## 2 Literature Review  
Two fields have been extensively investigated in the existing literature to reduce relocations during the retrieval process of import containers. In the first field, the objective is to allocate stacking locations for new unloaded containers to reduce relocation moves in the future. Such as, the formulations for evaluating the expected number of rehandles were proposed in [7; 8] based on the segregation storage strategies. Other similar studies on generating container storage strategies can be found in [9; 10; 11]. The second field, the container relocation problem, focuses on the relocation movements during the retrieval process and aims to retrieve all containers from a bay based on the prescribed order while minimizing relocations. The CRP has received much attention from scholars for decades, and extensive studies from multiple perspectives have been conducted [12; 13]. In order to obtain the optimal solution of CRP, many papers have formulated integer programming (IP) to find the optimal solution [4; 14; 15; 16; 17; 18]. Because the number of variables and constraints in IP exponentially increases as the size of the problem grows, the IP model cannot be solved for large instances. Another method to obtain the optimal solution of CRP is to utilize the exact algorithms. Many exact algorithms are developed based on tree search procedures, such as tree search algorithms [19; 20], (iterative deepening) A\({}^{\star}\) algorithms [21; 22], B&B algorithms [18; 23; 24; 25; 26; 27; 28], and abstract approaches [6]. However, the CRP is an NP-hard problem [29]. For larger-scale instances, the exact algorithms or IP models are time-consuming and impractical for real-time operation demands. Therefore, researchers have tackled this challenge from two viewpoints. First, [PERSON] et al. [29] proposed an assumption that a container can only be relocated if it is blocking the target container. This assumption reduces the number of movable containers during pickup operations, thereby reducing the size of the CRP problem. The CRP under this assumption is referred to as restricted CRP. The restricted CRP has become an important branch of the CRP research field [17; 18; 20; 27; 29]. Compared to unrestricted CRP, restricted CRP does save a small amount of computational time, but at the cost of losing the quality of the solution. It is worth noting that the restricted CRP is still an NP-hard problem. Another approach is to utilize the heuristics to solve the CRP model. Researchers have been increasingly exploring heuristic algorithms to overcome the computational complexity of CRP. To date, many outstanding heuristic algorithms have been developed, such as beam search algorithms [25; 26; 30; 31], greedy heuristics [14; 32; 33; 34], and other heuristics based on relocation rules [5; 6; 35; 36]. Due to the difficulty of obtaining the retrieval time information in advance, many models of CRP were constructed based on assuming that the retrieval processes of import containers conform to some probability distribution [37; 38]. Moreover, [PERSON] et al. [35] proposed the online CRP models where the retrieval orders of the containers are revealed progressively with the arrival of ETs. Similar studies can be found in [6; 39]. Truck appointment systems (TAS) can help terminals address this issue. The TAS has been deployed in most container terminals, which allows terminal operators to obtain the containers' retrieval time window. Based on the appointment information, [PERSON] et al. [5], [PERSON] and [PERSON] [6] introduced a batch model in which the containers within the same appointment time window are considered a batch. Based on the batch model, [PERSON] et al. [5] proposed the stochastic container relocation problem (SCRP) where the retrieval order between different batches is clear but uncertain within the same batch. It means the containers within the same batch have the same retrieval probability. Indeed, the retrieval sequence of the same batch containers is indistinguishable until associated ETs arrive.