Case Study: Swiss Federal Railways achieves faster, scalable automated timetable planning with Hazelcast

How the Swiss Federal Railways (SBB CFF FFS) Uses In-Memory Technology for Faster Train Scheduling

Swiss Federal Railways (SBB) needed to scale and automate one of Europe’s most complex timetable-planning problems for a dense network that runs about 3,000 passenger trains per day. The challenge was fast, reliable access to large computed timetable datasets so planners could evaluate algorithmically generated schedules instead of relying on slow, error-prone manual work. To meet that need, Swiss Federal Railways standardized on the Hazelcast Platform as an in-memory, high-performance data layer.

Hazelcast was deployed in SBB’s private OpenShift cloud as a microservices-backed, in-memory cache (including near-cache optimizations) to accelerate data access and enable the prototype to scale to the full national timetable. The Hazelcast implementation dramatically sped up data access—reducing update latency from several minutes to near real-time—enabled interactive dashboards for planners, and improved overall system performance; SBB did note initial tuning work on cache eviction and OpenShift integration but achieved a scalable, faster automated scheduling workflow with Hazelcast.

Swiss Federal Railways

Adrian Burri

Software Engineer