Optimizing stacking policies using an MOEA for an automated container terminal

This paper proposes a method of optimizing a stacking policy using a multi-objective evolutionary algorithm (MOEA) for the containers in the stacking yard of an automated container terminal (ACT). Deciding stacking locations of the containers coming to the stacking yard is one of the critical operational problems in any container terminal because the throughput of the yard is very much dependent on that decision. Additionally, the containers are often moved to other locations within the yard, and these temporary locations also highly affect the throughput of the stacking yard. Therefore, the stacking policy dealt with in this paper takes account of both the locations for the incoming containers and those for temporary movement within the yard. Since the productivity of the yard of an ACT is measured by two conflicting criteria of the seaside and the landside service level, it is impossible to get a single best solution which maximizes both service levels simultaneously. Instead, we get a Pareto set of stacking policies using an MOEA. The experimental results using a simulation system have shown that the obtained Pareto set of stacking policies is diverse and satisfactory in view of both the seaside and the landside service level.