## 6 Conclusions  
In this paper, we proposed using a crane dispatching policy to schedule the ASCs at the storage block of an automated container terminal. We also proposed a noisy optimization algorithm, named N-RTS, that can efficiently search for a good dispatching policy. Simulation experiments showed that our dispatching policy outperformed the online search method (OnS) by [5], in terms of both the quality of the crane schedules and the CPU times taken to derive the schedules.  
It has been generally suspected that deriving crane schedules of higher quality than those by OnS would be difficult, as the outcome of OnS is the result of an optimization search obtained through the significant investment of computational resources. The surprising success of the policy seems to be due to two reasons. First, the policy is not myopic (as are other simple heuristics), as it is optimized through simulations of long and diverse operation scenarios. Second, the scheduling horizon used by the policy is twice as long as that used by OnS (60 vs. 30 min). While OnS cannot afford to have a long horizon, as its optimization has to be completed in real time, the policy is free from such concern as it takes a small amount of time to build a schedule, given a long horizon.  
While our policy was very powerful and quick in usage, it was computationally demanding to search for a good policy, due to the heavy simulations involved. N-RTS makes this search efficient by smartly allotting samples to individuals for evaluation. Experiments with some benchmark functions revealed that 11% of all the competitions for survival selection, in which one looked obviously better than the others, were processed without using any additional samples. This savings contributed to enhanced search performance, by being invested in exploring more individuals in the search space. For the remaining cases, where the comparison results were not decisive, N-RTS invested additional samples in the better-looking individuals, to make their evaluations more reliable. This strategy turned out to be better than that adopted by NTGA, where the additional samples were never given to the newly born, but only to the old competitor from the population. Since the better-looking individual is more likely to win the survival selection, as seen from the last experiment, the samples invested in them are more likely to be accumulated to contribute to more reliable evaluations later. When the optimization problem at hand requires heavy simulations during the search, N-RTS would be a useful tool to find a solution effectively and efficiently.  
Figure 8: The winning probabilities of chosen individuals at different values of \(p_{c}\) during the search for an optimal solution of the Ackley function (\(\sigma=0.1\)) by N-RTS.  
**Author Contributions:** Conceptualization, J.K. and K.R.R.; methodology, J.K., E.J.H., Y.Y. and K.R.R.; software, J.K.; validation, J.K. and K.R.R.; formal analysis, J.K and K.R.R.; investigation, J.K. and K.R.R.; resources, J.K.; data curation, J.K. and K.R.R.; writing--original draft preparation, J.K.; writing--review and editing, K.R.R.; visualization, J.K.; supervision, K.R.R.; project administration, K.R.R.; funding acquisition, K.R.R. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant, funded by the Korea government (MSIT) (No. 2020-0-01450, Artificial Intelligence Convergence Research Center [Pusan National University]).  
Ethical review and approval were waived for this study, since no experimentation was carried out on human beings. The involvement of humans was limited to the completion of a questionnaire, for which informed consent was regularly obtained.  
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.  
Data sharing is not applicable to this article.  
The authors declare no conflict of interest.