This hands-on session introduces network design optimization, a critical problem across industries such as supply chain, energy, and telecommunications, where decisions on facility locations and flows drive cost and service performance.

Through a simple example of warehouse placement and customer allocation, participants will learn core modeling concepts.

The session includes practical Python implementations using SCIP, and extends beyond standard formulations to cover multiple objectives, soft constraints, and uncertainty, reflecting real-world complexity and decision-making needs

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In this hands-on session, participants will learn how to model and solve routing problems using PyVRP (https://pyvrp.org/), an open-source Python package for vehicle routing optimisation.

We begin with simple examples, model and solve them using PyVRP, and then slowly increase the complexity by adding more realistic business constraints.

Participants gain experience applying PyVRP to realistic routing problems and
understanding the solutions.

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There is a solver out there that keeps winning OR challenges - and not enough people know about it!

In this hands-on session, we will explore Constraint Programming with CP-SAT.
We dive into several problems that could be modeled using MIP - but instead we will use Constraint Programming and CP-SAT and showcase for which scenarios it is superior to MIP.

What you'll learn:

• How to formulate CP models and solve them with CP-SAT

• Best practices for creating scalable formulations

• When to use CP-approaches over MIP

• A basic understanding of how CP-SAT works (Conflict Driven Clause Learning)

Tech stack: Google OR-Tools's CP-SAT (solving + modelling), Pydantic (data validation)

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