Amazon Technologies, Inc.

Manik Kumar

Ivan Borges Oliveira

Philip Mark Kaminsky

Georgios Patsakis

Phillip Oliver Kriett

Akhand Pratap Singh

Timothy Lee Jacobs

Karthik Ramakrishnan

Ian Ward

Suresh Rangaswamy

Quico Pepijn Spaen

Diwakar Tiwari

Vighnesh Naik

Golbarg Kazemi Tutunchi

Quico Pepjin Spaen

Andrew V Goldberg

Sendhil Nathan

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

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

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Woodinville, WA, United States of America

Manik Kumar