Planung der Instandhaltung

This paper addresses the complex iterative process railway managers currently use to sequence necessary interventions 2-10 years in advance, which requires extensive knowledge of costs, service impacts, locations, and failure risks. The authors propose a mixed integer linear programming algorithm that optimizes multicomponent railway intervention programs by maximizing net benefit while considering possession windows, costs, service impacts, and interdependencies, demonstrating up to 58% improvement in net benefit when tested on a 25-km Swiss railway network.

This manuscript presents a digital methodology that uses condition-based modeling and Bayesian filters to generate comprehensive overviews of railway track interventions, including required maintenance, scheduling windows, and associated costs for entire railway networks. The approach was tested on a 25 km Swiss regional railway network and shows potential to improve early-stage planning efficiency, optimize preventive maintenance scheduling, and reduce corrective intervention costs.