# Deriving Situation-Adaptive Policy for Container Stacking in an Automated Container Terminal
## 3 Related Works
The rules adopt fuzzy logic to represent their conditions to deal with a high degree of uncertainty in the arrival of containers at the yard. Reference [23] proposed a multi-agent system for container stacking, in which the stack agent recommends a stacking position by consulting the knowledge base composed of if-then rules that check various conditions such as the container types, the configuration of the storage space, and the occurrence of exceptional events. Each stacking decision is evaluated by the evaluation agent that rejects the decision when unacceptable. If a decision turns out to be unacceptable, a learning mechanism is activated to add a new rule to the knowledge base so that the rules responsible for the wrong decision can be disabled. This learning mechanism makes the proposed system adaptive to changes, while the adaptation is mainly focused on the disturbances and unexpected events. Reference [24] investigated the impact of container stacking methods regarding how they deal with uncertainties in container terminals and reduce container handling costs. The stacking methods studied, however, determine only the best yard-bays but not the specific stacking slots. Compared to the works discussed above, References [4; 6] are much more closely related to our work. The stacking policy in these researches employs scoring functions that evaluate a candidate stacking location from various perspectives using different criteria, where the score of a location is calculated as the weighted sum of the scores for those criteria. The policy uses different scoring functions for different container types because each container type requires its own evaluation criteria for stacking. However, all those scoring functions with different weight vectors are together treated as a single policy. This policy is optimized by a search using a GA, where a candidate policy is evaluated by simulating the operations at a block under the policy for a certain period of time and measuring the resulting performance. For this simulation, Reference [4] provided a pool of operation scenarios of various kinds for a more accurate policy evaluation. To evaluate a policy, it is applied to a randomly selected subset of those scenarios, and the resulting performances were averaged. Therefore, the policy thus optimized can be said to be the best on average. Given a certain situation, there may be some other policy that works better than the on-average best policy. Another limitation with such policy is that the policy cannot change as the operational environment changes. As an effort to overcome this problem, Reference [5] proposed an online search algorithm that dynamically adjusts and optimizes a stacking policy by continuously generating variants of stacking policies and evaluating them while they are actually being applied for determining the stacking positions. However, this online search cannot keep up with the rapid changes in a situation. When the situation changes, the performance of the current policy begins to deteriorate, at which time that of a variant of the current policy may show a better performance. If this happens, the current policy would be switched to that variant, but only after experiencing some deterioration. In general, we cannot expect such a good variant to appear quickly at the right moment. Therefore, we can say the online search is not really reactive to changes, but it just gradually adapts to changes. Another drawback is that the online search cannot find really good policies because of its limited explorative capacity. Since all the variants must be simulated and tested online, it is hard to generate and test a number of variants under a real-time constraint, which deteriorates the search performance. The stacking policy derived by the method proposed in this paper is a significant improvement on the policy proposed in [4; 6]. While [4; 6] look for a policy whose average performance in various situations is the best, our method derives a policy that can quickly adapt to changing situations.