--- title: "User Guide & Key Workflows" description: "Explore TQRouting's core optimisation workflows, including defining routing problems, configuring constraints, running the solver and interpreting results, with links to documentation and examples." url: https://help.terraquantum.io/en/articles/210-user-guide-and-key-workflows collection: "TQrouting" updated: 2026-11-02 language: en --- # User Guide & Key Workflows Explore TQRouting's core optimisation workflows, including defining routing problems, configuring constraints, running the solver and interpreting results, with links to documentation and examples. TQRouting provides a flexible Python SDK for solving vehicle routing problems with real-world operational constraints. This guide introduces the main workflows and capabilities, with links to detailed technical documentation and practical examples. ## 1\. Define Your Routing Problem Start by defining the locations, customers and vehicles involved in your routing operation. A typical routing problem includes: - **Customers:** Locations to visit, delivery or pickup requirements and service times. - **Vehicles:** Fleet size, capacity, starting locations and operating restrictions. - **Depots:** Locations where vehicles begin or end their routes. - **Constraints:** Operational requirements such as time windows, vehicle capacities and route limits. TQRouting supports defining problems directly in Python or loading structured input data. **Documentation:** [Defining a Problem](https://routing-docs.terraquantum.io/problem-definition/) ## 2\. Configure Distance & Duration Matrices TQRouting uses travel distances and durations to calculate optimised routes. You can provide your own matrices or generate them using supported integrations. Available options include: - **Precomputed matrices:** Supply your own distance or travel-time data. - **Euclidean distances:** Calculate distances using Cartesian coordinates. - **OSRM:** Generate road-network distances and travel times. - **Google Maps:** Use Google Maps routing data. The appropriate approach depends on your routing environment and data requirements. **Documentation:** [Distance & Duration Matrices](https://routing-docs.terraquantum.io/matrices/) ## 3\. Configure & Run the Solver Once your routing problem is defined, configure the optimisation settings and execute the solver. Configuration options include optimisation time limits, computational resources and solver behaviour. A typical workflow involves: 1. Prepare your routing problem and input data. 2. Configure the relevant optimisation settings. 3. Run the solver using the TQRouting Python SDK. 4. Review the resulting solution and performance metrics. **Documentation:** [Solver Configuration](https://routing-docs.terraquantum.io/solver/) ## 4\. Explore Routing Constraints & Features TQRouting supports a range of routing scenarios and operational requirements.

Capability

Description

Capacity Constraints

Account for vehicle weight or volume limitations

Time Windows

Schedule visits within specified time periods

Multi-Trip Routes

Allow vehicles to return to a depot and complete additional trips

Route Limits

Restrict maximum route duration or distance

Pickup & Delivery

Combine collection and delivery requirements

Heterogeneous Fleets

Optimise routes across vehicles with different capacities or characteristics

Multi-Objective Optimisation

Balance travel duration and distance

Prize-Collecting

Prioritise selected visits based on their value

Features can be combined to model more complex routing scenarios. **Documentation:** [TQRouting Features](https://routing-docs.terraquantum.io/) ## 5\. Understand & Interpret Results After running an optimisation, TQRouting returns a solution containing the generated routes and associated metrics. Results can be used to: - Review which customers are assigned to each route. - Inspect route sequences and vehicle assignments. - Check solution feasibility. - Evaluate travel duration and distance. - Compare solutions against operational objectives. **Documentation:** [Reading Results](https://routing-docs.terraquantum.io/results/) ## 6\. Examples & Additional Resources For practical examples and detailed API guidance, explore the following resources:

Resource

Link

Complete Documentation

https://routing-docs.terraquantum.io/

Quick Start Guide

https://routing-docs.terraquantum.io/quickstart/

Problem Definition

https://routing-docs.terraquantum.io/problem-definition/

Solver Configuration

https://routing-docs.terraquantum.io/solver/

GitHub Examples

https://github.com/terra-quantum-public/tqrouting-examples

The GitHub repository includes example notebooks demonstrating common routing workflows. ## 7\. Need Help? For questions about routing problem configuration, solver behaviour or interpreting results, visit the [Terra Quantum Help Centre](https://help.terraquantum.io/). Our Help Centre provides technical documentation, knowledge articles and AI-assisted support, with access to our Support team when needed. Alternatively, email [support@terraquantum.swiss](mailto:support@terraquantum.swiss). **Related articles** - TQRouting — Overview & Getting Started - TQRouting — Troubleshooting & FAQs - Licensing & Access - Contacting Support