# Deriving Situation-Adaptive Policy for Container Stacking in an Automated Container Terminal
## 8 Concluding Remarks  
We derived a dynamic or situation-adaptive stacking policy and compared its performance with that of a static policy that is insensitive to the situation change. The result of simulation experiments tells us that the dynamic policy clearly outperforms the static policy. In the experiments, the policy was optimized using randomly generated operation scenarios and then tested using a different set of scenarios separately generated from the same distribution. This separation was an effort to guarantee the generality of the observed performance of the policy in similar situations. However, the policy needs to be newly optimized and tested for being applicable to a new container terminal whose distribution of the operations is different from that of our target container terminal.  
The way we proposed to obtain a dynamic policy is to synthesize a new policy from two boundary policies: one for a very low and the other for a very high workload. For the synthesis, we recommended a linear interpolation of the two boundary policies. We used a GA-based search algorithm to optimize not only the two boundary policies but also the two threshold values for distinguishing the very low and very high workloads. The idea of using linear interpolation is based on the assumption that the values of the weight vectors in the policy are linearly related to the seaside workload, which is the only indicator we consider to represent the situation. Although this linearity assumption might not be really correct, our empirical study has shown that the method based on linear interpolation results in a significant improvement over the previous works. As emphasized toward the end of the literature review in Section 3, no previous work has ever tried to solve the kind of problem that we deal with in this paper. The linearity assumption, however, is not scalable to large problems because it needs boundary policies whose number increases exponentially with the number of situation indicators. Our future work, therefore, will be to drop the linearity assumption and use a multi-layer perceptron to represent a policy instead of the linear scoring functions so that we do not need to use boundary policies. Another viable approach would be to apply a reinforcement learning algorithm that learns value functions based on the rewards from the environment.  
Conceptualization, T.K. and K.R.R.; methodology, T.K. and K.R.R.; software, T.K.; validation, T.K. and K.R.R.; formal analysis, T.K and K.R.R.; investigation, T.K. and K.R.R.; resources, T.K.; data curation, T.K. and K.R.R.; writing--original draft preparation, T.K.; writing--review and editing, K.R.R.; visualization, T.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]).  
Not applicable.  
Figure 6: Situation space displayed on a two-dimensional plane formed by the two coordinates of the situation indicators \(s_{1}\) and \(s_{2}\).