About the speaker
Roberto Maria Rosati is a postdoctoral researcher at the WU Vienna University of Economics and Business where he is working on optimization for railway transportation. In 2024, he received his PhD degree with honors from the University of Udine. His main research interests concern the design of stochastic local search methods for complex optimization problems. Among his research contribution, the multi-neighborhood simulated annealing for Sports Timetabling developed by him and his co-authors ranked second in the ITC2021 competition, and he was among the finalists for the AIRO Young Dissertation Award 2024.
Abstract
Railways are a backbone infrastructure for passenger and freight transport. Disruptions, however, happen frequently because of unexpected events, thus requiring the rescheduling of involved resources.
In this talk, we give an introduction to the problems that railway companies face when a disruption hit the network, and then we focus on the Rolling Stock Rescheduling Problem (RSRP), bringing an example of a real-world case from the Austrian freight transportation setting. We present mathematical models for the RSRP and propose a novel method, based on multi-neighborhood local search, that is able to obtain competitive solutions in short computing times, as required in the real-world application.