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

Marie-Sklaerder Vié (), Nicolas Zufferey () and Stefan Minner
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Marie-Sklaerder Vié: University of Geneva
Nicolas Zufferey: University of Geneva

OR Spectrum: Quantitative Approaches in Management, 2023, vol. 45, issue 4, No 2, 1113-1151

Abstract: 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 really 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.

Keywords: Locomotive and driver scheduling; Problem decomposition for sequential optimization; Matheuristics; Lexicographic objectives (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s00291-023-00729-9

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