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This paper formulates the 3D Stochastic Stowage Plan-ning (3D SSP) problem. The key objective of 3D SSP is to minimize the number of container movements and maximize the ship´s stability considering multiple scenar-ios. The binary formulation of this problem is described and an alternative formulation, called Representation by Rules, is combined with a Genetic Algorithm and a Par-ticipatory Learning System. The robustness of the devel-oped framework is verified in a problem for which the corresponding binary representation demands 405,450,000 variables. Keywords : Stochastic Stowage Planning, Container Ship, Representation by Rules, Genetic Algorithm, Participa-tory Learning System. 1. Introduction Today, international sea freight container transportation and container terminals play a key role in the global transportation network. According to [21], over 60% of the world’s deep-sea general cargo is transported in con-tainers, and the routes between some countries are con-tainerized up to 90%. The improvement of the operational efficiency of con-tainer terminals is essential to handle the increasing flow of containers that has occurred over the last years. Re-ducing the time ships must spend in port makes container seaports more competitive because they can offer lower rates for loading and discharging. Therefore, an essential competitive advantage is the reduction of the time in port of the container ships, and of the costs of the transship-ment process itself. The optimization of seaport opera-tions problems should be defined and methods of solution proposed for such tasks.

Solution of the 3D Stochastic Stowage Planning for Con-tainer Ships through Representation by Rules