## Literature Review
### Studies on CBAP  
Some studies, such as those by [PERSON] and [PERSON] [21], [PERSON] et al. [22; 23], [PERSON] et al. [24; 25; 26], and [PERSON] et al. [27], have focused on the DBAP, while [PERSON] [4], [PERSON] et al. [28], [PERSON] et al. [29], [PERSON] and [PERSON] [30; 31], and [PERSON] and [PERSON] [5], [PERSON] et al. [1], [PERSON] and [PERSON] [32], [PERSON] and [PERSON] [33], [PERSON] et al. [34] carried out studies focused on the CBAP. Due to the focus on quays with continuous configuration, we detail these CBAP studies below. The study by [PERSON] [4] is an early study focusing on the CBAP. In that study, the CBAP was considered as a two-dimensional packing problem, but transformed into a graph theoretical representation once the authors had captured the CBAP characteristics. Then, the author proposed a heuristic for solving this problem with the objective of minimizing the quay length required to accommodate all incoming ships under the assumption that these ships would not further change their berthing positions. On the other hand, [PERSON] et al. [28] solved the CBAP both with and without this restriction in ships' movements. Their objective was to minimize the makespan of the schedule. [PERSON] et al. [29] developed a heuristic for solving the CBAP. Their objective was to find the solution with the minimum total weighted completion time of ships. [PERSON] and [PERSON] [30] proposed a subgradient optimization method for dealing with the CBAP with the objective of minimizing costs arising from the delayed departures of ships due to undesirable service order and the additional complexity of handling containers when ships are served at non-optimal mooring locations in the port. This study appears to be more practical than the above-mentioned studies, in that it took the actual berthing positions of ships into consideration. [PERSON] and [PERSON] [5] addressed the same CBAP discussed in [PERSON] and [PERSON] [30]. The authors proposed a simulated annealing method to deal with this problem, finally concluding that this approach had the capacity to find an optimal solution. However, the CBAP studies mentioned above neglected the impact of berthing position on the handling time of a ship. In a later study, [PERSON] and [PERSON] [31] studied a CBAP with a similar objective function to those used in [PERSON] and [PERSON] [30] and [PERSON] and [PERSON] [5]. A major difference of the objective function used in [31] was that it accounted for additional costs resulting from the early or late start of ship handling with respect to the ETA of a ship. Their approach generated the optimal starting times of ship services and berthing positions, as well as QC assignments to ships. However, in that study, the handling times of ships were still independent from the ships' mooring positions. Having completed the DBAP [24; 25; 26], [PERSON] et al. [1] moved on to deal with the  
Figure 2: (a) Intra-terminal transshipment; (b) Inter-modal transportation. CBAP, which was treated as a two-dimensional cutting stock problem. The CBAP was solved by using a two-stage heuristic. The first stage generates an initial solution to the DBAP, and then this solution is further transformed into a feasible one by repositioning overlapped ships as well as sparsely located ships at the second stage. In that study, the impacts of the mooring positioning of a ship on its handling time were taken into consideration. [PERSON] and [PERSON] [32] solved the CBAP by proposing a stochastic beam search. Their experiments showed that the proposed approach was able to find a near-optimal solution, with the solution being better than those found by state-of-art metaheuristics and traditional deterministic beam search in terms of accuracy and efficiency. [PERSON] and [PERSON] [33] proposed a neighborhood-search optimization heuristic to solve the CBAP. In that study, factors including the First Come First Served (FCFS) rule, clearance distance between ships, and possible ship shifting were taken into consideration.