Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
P-Complete Approximation Problems
Journal of the ACM (JACM)
Discrete Applied Mathematics
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
A Survey of Optimization Models for Train Routing and Scheduling
Transportation Science
Shunting of Passenger Train Units in a Railway Station
Transportation Science
The over-constrained airport gate assignment problem
Computers and Operations Research
A tabu search heuristic for the quay crane scheduling problem
Journal of Scheduling
An assignment model for dynamic load planning of intermodal trains
Computers and Operations Research
Scheduling Freight Trains in Rail-Rail Transshipment Yards
Transportation Science
Coevolving Memetic Algorithms: A Review and Progress Report
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
New bounds and algorithms for the transshipment yard scheduling problem
Journal of Scheduling
A Survey on Container Processing in Railway Yards
Transportation Science
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In modern rail---rail transshipment yards huge gantry cranes spanning all railway tracks allow for an efficient transshipment of containers between different freight trains. This way, multiple trains loaded with cargo for varying destinations can be consolidated to a reduced number of homogeneous trains, which is an essential requirement of hub-and-spoke railway systems. An important problem during the daily operations of such a transshipment yard is the train location problem, which assigns each train of a given pulse to a railway track (vertical position) and decides on each train's parking position on the track (horizontal position), so that the distances of container movements are minimized and the overall workload is equally shared among cranes. For this problem a mathematical model is presented; different heuristic solution procedures are described and tested in a comprehensive computational study. The results show that our procedures allow for a remarkable reduction of train processing time compared with typical real-world train location policies.