An Agent-Based Approach to Schedule Crane Operations in Rail-Rail Transshipment Terminals

  • Sam HeshmatiEmail author
  • Zafeiris Kokkinogenis
  • Rosaldo J. F. Rossetti
  • Maria Antónia Carravilla
  • José Fernando Oliveira
Part of the Lecture Notes in Economics and Mathematical Systems book series (LNE, volume 682)


Rail-rail transshipment terminals (RRTT) play the hub role in hub-and-spoke rail networks. In this type of terminals, containers are transshipped among freight trains by gantry cranes. This study aims at scheduling crane operations with an agent-based simulation approach to minimize the total transshipment time. The decisions to make include: the positions of containers on outbound trains; the assignment of container moves to cranes, as overlapping areas for the cranes’ operation are considered; the sequence of transshipments. The contribution of this paper is an agent-based approach to the crane operations scheduling problem in a RRTT framework. This approach is validated on a set of instances taken from the literature and the results compared against a variable neighbourhood descent algorithm.


Load Factor Ground Vehicle Variable Neighbourhood Descent Gantry Crane Container Move 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



Project “NORTE-07-0124-FEDER-000057” is funded by the North Portugal Regional Operational Programme, under the National Strategic Reference Framework (NSRF), through the European Regional Development Fund (ERDF), and by national funds, through the Portuguese funding agency, Fundação para a Ciência e a Tecnologia (FCT). Zafeiris Kokkinogenis is funded by FCT under the PhD grant SFRH/BD/67202/2009.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sam Heshmati
    • 1
    Email author
  • Zafeiris Kokkinogenis
    • 2
  • Rosaldo J. F. Rossetti
    • 2
  • Maria Antónia Carravilla
    • 1
  • José Fernando Oliveira
    • 1
  1. 1.INESC TEC, Faculty of EngineeringUniversity of PortoPortoPortugal
  2. 2.Artificial Intelligence and Computer Science Laboratory, Faculty of EngineeringUniversity of PortoPortoPortugal

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