Shunting operations at flat yards: Retrieving freight railcars from storage tracks


Jaehn, Florian ; Otto, Alena ; Seifried, Kilian



DOI: https://doi.org/10.1007/s00291-017-0495-x
URL: https://link.springer.com/article/10.1007/s00291-0...
Weitere URL: https://www.researchgate.net/publication/321256114...
Dokumenttyp: Zeitschriftenartikel
Erscheinungsjahr: 2018
Titel einer Zeitschrift oder einer Reihe: OR Spectrum
Band/Volume: 40
Heft/Issue: 2
Seitenbereich: 367-393
Ort der Veröffentlichung: Berlin ; Heidelberg
Verlag: Springer
ISSN: 0171-6468 , 1436-6304
Sprache der Veröffentlichung: Englisch
Einrichtung: Fakultät für Betriebswirtschaftslehre > ABWL u. Logistik (Fleischmann 2009-)
Fachgebiet: 380 Handel, Kommunikation, Verkehr
510 Mathematik
650 Management
Fachklassifikation: MSC: 90B06,
Abstract: In this paper, we study the railcar retrieval problem (RRT) in which specified numbers of certain types of railcars must be withdrawn from the storage tracks of a flat yard. This task arises as part of daily operations of railcar maintenance workshops. The objective is to minimize the total cost of the shunting operations. We describe the RRT formally, present a mixed-integer program formulation and prove the general case to be NP-hard. For some special cases, exact algorithms with polynomial runtimes are proposed. We also analyze several intuitive heuristic solution approaches motivated by current real-world planning routines. We evaluate their average performances in simulations with different scenarios and provide their worst-case performance guarantee. We show that although the analyzed heuristics result in much better solutions than the naïve planning approach, on average, they are still 30–50% from the optimal objective value and may result in costs up to 14 times higher in the worst case. Therefore, we conclude that optimization should be implemented in practice to save valuable resources. Furthermore, we analyze the impacts of yard layout and the widespread organizational routines on retrieval costs in detailed computational experiments.




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