## 2 Literature Review
### Studies Focusing on Simultaneous Problems  
Some studies have been dedicated to solving the three seaside operational problems simultaneously. Treating the arrival times and handling times of ships as stochastic variables, [PERSON] and [PERSON] [PERSON] and [PERSON] (2018) proposed a model to deal with the berth and QC allocation problems simultaneously, with the aim of minimizing the average waiting time for calling ships. However, because of the polynomial computational time of computer they proposed a GA approach to the solution from a reduced solution space. [PERSON] et al. [PERSON] et al. (2018) used a dynamic allocation model to deal with the BAP and QCAP simultaneously. In that study, the authors employed a hybrid parallel genetic algorithm (HPGA), combining a parallel genetic algorithm (PGA) with a heuristic to find solutions that were being further evaluated by the simulation approach. [PERSON] et al. [PERSON] et al. (2018) studied the berths and QCs allocation problem, with the special factor of the QC coverage range limitation taken into account. A sub-gradient optimization algorithm was developed solve the BAP and QCAP simultaneously. However, that algorithm only allowed limited QC adjustments during the loading and unloading of containers. [PERSON] et al. [PERSON] et al. (2018) formulated a Mixed Integer Linear Programming (MILP) model for the BAP and QCAP, taking into account the ship priorities, preferred berthing locations, and handling time. Finally, they concluded that this model was able to support operational and tactical decision-making. [PERSON] et al. [PERSON] et al. (2018) had a study on the BAP and dynamic QCAP. A multi-objective hybrid GA was proposed in that study to assign QCs dynamically. Some studies have focused on the BAP and QCSP simultaneously. Having formulated the BAP and QCSP as a MIP model, [PERSON] et al. [PERSON] et al. (2018) proposed a GA-based heuristic to deal with the two problems due to NP-hard. The GA-based heuristic was able to find approximate solutions and their experimental results concluded the applicability of that heuristic. [PERSON] et al. [PERSON] et al. (2018) solved the BAP together with the QCSP with the aim of minimizing the total handling time, waiting time, and the delay for every ship. After formulating the two problems as mathematical models, a GA was later proposed to find approximate solutions. Similarly, [PERSON] and Wang [PERSON] and [PERSON] (2018) formulated the BAP and QCSP as a MIP model and solved them with a GA. Their experimental results showed that the GA was effective and efficient in finding near-optimal solutions. [PERSON] et al. [PERSON] et al. (2018) dealt with the BAP and QCSP. In that study, the arrival and handling times of vessels were treated as uncertainties and the quay was configured as discrete berths. Ships were assumed to arrive stochastically and QCs were allowed to move among the berths. Having formulated a MIP model, the authors proposed a simulation-based GA approach to generate schedules for the berths and QCs. They found that the obtained results were desirable. To deal with the BAP and QCSP, [PERSON] et al. [PERSON] et al. (2018) provided a Bi-Level Programming (BLP) to deal with the two problems. At the upper level a GA was used to deal with the BAP and at the lower level a Branch and Bound (B & B) approach was proposed to deal with the QCSP. [PERSON] (1988) used a simulation model to study an automated container terminal with multiple discrete berths. In addition to the aforementioned approaches, some other approaches have been proposed for dealing with CT seaside operational problems. For example, [PERSON] et al. [PERSON] et al. (2018) proposed an agent-based approach for dynamic port planning. [PERSON] et al. [PERSON] et al. (2018) used a Decision Support System (DSS) to help make decisions for yard operations in a CT.