Re-marshaling export containers in port container terminals
ICC&IE Selected papers from the 22nd ICC&IE conference on Computers & industrial engineering
An analysis of cooperative coevolutionary algorithms
An analysis of cooperative coevolutionary algorithms
Archive-based cooperative coevolutionary algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
An optimization model for the container pre-marshalling problem
Computers and Operations Research
Determination of storage locations for incoming containers of uncertain weight
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Planning for intra-block remarshalling in a container terminal
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
Integrated intelligent techniques for remarshaling and berthing in maritime terminals
Advanced Engineering Informatics
Intelligent planning for allocating containers in maritime terminals
Expert Systems with Applications: An International Journal
A decision support system for managing combinatorial problems in container terminals
Knowledge-Based Systems
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The productivity of a container terminal is highly dependent on the efficiency of loading the containers onto the vessels. The efficiency of container loading depends on how the containers are stacked in the storage yard. Remarshaling refers to the preparatory task of rearranging the containers to maximize the efficiency of loading. In this paper, we propose cooperative coevolutionary algorithms (CCEAs) to derive a plan for remarshaling in an automated container terminal. CCEAs efficiently search for a solution in a reduced search space by decomposing a problem into subproblems. Our CCEA decomposes the problem into two subproblems: one for determining where to move the containers and the other for determining the movement priority. Simulation experiments show that our CCEA can derive a better plan in terms of the efficiency of both loading and remarshaling than other methods which are not based on the notion of problem decomposition.