Difference between revisions of "Freight consolidation"

From Climate Change AI Wiki
Jump to navigation Jump to search
 
Line 1: Line 1:
 
{{Stub}}
 
{{Stub}}
  +
  +
{{Disclaimer}}
   
 
Bundling shipments together through freight consolidation can dramatically reduce the number of trips and associated GHG emissions. ML can optimize complex relationship between the various dimensions involved in shipping decisions, such as shipment mode and origin-destination pairs.
 
Bundling shipments together through freight consolidation can dramatically reduce the number of trips and associated GHG emissions. ML can optimize complex relationship between the various dimensions involved in shipping decisions, such as shipment mode and origin-destination pairs.

Latest revision as of 14:54, 26 August 2021

🌎 This article is a stub, and is currently under construction. You can help by adding to it!

This page is part of the Climate Change AI Wiki, which aims provide resources at the intersection of climate change and machine learning.

Bundling shipments together through freight consolidation can dramatically reduce the number of trips and associated GHG emissions. ML can optimize complex relationship between the various dimensions involved in shipping decisions, such as shipment mode and origin-destination pairs.

Background Readings[edit | edit source]

Community[edit | edit source]

Libraries and Tools[edit | edit source]

Data[edit | edit source]

Future Directions[edit | edit source]

References[edit | edit source]