Route Optimization Engine for the Real World
summary
A routing optimization engine for real-world vehicle routing (waste collection, deliveries) that handles many constraints (time windows, driver shifts, vehicle capacities, loading/unloading). The solution ingests OpenStreetMap data into PostgreSQL with PostGIS and pgRouting, uses OR-Tools metaheuristics to solve the NP-hard routing problems, orchestrates workflows with Apache Airflow on Kubernetes, and leverages the SciPy Python stack (Pandas, NumPy, scikit-learn) for processing.
AI-written summary · about
chapters (9)
- 0:00Introduction
- 2:08Real-world case
- 5:01Route optimization complexity
- 7:11Solving VRP
- 10:24Workflow and pipeline
- 13:11Execution example
- 16:23Cost matrix challenges
- 18:11Optimization algorithms
- 19:36Impact and conclusion
related talks
30mBuild on-demand logistics apps with Python, OR-Tools, and DecisionOps
39mSolving the vehicle routing problem for optimizing shipment delivery
23mRevolutionizing Secondary Mile: Transforming VRP with Meta Heuristics
15mComputational Mobility: Learning, Prediction, and Algorithms
20mAI on the Fast Lane: Smart Traffic Systems to Unclog our Cities
16mOptimized Routing with Satellite Imagery and Computer Vision
21mCombinatorial Optimization of Train Timetable Fulfilment After Incidents
15mThe turf wars on data - can be a matter of life and death