# Solving the Container Relocation Problem by Using a Metaheuristic Genetic Algorithm
## 3 Literature Review
As stated in the Introduction section, for solving restricted CRP, the use of the genetic algorithm [10] is proposed in [17; 18], which determines the order of execution of one of the four relocation rules (based on the stacks occupancy) for blocked containers on the terminal stacking area. These rules prefer to make the reshuffling of blocking containers to the nearest possible stack (column), which satisfies the restrictions set within a particular relocation rule. In this way, a minimum number of relocations is not obtained for each CRP. Thus, in this paper, we propose a new method based on the genetic algorithm in order to minimize the number of relocations within the bay for each CRP test problem. In addition of using a genetic algorithm, some other nature-inspired metaheuristic algorithms have been used in solving CRPs. Simulation annealing [23] is used for restricted CRP solving in [21]. In [20], the Ant Colony algorithm [22] was used for restricted CRP resolving. In [17], a method that implements a genetic algorithm is proposed for solving restricted CRP with containers that are grouped by the retrieval time window. This problem is much easier to solve than classic restricted CRP because more containers are retrieved in a common time window. Containers within the same window do not always have to be retrieved in the same order, but in such a way that the number of additional movements is minimal. Although this method is used for the easier (simple) restricted CRP, it is interesting insofar as the only solution, along with [17; 18; 20; 21], that uses metaheuristic algorithms inspired by nature for resolving restricted CRP. In continuation, the focus will be on related papers dealing with the solution of restricted CRP of the currently most complex test set in the literature, which was presented in [24]. As stated before, this test set consists of different restricted CRPs in which the bays are maximally occupied with containers. For the bay, consisting of W stacks and H tiers (maximum stacking height), the maximum number of N containers that such a bay can store is N = W \(\times\) H - (H \(-\) 1). We assumed that the models proposed for finding the solution of this test set are more referenced for being presented here. For this testing data set, the best results were achieved by the methods proposed in [17; 24; 33; 34]. As stated before, in [17], the genetic algorithm is implemented within the proposed solution so that it finds the best order to execute the container relocating rules by minimizing the number of container reshuffling. The method in [17] is the only method that uses nature inspired metaheuristic algorithm for resolving the most complex test set presented in [24]. Considering the very good results obtained when solving the CRP and possible improvements in the method, we assume that there is a great room for improvement in restricted CRP solving with the usage of nature-inspired metaheuristic algorithms. Furthermore, there is a large number of newly proposed methods for solving CRP, and some of them are described in [39; 40; 41; 42; 43]. However, the authors do not test out the methods on this highly complex test set [24]. Therefore, we did not compare our new method with these methods because we believe that the test set from [24] is the most relevant test set for comparing methods for solving CRP. As mentioned earlier, the test set from [24] consists of several restricted CRPs where the bays are maximally occupied by containers.