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Route Optimization

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A collections of resources related to route optimization with NVIDIA cuOpt




September 19, 2022
Helm Charts

NVIDIA cuOpt™ is an Operations Research optimization API using AI to help developers create complex, real-time fleet routing solutions.

Key features of NVIDIA cuOpt:

  • Dynamic Rerouting : Rerun models and adjust for changes like down drivers, inoperable vehicles, traffic/weather disruptions, and the addition of new orders—all within SLA time constraints. Route 1,000 packages in 10 seconds instead of 20 minutes (that’s 120X faster), with the same level of accuracy.

  • World-Record Accuracy : Achieve world-record accuracy with a 2.98% error gap on the Gehring & Homberger benchmark.

  • Scale Seamlessly : Scale out to 1000s of nodes to facilitate computationally heavy use cases. NVIDIA cuOpt performs better than SOTA solutions to address innovative use cases not otherwise possible today.

This NGC collection contains a containerized version of the cuOpt library that can be run as a Python SDK or RESTful microservice. In addition, a helm chart is made available for Kubernetes based deployments.

Example notebooks and deployment scripts can be found on GitHub : NVIDIA/cuOpt-Resources

Latest Release

Change Log for 22.08
cuOpt 22.08.00 (15 Aug 2022)

🚨 Breaking Changes

  • Fix server on handling pick-up and delivery (#612)
  • Run only relevant tests based on the ChangeList (#592)

🐛 Bug Fixes

  • Fix python tests for max lateness and max distance (#648)
  • Reverting to kmeans_deprecated (#622)
  • Fix server on handling pick-up and delivery (#612)
  • Update dependencies to 22.08 and add version update script (#604)
  • Fix drop infeasible orders option (#599)
  • Fix race condition in insertion heuristic (#593)
  • Use order locations when break locations are set (#590)
  • Fix notebook cost matrix image (#589)
  • Fix file extensions in handling PDP test cases (#575)

📖 Documentation

  • Adding examples to docs and updating docs (#563)

🚀 New Features

  • Add a notebook to demonstrate the usage of multi cost matrix for mixed fleet modeling (#627)
  • Implement vehicle to order matching constraints (#609)
  • Configfile save (#607)
  • Adds multi depot, vehicle break and precedence constraint support to server (#588)
  • Add support for vehicle types and multiple cost/constraint matrices (#569)
  • Implement a script to model dynamic optimization in the context of pickup and delivery problem (#561)
  • cuOpt available in launchpad

🛠️ Improvements

  • New Pick-up and Delivery algorithm with improved accuracy (#524)
  • Make vehicle order matching API consistent with other APIs (#635)
  • Adding container environment set-up scripts and utilities for running container (#620)
  • Feature and API testing in python (#616)
  • Enable codecoverage for cuOpt Python (#611)
  • Add an option to disable tabu search in SAT solver (#610)
  • Update style checks and add pre-commit support (#606)
  • Adds sync endpoint to server and supports order locations (#603)
  • Pin max version of cuda-python to 11.7.0 (#595)
  • Run only relevant tests based on the ChangeList (#592)
  • Adds max distance per route, objective function and skip first trip support to async server (#579)
  • Add container builder (#574)


Additional Resources

NVIDIA also provides a course on the basics of using cuOpt through our Deep Learning Institute platform. Users can work with cuOpt interactively on hardware provided by NVIDIA hosted in the cloud.

Access the course here. Users must have an developer account and be signed in.

By pulling and using the containers or Helm charts, you accept the terms and conditions of this End User License Agreement.