## 5 Numerical Examples
### Analysis of Results and Discussion
#### 5.4.2 Discussion  
1. To our best knowledge, this is the first research employing SFLA for dealing with the BAP in a seaport container terminal. The SFLA also shows potential in dealing with problems in the yard and landslide areas, in addition to the seaside area.
2. Some early CBAP studies, such as those by [PERSON] [4], [PERSON] et al. [28], and [PERSON] et al. [29], assumed static berth allocation and independent handling times of ships in their berthing positions. In such studies, the problems to be solved are equivalent to the CSP with fixed orientation of placed items. In contrast, this present research considers the dynamic nature of ship arrivals and assumes that the handling times of ships are dependent on their berthing positions.
3. This present research also differs from the studies of [PERSON] and [PERSON] [30; 31] and [PERSON] and [PERSON] [5], as those studies assumed fixed handling times for ships. In addition, in a strict sense, the BAP solved in the study of [PERSON] and [PERSON] [31] was downgraded to a static version of the problem due to ships being able to be served before their ETAs, albeit with some penalty cost imposed in the objective function [1].
4. The CBAP solved in this study is quite different from the conventional CSP due to the following three differences: first, in the CBAP a variable handling time is considered; second, the CBAP considers the dynamic nature of ship arrivals; third, the CBAP considers the orientation of the ship placed in a berth plan.
5. Ref. [1] also proposed a two-stage approach for dealing with the CBAP. The first stage generates an initial solution for the DBAP, and then this solution is further transformed into a feasible one by repositioning overlapped and sparsely located ships at the second stage. However, this approach appears to be rigorous, because to find the final solution to the CBAP, it is first necessary to find the solution to the DBAP. In addition, in the first stage, it is necessary to set a minimumand maximum berth length for the DBAP, and to prepare several intermediate berth lengths for this approach. Such calculations for the setting of parameters take a long time. In contrast, our approach appears to be simpler, as it finds the solution to the CBAP directly.
6. In [1], the proposed heuristic arranges ships in ascending order of their handling time, which is similar to the FCFS rule, before resolving overlaps of ships. This differs from our approach, which creates alternative placement sequences of ships by using the ISFLA so as to explore more alternative solutions.
7. Based on a simulated annealing approach, the metaheuristic proposed in [34] is capable of exploring alternative solutions by means of an \(R\) parameter that denotes the number of sequences of the neighborhood search. However, the sequence approach proposed in [34] is different from the ISFLA proposed in this research. In addition, [34] did not describe the resolution of overlapping ships.
8. Compared with [1,34], our approach appears to be simpler and more practically applicable. However, it is too early to say that our approach is better than the approaches proposed in [1,34], as more experiments are required for comparison. Nevertheless, according to the objective defined and used in this research, we know that the optimal berthing position and time for a ship in a berth plan are the \(d_{j}\) and \(ETA_{j}\) of the ship. As the ISFLA will assign a ship to or around the optimal berthing position (\(d_{j}\)) and start working time (\(ETA_{j}\)) for each ship, we can conclude that the resulting berth plan will be the optimal solution (when \(Z\geq 0\)), or a near-optimal solution (when \(Z>0\)). As the average computational times for our approach when solving a problem with 60 ships was about 1.4 h; this shows the applicability of the proposed method in practice.
9. This ISFLA is able to improve the simplicity of simple heuristics while avoiding the computational intractability of exact approaches by tuning the total number of iterative runs.