# Solving the Container Relocation Problem by Using a Metaheuristic Genetic Algorithm
## 6 Conclusions  
Today, more than 90% of global trade is carried out by sea, which is why seaports play a very important role in world transport. Container transport is one of the most important modes in maritime transportation. As container traffic increases every year, container transportation requires near perfect coordination between all stakeholders, working resources and processes within the entire container transportation system to minimize transit time, delivery time reliability and consequently cost as the main factor for efficient container transportation. This paper deals with the restricted CRP, which is one of the most important optimization problems in the stacking area of the port container terminal. There is an increasing number of containers that need be stacked in seaports before further shipping. In order to minimize the retrieval time of all containers, that need to be shipped from the stack in the specified order, the relocation of blocking containers must be done in a way that minimizes the number of additional movements (reshuffles) of these blocking containers that stay in the stacking area (it is not yet their turn to ship). Therefore, the crane operation schedule must be optimized to minimize the number of reshuffles, i.e., shorten the time to retrieve the blocking containers, as well as to maximize the utility of each crane operating in the stacking area. Since the restricted CRP optimization problem is NP-hard, it is difficult to find a good solution for retrieving all containers with a minimum number of unproductive reshuffles. In this paper, a new method based on genetic algorithm was proposed to resolve the restricted CRP at the stacking area of a port container terminal. The experiment of the proposed method was conducted on test cases with 20 various bay sizes (different numbers of tiers and stacks), each of which consists of 40 different test instances. These test instances are the most complex test instances that exist for evaluating models for solving restricted CRPs, since all instances of this test set contain the maximum possible number of containers (maximum occupancy) within the bay considering the bay size of particular CRP test case. The experimental results prove that the proposed method successfully determines the optimal relocation of blocking containers in order to minimize the total number of relocations within the bay. For all test cases, regardless of the size of the bay, the proposed method proved to be the best or second best solution compared to the well-known solutions of other authors in this field of research. Figure 15: An analysis of the average computational results (in seconds) of our new method for solving CRP of different bay sizes (\(3\times 3\)–\(6\times 7\)). The only limitation of this method is that it is not possible to verify the quality of the solutions obtained for large test sets (e.g., yard bay with a size of 6 tiers and 6 stacks). Nevertheless, we can assume that the method finds excellent solutions since it has been proven that it gives optimal results for small test sets. For large test sets, this method achieves the best or second-best results compared to other relevant models or methods that solve CRP. Considering the excellent results obtained by using a genetic algorithm as a major component of the proposed method, we assume that there is a great potential for using nature inspired metaheuristics to solve the restricted CRP. Therefore, in future work, we will focus on improving the method by using other nature-inspired algorithms such as Lion Optimization algorithm [46] or Red deer algorithm [47]. We will try to improve the proposed method so that it is capable of solving 3D CRPs. Conceptualization, M.G. and L.M. ([PERSON]); methodology, M.G. and L.M. ([PERSON]); software, M.G. ; validation, M.G., L.M. ([PERSON]), T.K. and L.M. ([PERSON]); formal analysis, M.G. and L.M. ([PERSON]); resources, M.G., [PERSON] ([PERSON]), [PERSON] and L.M. ([PERSON]); writing--original draft preparation, M.G., L.M. ([PERSON]), T.K. and L.M.