Optimizing rail staffing: A matheuristic approach

GSEM Professor Nicolas Zufferey co-authored an article with Marie-Sklaerder Vié and Stefan Minner published in OR Spectrum, a top-tier journal. The study addresses the locomotive and driver scheduling challenge faced by SBB Cargo AG, the Swiss national railway company. The efficient coordination of trains and drivers is critical for optimizing operations and minimizing costs. Their innovative approach combines mathematical optimization methods with heuristics, resulting in a matheuristic method. This hybrid approach strikes a balance between computational efficiency and solution quality.

Nicolas Zufferey received funding from SBB Cargo AG and the University of Geneva.

ABSTRACT

At the scale of Switzerland, the national railway company SBB Cargo AG has to schedule its locomotives and drivers in order to be able to pull all trains. Two objective functions are considered in a two-stage lexicographic fashion: (1) the locomotive and driver costs and (2) the driver time that is spent without driving. As the problem instances tend to reach big sizes (up to 1900 trains), we propose to schedule locomotives and drivers in a sequential way, thus having a sequence of smaller problems to solve. Moreover, for smaller instances, we also propose to schedule jointly locomotives and drivers in an integrated way, therefore increasing the search space but possibly leading to better solutions. In this paper, we present a mathematical formulation and model for the problem. We also consider the contract-related constraints of the drivers, and we propose a way to integrate some time flexibility in the schedules. Next, we propose an innovative matheuristic to solve the problem, relying on a descent local search and a rolling horizon decomposition. An important goal of this method is to explore thoroughly at which extent a general-purpose solver can be used on this problem. Finally, the benefits of each aspect of the model and of the method are analyzed in detail on the results obtained for 20 real SBB Cargo AG instances.

Access the study: A matheuristic for tactical locomotive and driver scheduling for the Swiss national railway company SBB Cargo AG

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