Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Planning the operational procedures in a railway marshalling yard is a complex problem. When a train arrives at a marshalling yard, it is uncoupled at an arrival yard and then its cars are rolled to a classification yard. All cars should eventually be rolled to the classification track that has been assigned to the train they're supposed to depart with. However, there is normally not enough capacity to compound all trains at once. In Sweden, cars arriving before a track has been assigned to their train can be stored on separate tracks called mixing tracks. All cars on mixing tracks will be pulled back to the arrival yard, and then rolled to the classification yard again to allow for reclassification. Today all procedures are planned by experienced dispatchers, but there are no documented strategies or guidelines for efficient manual planning. The aim of this paper is to examine operational planning strategies that could help dispatchers find a feasible marshalling schedule that minimizes unnecessary mixing. In order to achieve this goal, two different online planning strategies have been tested using deterministic and stochastic simulation. The Hallsberg marshalling yard was used as a case study, and was simulated for the time period between December 2010 and May 2011. The first tested strategy simply assigns tracks to trains on a first come-first served basis, while the second strategy uses time limits to determine when tracks should be assigned to departing trains. The online planning algorithms have been compared with an offline optimized track allocation. The results from both the deterministic and the stochastic simulation show that the optimized allocation is better than all online strategies and that the second strategy with a time limit of 32 hours is the best online method.
Planning the operational procedures in a railway marshalling yard is a complex problem. When a train arrives at a marshalling yard, it is uncoupled at an arrival yard and then its cars are rolled to a classification yard. All cars should eventually be rolled to the classification track that has been assigned to the train they're supposed to depart with. However, there is normally not enough capacity to compound all trains at once. In Sweden, cars arriving before a track has been assigned to their train can be stored on separate tracks called mixing tracks. All cars on mixing tracks will be pulled back to the arrival yard, and then rolled to the classification yard again to allow for reclassification. Today all procedures are planned by experienced dispatchers, but there are no documented strategies or guidelines for efficient manual planning. The aim of this paper is to examine operational planning strategies that could help dispatchers find a feasible marshalling schedule that minimizes unnecessary mixing. In order to achieve this goal, two different online planning strategies have been tested using deterministic and stochastic simulation. The Hallsberg marshalling yard was used as a case study, and was simulated for the time period between December 2010 and May 2011. The first tested strategy simply assigns tracks to trains on a first come-first served basis, while the second strategy uses time limits to determine when tracks should be assigned to departing trains. The online planning algorithms have been compared with an offline optimized track allocation. The results from both the deterministic and the stochastic simulation show that the optimized allocation is better than all online strategies and that the second strategy with a time limit of 32 hours is the best online method.
No abstract
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.