Kumar Sharma

Dorothy Mary Watts

Priyanka Prakash Mhatre

Karan Kumar

Mohammadmohsen Moarefdoost

Chetan Mahendru

A computer system may provide a plurality of load records for presentation in a user interface provided at a first plurality of user devices. The user interface may enable searching for a plurality of loads represented by the plurality of load records. The computer system may also determine, for a load record of the plurality of load records and based at least in part on inputting contextual information into a predictive model, an acceptance prediction indicating a probability that a load record of the plurality of load records will be accepted by a user. The computer system may also provide at least a portion of the plurality of load records for presentation in the user interface provided at a second plurality of user devices associated with a second plurality of users. The portion of the plurality of load records may include the load record including an updated value of an attribute of the load record that is dependent on the acceptance prediction.

Dynamic generation and presentation of load records for load allocation

One challenge for middle-mile route planning is that the set of loads changes significantly between daily planning and execution. Systems and methods are provided for optimizing a transportation plan for a transportation network based on these load changes. The disclosed system re-optimizes a solution by starting from a previously existing plan and previously generated columns (e.g., candidate routes). The disclosed techniques significantly improve the compute time of the system to generate transportation plans that are optimized accordingly to an optimization parameter. The system takes into account the current execution status associated with a given entry of the plan to determine whether the entry should be re-optimized. Entries corresponding to tours that have already commenced, may be at least partially ignored for re-optimization consideration. The disclosed techniques enable state-aware, adaptive re-optimization for even tours that are in-progress or have been tendered.

Techniques for reactive route planning

Techniques for planning resources using block and route information are described. In an example, a computing system determines a demand for item transportation expected during a planning horizon. The computing system determines information about a pre-planned transportation resource available during the planning horizon and costs associated with the pre-planned transportation resource. The computing system uses an optimization model to determine a block having a time length, a tour to transport, during the block, a first portion of the demand using the pre-planned transportation resource, and a second portion of the demand to be transported using an on-demand transportation resource. The computing system indicates, to a first computing device of the pre-planned transportation resource, an assignment of the block to the pre-planned transportation resource.

Resource planning using block and route information

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

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El Cerrito, CA, United States of America

Philip Mark Kaminsky