@InProceedings{huynh10a,
  author =	 {Nathan Huynh and Jos\'{e} M. Vidal},
  title =	 {An Agent-Based Approach to Modeling Yard Cranes at
                  Seaport Container Terminals},
  booktitle =	 {Proceedings of the Symposium on Theory of Modeling
                  and Simulation},
  year =	 2010,
  abstract =	 {Due to environmental concerns, terminal operators at
                  seaport container terminals are increasingly looking
                  to reduce the time a truck spends at the terminal to
                  complete a transaction. For terminals that stack
                  their containers, the solution may seem obvious: add
                  more yard cranes to reduce trucks’ wait time in the
                  yard. However, the high cost of these cranes often
                  prohibits terminal operators from freely buying
                  more. Another reason is because there is no clear
                  understanding of how the yard cranes’ availability
                  and service strategy affect truck turn time. This
                  study introduces an agent-based approach to model
                  yard cranes for the analysis of truck turn time with
                  respect to service strategy. It is accomplished by
                  modeling the cranes as utility-maximizing
                  agents. This study has identified a set of utility
                  functions that properly capture the essential
                  decision making process of crane operators in
                  choosing the next truck to provide service to. The
                  agent-based model is implemented using NetLogo, a
                  cross-platform multi-agent programmable modeling
                  environment. Simulation results show that the
                  distance-based service strategy produces the best
                  results in terms of average waiting time and the
                  maximum waiting time of any truck.},
  url = 	 {http://jmvidal.cse.sc.edu/papers/huynh10a.pdf},
  comment = 	 {36\% acceptance rate}
}