Simulation and analytical techniques are used to test the impact
of the yard and block layout decisions on specific performance
measures in the terminal. Chen, Fu, Lim, and Rodrigues (2004) focus
on minimizing the utilized yard space while satisfying space
requirements. The problem is transformed into a directed acyclic
graph and used in several metaheuristics. A Genetic Algorithm
(GA)-based heuristic outperformed all other heuristics, finding results
within 8% of a simple lower bound in most cases. Lim and Xu
(2006) propose a critical-shaking neighborhood search to overcome
the long runtimes and outperform the results of Chen et al.
(2004). Petering (2009) uses simulation to determine the effect of
storage block width on the average quay crane work rate (GCR) on
a transshipment container terminal with an Asian layout and
blocks dedicated to groups. For each simulation run, the total yard
storage capacity, number of gantry cranes (GCs) and Yard Trucks
(YTs) deployed, and GC handling speeds are considered constant.
The simulation uses control policies of Petering and Murty
(2009). It is concluded that: (1) GCR is concave with respect to
the block width; (2) GCR improves as the shape of the terminal becomes
more square; and (3) the optimal block width decreases
when more gantry cranes are deployed.