## 5 Conclusions and Future Works  
This paper presented an extension of a methodology based on rules representation to solve the integration of the MPSP and the CRP at port yards.  
As a contribution, the entire methodology was revised to provide a simpler representation of the problem. Some unpublished rules were presented, and a three-dimensional container ship was included. We were able to provide a faster evaluation of the possible actions via rules representation, whose associated binary model would not be feasible for a container yard with dimensions compatible with large ports.  
As a result, good sequences of feasible movements were obtained within a short computational time. Moreover, we have demonstrated in detail that the container relocation problem should be considered when solving the multi-port stowage planning problem to ensure an efficient operation between the ship and port and prevent any loss of money.  
For future works, we hope to extend the entire problem, including the scheduling of quay cranes, yard cranes, and vehicles, using a simulation and its respective rules. Another possible development is to couple the berth allocation problem and include the management of more than one ship in the simulation process. Finally, this simulation scheme also enables the consideration of uncertainty in processing times and the arrival of all equipment and structures related to the problem.  
What-if scenarios varying the flow of containers at certain ports could be another interesting approach to effectively support the decision-making process.  
Another objective function could also be considered, such as the distance traveled by the containers or total time to perform the operations. To solve a problem considering multiple objectives, multi-objective evolutionary algorithms such as NSGA or SPEA variants could also be implemented and have their results compared to the genetic algorithm proposed.  
Finally, to improve performance, the problem could be employ a heuristic that considers groups of containers, and parallel processing could be implemented to provide faster evaluation of individuals through simulation. Furthermore, a comparison with other meta-heuristics in terms of computational performance could be carried out.  
Conceptualization, A.T.d.A. and T.O.; Data curation, C.J.; Formal analysis, A.T.d.A.; Funding acquisition, T.O.; Investigation, C.J. and T.O.; Methodology, C.J. and A.T.d.A.; Project administration, T.O.; Resources, T.O.; Software, C.J. and A.T.d.A.; Supervision, A.T.d.A. and T.O.; Validation, A.T.d.A. and T.O.; Visualization, C.J.; Writing--original draft, C.J.; Writing--review & editing, A.T.d.A. and T.O. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Sao Paulo Research Foundation (FAPESP) (process: 2015/24295-5), and it benefited from a Ph.D. grant from the Coordination for the Improvement of Higher Education Personnel (CAPES).  
Not applicable.  
All implementations and test instances will be provided at github after approval.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.