Efficiency Of Port Operations Research Articles

How AI can influence efficiency of port operation specifically ship arrival process: developing a cost–benefit framework

How AI can influence efficiency of port operation specifically ship arrival process: developing a cost–benefit framework

The impact of AI technology adoption on operational decision-making in competitive heterogeneous ports☆

Artificial Intelligence (AI) technology is changing the industrial paradigm and has been widely adopted in port operations. Although AI technology can improve the efficiency of port operations and service quality, ports need to bear some costs. Discussing the role of applying AI technology to ports in complex competitive environments has become an important issue in the operations of ports and shipping. In this study, we construct a game-theoretic model of competitive heterogeneous ports. The research conclusions indicate that the adoption of AI technology by heterogeneous ports can enhance port profits. Unfortunately, simultaneous adoption exacerbates homogenized competition, posing a threat to profit realization. Furthermore, while the hub port can leverage AI-empowered capabilities to strengthen own competitiveness, it can undermine the performance of competitors and society at large. Surprisingly, the adoption of AI technology by feeder port is more advantageous in achieving social welfare and achieving multiple benefits such as carbon reduction.

AHP기법을 활용한 국내 중소항만의 경쟁력 제고 방안에 관한 연구

Purpose: This study derives the factors necessary for the Pohang Yeongilman Port to secure competitiveness as a small or medium-sized port.
 Research design, data, and methodology: By conducting AHP and IPA, the model from this study sets the top factors in four areas: specialized port development, port operation efficiency, port hinterland development and strengthening of connections, and activation of marine tourism and water-friendly functions.
 Results: Among the top factors, specialized port development was identified as the most important, and results were derived in the following order: development and strengthening of the port hinterland, port operational efficiency, and activation of marine tourism and waterfriendly functions.
 Conclusions: All Pohang Yeongilman Port stakeholders recognize the important factors for port development, but perceive low satisfaction with performance.

Traffic Density Prediction Model At Loading Terminal Using Discrete Simulation Method Quantitative Approach

Traffic density at the port loading terminal is a critical factor affecting the port's operational efficiency and overall performance. To overcome the challenges of the complexity of the traffic system at the port terminal, a discrete simulation approach is adopted in this study. A quantitative approach is used to collect data and accurately model traffic processes. This study aims to develop a predictive model of traffic density at Tanjung Emas port loading terminals using a discrete simulation methods quantitative approach. The results showed that the resulting traffic density prediction model has high accuracy and can be used as a predictive tool to assist decision-making in optimizing port loading terminal operations

Development of a “Smart Dry Port” Indicator and Ranking Calculation for Spanish Dry Ports

The objectives set in the 2030 agenda gravitate towards the concept of sustainability, a concept that is closely related to the efficiency of port operations. As a direct derivative of the search for more efficient logistics and port operations, a disruption of new technologies, associated with the term ‘Smart’, is making its way with a large range of new digital applications. The logistics and port sector is also undergoing a process of transition towards this kind of operational intelligence through digitized and increasingly automated devices. In this work, an analysis of the most relevant Spanish dry ports is carried out, with the clear objective of discovering their degree of adaptation to the ideal Smart concept. This was developed through the implementation of a descriptive statistical analysis and a Delphi methodology, a battery of indicators and measurement variables that classify, in an integral and objective way, the degree of the adaptation of these ports to the concept of the ideal Smart Dry Port; in this way, we obtained a ranking of the ports. The current and future trend of this type of Smart facilities is solid and clearly points towards a greater digitalization of port processes and an openness of information, with port facilities interconnected with each other through automated processes. The analysis gives a clear picture of the adaptation to digitalization and automation of three of the largest Spanish dry ports, in which there are ongoing efforts to adapt to this concept but for which there is still a long way to go.

