Maritime transport is the backbone of international trade of goods. Therefore, seaports are of great importance for maritime transport. Container transport plays an important role in maritime transport and is increasing year by year. Containers transported to a container terminal are stored in container yards side by side and on top of each other, forming blocks. If a container that is not on top of the block has to be retrieved, the containers that are above the required container must be relocated before the required container is retrieved. These additional container relocations, which block the retrieval of the required container, slow down the entire retrieval process. The container relocation problem, also known as the block relocation problem, is an optimization problem that involves finding an optimal sequence of operations for retrieving blocks (containers) from a container yard in a given order, minimizing additional relocations of blocking containers. In this paper, the focus is on the two-dimensional, static, offline and the restricted container relocation problem of real-size yard container bays. A new method for resolving the container relocation problem that uses a genetic algorithm is proposed to minimize the number of relocations within the bay. The method is evaluated on well-known test instances, and the obtained results are compared with the results of various relevant models for resolving the container relocation problem. The results show that the proposed method achieves the best or the second-best result for each test instance within the test set.

The paper examines the sampling effectiveness of seabin devices and the composition of floating marine litter in port areas. Sampling was carried out from May to September 2021 in Port of Cristo and Port of Colonia de Sant Jordi on Mallorca Island, Spain. This is the first study of the composition of floating marine litter in the ports of Mallorca collected by seabin devices. During the study, 15,899 items and 336 kg of litter were collected and analyzed. The results indicate that seabin effectively collects floating litter from sea surfaces different in size (2 mm to 40 cm). Microplastics (60.8%) were the most commonly found litter, followed by soft plastic items > 5 mm (11.6%) and unidentified hard plastic items > 5 mm (9.6%). Significantly more marine litter was collected in the Port of Cristo (78.6%), compared to the collection of one device in the Port of Colonia de Sant Jordi (21.4%). Time series analysis showed that the average seasonal component was highest in May (68% above baseline). The linear time trend with an R2 of 52.25% indicated the acceptable significance of the model.

The new technology greatly affects the way of production, consumption, communication, service delivery and ultimately on the entire supply chain. All stakeholders in the business process must invest in new knowledge and develop new business models to adapt to the changing business environment. Connecting devices over internet (Internet of things) and stakeholders’ synergy open up opportunities for new market achievements as well as for the improvement of business processes both in the supply chain and in ports. The development of information technologies has an impact on the reduction of errors, costs, time of information transfer and transport, inventory reduction and thus on better customization. There should be no weak links in the supply chain, which is especially related to the port and port processes that are the basis of the supply chain network. The port is the core of all activities of the supply chain and is also a place where supply and demand meet.

In this paper, a hybrid nature-inspired metaheuristic algorithm based on the Genetic Algorithm and the African Buffalo Optimization is proposed. The hybrid approach adaptively switches between the Genetic Algorithm and the African Buffalo Optimization during the optimization process, leveraging their respective strengths to improve performance. To improve randomness, the hybrid approach uses two high-quality pseudorandom number generators—the 64-bit and 32-bit versions of the SIMD-Oriented Fast Mersenne Twister. The effectiveness of the hybrid algorithm is evaluated on the NP-hard Container Relocation Problem, focusing on a test set of restricted Container Relocation Problems with higher complexity. The results show that the hybrid algorithm outperforms the individual Genetic Algorithm and the African Buffalo Optimization, which use standard pseudorandom number generators. The adaptive switch method allows the algorithm to adapt to different optimization problems and mitigate problems such as premature convergence and local optima. Moreover, the importance of pseudorandom number generator selection in metaheuristic algorithms is highlighted, as it directly affects the optimization results. The use of powerful pseudorandom number generators reduces the probability of premature convergence and local optima, leading to better optimization results. Overall, the research demonstrates the potential of hybrid metaheuristic approaches for solving complex optimization problems, which makes them relevant for scientific research and practical applications.

Today, there is a rapid increase of the available data because of advances in information and communications technology. Therefore, many mutually heterogeneous data sources that describe the same domain of interest exist. To facilitate the integration of these heterogeneous data sources, an ontology can be used as it enriches the knowledge of a data source by giving a detailed description of entities and their mutual relations within the domain of interest. Ontology matching is a key issue in integrating heterogeneous data sources described by ontologies as it eases the management of data coming from various sources. The ontology matching system consists of several basic matchers. To determine high-quality correspondences between entities of compared ontologies, the matching results of these basic matchers should be aggregated by an aggregation method. In this paper, a new weighted aggregation method for parallel composition of basic matchers based on genetic algorithm is presented. The evaluation has confirmed a high quality of the new aggregation method as this method has improved the process of matching two ontologies by obtaining higher confidence values of correctly found correspondences and thus increasing the quality of matching results.