## 6 Conclusions
The newly proposed method in this paper is suitable for large-throughput and busy-operation container terminals because more than one ET may arrive during most of the operation rounds, while it is difficult to achieve good optimization results for small-throughput and loose-operation container terminals because not more than one ET arrives at during most of the operation rounds. Since the SCRP is an NP-hard problem, reducing the problem size is the main idea to improve its solution efficiency. In this paper, focusing on only one stage of the overall pickup operation process of imported containers significantly reduces the problem size, which provides ideas for efficiently solving large-scale instances. Meanwhile, this idea can be used to solve other NP-hard problems related to container terminal yard operation optimization. However, the method proposed in this paper has two potential limitations: retrieval priority of the containers and target containers' pickup sequence during each real-time operation round. First, the retrieval priority of containers is crucial for estimating the ENBC and deciding the new slot for the blocking containers. But, this paper relies on the appointment information from the TAS to determine containers' retrieval priority. Theoretically, the greater the proportion of containers within a bay booked in advance, the higher the accuracy of the solution. Suppose none of the containers within a bay have been booked in advance. In that case, the method proposed in this paper will degenerate into a general SCRP, except that the problem size is reduced, while the accuracy of the solution is unknown. The second limitation is caused by the procedure GenerateSequence in algorithm \(SPFH\), and its solution approach has been mentioned in Section 5.2.2. In view of the limitation and applicability, further research can be carried out in the following directions based on this paper. First, using machine learning algorithms to mine and analyze the operation logs of container terminals to predict the customers' retrieval time and infer the containers' pickup priority is a worthwhile area of research for improving the solution quality of SCRP due to insufficient appointment information. Alternatively, mining the operation logs of the container terminal to derive the probability distribution of customers' retrieval for estimating the ENBC is a valuable topic, too. Second, by extending the new method proposed in this paper to the CRP of export containers, the container pre-marshaling problem (PMP) or the storage allocation problem (SAP) of import containers will be our next major concern. Moreover, the integrated optimization of the container relocation problem, yard crane scheduling, and balancing the waiting time of ETs is a challenging topic for future studies. Conceptualization, Q.Z. ; Methodology, S.Z. ; Writing--original draft, S.Z. ; Writing--review & editing, Q.Z. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The data presented in the study are openly available in GitHub at [[https://github.com/zhou-sf/rt-scrp](https://github.com/zhou-sf/rt-scrp)]([https://github.com/zhou-sf/rt-scrp](https://github.com/zhou-sf/rt-scrp)) (accessed on 5 January 2024). The authors declare no conflicts of interest.