Willem Feijen
Willem Feijen joined Lynxx 1.5 years ago, bringing an academic background to practical logistics. He earned his PhD from Centrum Wiskunde & Informatica (CWI) in 2024, where his research focused on integrating Machine Learning into applied optimization algorithms. At Lynxx, he focuses on developing algorithms to create train schedules.
Session
Modern railway systems operate under tight capacity constraints, especially during planned maintenance. In this talk, we present a Python-based timetable optimization system that generates feasible alternative schedules while staying as close as possible to the original plan.
We walk through how a real-world optimization problem, based on the Periodic Event Scheduling Problem (PESP) and Station Capacity Model (SCM), can be translated into a scalable Python application. The talk covers modeling decisions, solver integration, and practical trade-offs between solver-agnostic frameworks (Pyomo) and solver-specific implementations (Gurobipy).
Beyond the optimization model itself, we highlight lessons learned from building and maintaining an optimization codebase, including object-oriented design, and handling growing model complexity.
This talk is aimed at data scientists, operations researchers, and software developers interested in applying optimization techniques in Python to real-world systems.
Attendees will leave with practical insights into modeling, implementation choices, and scaling optimization workflows in Python.