## 2 Literature Overview  
The allocation problem in the context of container space planning is defined as the arrangement of storage locations and determining the sequence of container storage operations. It is one of the most important operations at a container terminal. The specificity of container storage is different from a typical pallet warehouse. We cannot obtain a lower tier container without translocating the higher tier containers. Optimizing the trafficking of containers becomes very important as each additional adjustment is redundant, generates costs and takes time. The challenge of optimal storage is known as the \"location mapping (stacking) problem\", as it involves stacking containers. Industry feedback suggests that shuffling containers continues to be a major operational challenge in yard management. If the space allocation is poorly designed, the yard crane must move all the containers on top of the destination container to other stacks before it can retrieve the target container, and these additional moves will significantly reduce the yard crane's throughput. Containers have different attributes such as weight, size, brand, destination and special requirements (e.g., cold stores, dangerous goods, oversized cargo, tight joints or a specific handling sequence). Containers with similar attributes are classified as a single container class [1; 2; 3]. To reduce reshuffling, the \"homogeneity rule\", which strictly arranges containers of the same class for easy retrieval, has been widely implemented in terminal operations. From a space allocation perspective, the consignment strategy, in which containers belonging to the same destination vessel are placed close together, has been adopted to support yard planning in terminals [4]. Although the above principles are able to reduce reshuffling to some extent, the operator of one of the largest container ports in the world still suggests including the impact of reshuffling on space allocation. An interesting approach using metaheuristic methods is presented in [5]. The optimization objective was to reduce the costs associated with container allocation during unloading from railcars at a land terminal, which can be achieved by applying the presented heuristics supported by generic algorithms. An interesting approach related to the mathematical modeling of the problem specific to the allocation of containers is presented in [6; 7; 8; 9]. Unproductive movements associated with container allocation have a large impact on the operating costs of a container terminal. These are mainly caused by the fuel consumption and the time wasted on unproductive operations. Due to the fact that each train brings dozens of containers and there are usually several hundred terminal slots, the optimization has the character of an NP-hard problem. Therefore, it cannot be managed without introducing heuristic and simulation solutions aiming at the best solution, which is not optimal, but can be called rational. Heuristic models in yard management can be found, for example, in [10]. Some articles propose the use of several heuristics to search for the most advantageous solution [11]. The problem of allocation and reshuffling is prevalent for marine terminals, especially in combination with the proper distribution of cargo on the ship. An overview of short-term reshuffling in port terminals was described by [12]. The problem of scheduling crane operation between container trains was addressed as a heuristic solution in [13]. In the same article, the operation of the crane in the transshipment of the railcar-yard relationship was described and consisted of the division of space into sections. In [14], a description of the operation and management of land container terminals is included. Other types of approaches to the aforementioned topic were proposed in article [15], where a graph model for container routing was presented, while a tabu search algorithm was presented in [16].