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ApplicationAdvanced04.05
Networks, Games & Systems

Vehicle Routing

Plan routes for fleets with capacity, time windows, and uncertainty.

Overview

Vehicle routing turns fleet operations into a structured combinatorial decision: which stops go on which route, in what order, with which vehicle, and under which time, capacity, and service constraints.

Real VRP work is rarely just shortest distance. Time windows, driver rules, pickup-delivery precedence, stochastic travel times, and customer priorities usually determine whether a plan can actually run.

Core ideas

TSP

TSP is a core checkpoint for Vehicle Routing: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.

VRP

VRP is a core checkpoint for Vehicle Routing: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.

Time windows

Time windows is a core checkpoint for Vehicle Routing: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.

Pickup-delivery

Pickup-delivery is a core checkpoint for Vehicle Routing: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.

Large neighborhood search

Large neighborhood search is a core checkpoint for Vehicle Routing: define it concretely, attach units or rules where possible, and test whether stakeholders interpret it the same way.

How to use it

  1. 1Start with Last mile: write the decision, time horizon, actors, and objective in operational language.
  2. 2Translate the problem into TSP, VRP, and Time windows; define units and data sources for each one.
  3. 3Build a small instance of Vehicle Routing that can be solved or simulated by hand inspection before using full production data.
  4. 4Compare the recommendation against a baseline policy, not just against mathematical optimality.
  5. 5Document assumptions, sensitivity results, and the conditions under which the recommendation should be revisited.

Applications

Last mileTruckingField serviceWaste collection
  • Last mile: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.
  • Trucking: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.
  • Field service: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.
  • Waste collection: compare feasible policies, quantify the operating tradeoffs, and make the assumptions behind the recommendation visible.

Common pitfalls

  • Applying Vehicle Routing because the label sounds appropriate while leaving the actual decision boundary vague.
  • Treating TSP as a technical detail instead of a modeling choice that affects the recommendation.
  • Reporting one answer without showing sensitivity to demand, capacity, costs, or behavioral assumptions.
  • Ignoring implementation details such as data quality, explainability, ownership, and how users will override bad recommendations.

Resources

  • OR-Tools Routing

    Topic-specific source curated for Vehicle Routing.

  • NVIDIA cuOpt

    Open-source CUDA GPU-accelerated solver with Python and server support for routing problems such as TSP, VRP, and pickup-delivery.

  • Exploring NVIDIA cuOpt — Marvik

    Applied vehicle-routing walkthrough comparing cuOpt usage with hand-modeled Pyomo examples.

  • VRP-REP

    Topic-specific source curated for Vehicle Routing.

  • Google OR-Tools

    Practical toolkit for routing, assignment, CP-SAT, scheduling, flows, LP, and MIP.

  • MIPLIB

    Benchmark library for mixed-integer programming models and solver comparisons.

  • INFORMS Journal on Applied Analytics

    Applied OR case studies focused on implementation, adoption, and business impact.