Join the 155,000+ IMP followers

railway-international.com

Automated Personnel Dispatch in Regional Rail Operations

AKN Eisenbahn GmbH and IVU Traffic Technologies AG integrate rule-based automation into crew scheduling to comply with revised collective agreements and increase planning efficiency.

  www.ivu.com
Automated Personnel Dispatch in Regional Rail Operations

AKN Eisenbahn GmbH has implemented automated personnel dispatch within its existing IVU.rail system to generate individual annual duty rosters in line with new labour agreement requirements. The cooperation extends long-standing use of IVU’s integrated rail planning software to include algorithm-based crew scheduling for regional passenger transport in northern Germany.

Context of the Cooperation
AKN operates a dense regional rail network in the Hamburg metropolitan area, requiring coordinated vehicle scheduling, crew assignment, and compliance with work-time regulations. A change in the applicable collective agreement mandated the preparation of individual annual duty rosters for each transport services employee from 2025 onward.

This requirement increased planning complexity due to the need to reconcile personal shift requests, qualification matrices, statutory work-time limits, and minimum rest periods within a fixed timetable structure. Manual roster generation would have required substantial additional planning resources and carried a higher risk of rule conflicts.

IVU Traffic Technologies, as supplier of the IVU.rail platform already used for operational planning and dispatch, extended the system with Automatic Personnel Dispatch (APD) functionality to address these constraints.

Technical Solution and System Architecture
IVU.rail forms part of IVU’s integrated suite for rail operations management. The APD module applies rule-based optimisation algorithms to generate duty schedules. Input parameters include:
  • Employee-specific qualifications and route authorisations
  • Collective agreement rules and statutory working-time limits
  • Minimum rest times and shift sequencing constraints
  • Individual availability and shift preferences
The system evaluates feasible combinations across the annual planning horizon and generates duty proposals at the push of a button. Constraint-checking mechanisms automatically identify conflicts that would be difficult to detect in manual spreadsheet-based processes.

Because APD is embedded in the existing digital infrastructure, roster generation remains synchronised with vehicle scheduling and timetable data, reducing interface inconsistencies between planning domains.

Deployment and Integration
Implementation was carried out within AKN’s existing IVU.rail environment, avoiding parallel system operation. Data migration focused on updating personnel master data, qualification records, and rule sets derived from the revised collective agreement.

Testing phases validated compliance with work-time regulations and ensured compatibility with ongoing operational control processes. Planning staff continue to supervise and adjust system-generated proposals, maintaining human oversight in exception cases.

Applications and Operational Impact
The solution is applied in regional rail passenger transport, where crew availability directly affects timetable stability. Automated duty generation supports:
  • Legally compliant annual roster creation
  • Faster recalculation in case of timetable adjustments
  • Reduced manual workload in dispatch departments
From a systems perspective, automation increases planning transparency by formalising rules within the software rather than relying on tacit knowledge. This contributes to process stability and reproducibility in industrial automation of rail workforce management.

The cooperation demonstrates how extending an existing digital platform can address regulatory changes without replacing core operational systems, thereby limiting integration risks while increasing scheduling precision.

www.ivu.com

  Ask For More Information…

LinkedIn
Pinterest

Join the 155,000+ IMP followers