The objective of this research is to analyse smart technologies implemented in Croatian marinas and their impact upon the safety and service quality improvement, sustainability, and environmental protection, as well as energy consumption and operations optimisation. Key performance indicators and a definition of smart marina concept have been derived based on the smart port concept. The analysis has been conducted on a sample of 78 marinas in six different counties along the Croatian coast. Ultimately, the SWOT analysis has been performed in order to determine the advantages and disadvantages of introducing smart technologies in marina management. The results indicate that the Croatian marinas are undergoing a revolution in terms of facilitating booking management process and achieving greater safety and service quality, but still need to improve in the field of monitoring and controlling nautical tourism impact upon the environment.
The optimization of seaside operations at container terminals includes solving standard berth and crane allocation problems. The question arises about the efficiency of such optimizations in small and medium-sized container terminals, with different quay designs or different terminal layouts. This paper focuses on developing an integrated model that would apply to medium-sized terminals with a multi-quay layout. The main objectives are determining the shortest possible vessel stay at the port and providing a high-reliability service to ship operators. The developed integrated model includes the optimization process in three stages: initiation, assignment, and adjustment. The model’s main feature is generating operational scenarios based on the cargo distribution onboard and integrated berth and crane allocation. The aim is to choose the most favorable option to optimize ships’ overall processing time in the planning horizon. The experiment was conducted to test the model’s functionality and justify the results by comparing the results obtained by the integrated model with the classical approach of berth and crane allocation in a multi-quay environment. The results show significant improvements in peak periods when ships’ arrivals are concentrated in smaller time intervals by applying the integrated model.
The growth of container transport places increasing demand on traffic, especially in situations where container terminals are located near the city centers. The main problem is traffic congestion on networks caused by the integration of Heavy-Duty Vehicles and urban traffic flows. The main objective is to identify the critical traffic parameters which cause negative organizational and environmental impacts on the existing and future traffic demand. A micro-level traffic simulation model was implemented for the testing of the proposed framework-based supply, demand, and control layers. The model was generated and calibrated based on the example of a mid-size Container Terminal “Brajdica” and the City of Rijeka, Croatia. The results indicate that the critical parameters are Queue Length on the approach road to the Container Terminal and the Stop Delay on the main city corridor. High values of these parameters cause negative effects on the environment because of increased fuel consumption and the generation of extra pollution. Due to this problem, a sensitivity analysis of the traffic system performance has been conducted, with a decrement of Terminal Gate Time distribution by 10%. After re-running simulations, the results indicate the impact of subsequent variation in Terminal Gate Time on the decrease of critical parameters, fuel consumption, and vehicle pollution.
The tasks of an officer of the watch (OOW) on complex ships during navigation in coastal areas may be very challenging. Almost all the tasks require substantial information processing and timely decision making. Every distracting element should be avoided during navigation. Every call, made through any communication system, activates a new process that the OOW needs to conduct. The new process may interrupt a previously started task. In case of too many incoming calls, the workload of the OOW may increase significantly, thus, their situational awareness may be compromised and the risk of errors may increase. The objective of this research was to analyze the impact of incoming voice calls on the OOW. The research methods used include a questionnaire survey and a series of interviews with experienced officers. The main outcomes refer to the average frequency of incoming calls, duration of conversations and subjective assessment of their influence. The results indicate that, during one watch, an officer needs to answer 14 calls that last 16.19 min altogether. However, the officers consider 45% of calls made during watch keeping as distracting. A possible call management system with the aim to reduce distractions made by low priority calls is proposed in this paper.
The growing demand for private and public transport services in urban areas requires sophisticated approaches to achieve satisfactory mobility standards in urban areas. Some of the main problems in urban areas today are road congestions and consequently vehicle emissions. The aim of this paper is to propose a methodological approach for the estimation of vehicle emissions. The proposed methodology is based on two interrelated models. The first model is a microscopic simulation SUMO model which can be used to identify the most congested urban areas and roads with critical values of traffic parameters. The second model is the COPERT Street Level for estimating vehicle emissions. The proposed models were tested on the urban area of Rijeka. The results of the microscopic SUMO simulation model indicate six urban roads with the critical traffic flow parameters. On the basis of the six identified urban roads, an estimation of vehicle emissions was carried out for specific time periods: 2017, 2020, 2025, and 2030. According to the results of the second model, the urban road R20-21 was identified as the most polluted road in the urban district of Rijeka. The results indicate that over the period 2017–2030, CO emissions will be reduced on average by 57% on all observed urban roads, CO2 emissions by 20%, and PM emissions by 58%, while the largest reduction of 65% will be in NOx emissions.
Nowadays, maritime transport is the backbone of the international trade of goods. Therefore, seaports play a very important role in global transport. The use of containers is significantly represented in the maritime transport. Considering the increased number of container shipments in the global transport, seaport container terminals have to be adapted to a new situation and provide the best possible service of container transfer by reducing the transfer cost and the container transit time. Therefore, there is a need for optimization of the whole container transport process within the terminal. The logistic problems of the container terminals have become very complex and logistics experts cannot manually adjust the operations of terminal processes that will optimize the usage of resources. Hence, to achieve further improvements of terminal logistics, there is a need to introduce scientific methods such as metaheuristics that will enable better and optimized use of the terminal resources in an automated way. There is a large number of research papers that have successfully proposed the solutions of optimizing the container logistic problems with well-known metaheuristics inspired by the nature. However, there is a continuous emergence of new nature inspired metaheuristics today, like artificial bee colony algorithm, firefly algorithm and bat algorithm, that outperform the well-known metaheuristics considering the most popular optimization problems like travel salesman problem. Considering these results of comparing algorithms, we assume that better results of optimization of container terminal logistic problems can be achieved by introducing these new nature inspired metaheuristics. In this paper we have described and classified the main subsystems of the container terminal and its logistic problems that need to be optimized. We have also presented a review of new nature inspired metaheuristics (bee, firefly and bat algorithm) that could be used in the optimization of these problems within the terminal.
The relocation of containers is a crucial operation in container ports all around the world. The Container Relocation Problem (CRP) is focused upon to find a sequence of container retrievals in a defined order from a single yard container bay with a minimum number of relocations. The goal of this paper is to find out if Genetic Algorithm (GA) can give new insights in the problem of solving the CRP. In this paper we focus on the two-dimensional, static, offline and restricted CRP of real-world yard container bays. Four rules are proposed for determining the position of relocated containers. We applied GA to find the best sequence of container retrievals according to these four rules in order to minimize the number of relocations within the bay. The experimental testing was run on a total of 800 different instances with varying bay sizes and number of containers. The given results are compared with the results of different authors using other heuristic methods. The results show that the proposed model solves CRP and achieves near optimal solutions.
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.
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