Publica
Hier finden Sie wissenschaftliche Publikationen aus den FraunhoferInstituten. Solving sorting of rolling stock problems utilizing pseudochain structures in graphs
 Fidanova, S.: Recent advances in computational optimization : Results of the Workshop on 'Computational Optimization' and 'Numerical Search and Optimization' 2018 Cham: Springer, 2020 (Studies in computational intelligence 838) ISBN: 9783030227227 ISBN: 9783030227234 S.4559 
 International Workshop on Computational Optimization <11, 2018, Posen> Workshop on Numerical Search and Optimization <2018, Posen> 

 Englisch 
 Konferenzbeitrag 
 Fraunhofer SCAI () 
Abstract
Rearranging cars of an incoming train in a hump yard is a widely discussed topic. Sorting of Rolling Stock Problems can be described in several scenarios and with several constrains. We focus on the train marshalling problem where the incoming cars of a train are distributed to a certain number of sorting tracks. When pulled out again to build the outgoing train, cars sharing the same destination should appear consecutively. The goal is to minimize the number of sorting tracks. We suggest a graphtheoretic approach for this NPcomplete problem. The idea is to partition an associated directed graph into what we call pseudochains of minimum length. We describe a greedytype heuristic to solve the partitioning problem which, on random instances, performs better than the known heuristics for the train marshalling problem. In addition we discuss the TMP with bbounded sorting tracks.