The Estimation of the Correlation between GHG and the Technical Efficiency of Korean Short-Sea Ports

Short-sea ports in Korea are classified as national or local according to their management and operation entities. Korea has 29 ports, of which 11 are nationally managed and 18 are locally managed. Meanwhile, according to Korea’s Harbor Act, short-sea ports are designed to support the promotion of benefits such as handling cargo required for local industries, transportation of passengers, and revitalization of tourism. However, even though they are designated as short-sea ports, there are cases in which the initial designation purpose was not achieved because of the minor traffic volume and number of passengers. Consequently, this study evaluated the operational efficiency of 29 Korean short-sea ports employing the data envelopment analysis (DEA) and presented the operational efficiency of 29 short-sea ports. Moreover, the study measured the correlation between greenhouse gas (GHG) emissions and the port operation efficiency of 29 Korean short-sea ports. The analysis results of this study are as follows: First, the correlation between port-operation efficiency and sulfur dioxide was −0.41707. Second, the correlation between efficiency and carbon monoxide was −0.39952. Third, the correlation between efficiency and nitrogen dioxide was −0.30888. In summary, this study concludes that the higher the port-operational efficiency, the lower the GHG emissions. Improving the operational efficiency of ports can reduce GHG emissions, which can positively (+) affect port sustainability.

Port congestion under the COVID-19 pandemic: The simulation-based countermeasures

As the hub connecting global production and consumption, ports play a pivotal role in the global maritime supply chain system. The outbreak of the COVID-19 pandemic and its rapid global spread have caused significant port congestion, resulting in skyrocketing freight rates and posing a serious threat to the smooth operation of international trade. Based on the impact of the COVID-19 pandemic, we have developed a novel container port congestion evaluation model by integrating the classical SEIR model, using the system dynamics (SD) theory. In this study, we simulate the impact of different port congestion countermeasures using data from Ningbo Zhoushan Port. The results demonstrate that implementing smart regulations at a high level of epidemic prevention and control is the most efficient approach to swiftly eliminate port congestion. In addition, it is worth mentioning that in the scenarios with a low level of epidemic prevention and control, increasing port investment to optimize smart ports can yield even better effects. Furthermore, we have discussed the impact of factors such as the ratio of infected people, the number of contacted people, and epidemic prevention and control capacity on port congestion index. Hence, we propose that the implementation of advanced smart regulation systems can enhance port operational efficiency and effectively alleviate port congestion. Additionally, amidst the normalization of the COVID-19 pandemic, which has yet to disappear, it is crucial to maintain a vigilant approach to prevention and control to ensure the seamless operation of the port industry.

A Study of Regional Variability in Port Transport Efficiency in China - Based on A Three-Stage DEA-Malmquist Index Model

To study the impact of COVID-19 on China's port logistics efficiency, this paper uses a three-stage DEA and Malmquist model to measure the efficiency of China's port logistics industry in 38 coastal and inland river cities under environmental constraints from 2016-2021, and analyses its spatial and temporal characteristics and evolutionary dynamics. The study shows that the comprehensive technical efficiency of port logistics in coastal cities is higher than that of inland river ports. The progress in port logistics efficiency stems from the increase in scale efficiency, and the dependence of port logistics on scale expansion is high. In addition, COVID-19 has had some hit on port operational efficiency, where the reduction in total factor productivity is mainly caused by a lower technological progress index. Compared to coastal ports, inland ports are more affected by COVID-19. Meanwhile, the impact of COVID-19 on the productivity of Chinese ports is characterised by a clear short-term pattern. The paper then makes recommendations for further improving the efficiency of port logistics and the ability to cope with major contingencies in China and globally.

Joint Optimization of Pre-Marshalling and Yard Cranes Deployment in the Export Block

To improve the efficiency of loading operation by researching the optimization of the pre-marshalling operation scheme in the export container block between the time when the ship stowage chart was published and the beginning time of loading, a two-stage mixed integer programming model was established. The first stage established an optimization model of the container reshuffling location, based on the objective function of the least time-consuming operation of a single-bay-yard crane, and designed an improved artificial bee colony algorithm to solve it. Based on the first stage, an optimization model of yard crane configuration and scheduling was built to minimize the maximum completion time of the yard crane in the export block, and an improved genetic algorithm was designed to solve the built model. Through comparative analysis, the performance of our algorithm was better than CPLEX and traditional heuristic algorithms. It could still solve the 30 bays quickly, and the solving quality was 8.53% and 11.95% higher than GA and TS on average, which verified the effectiveness of the model and the science of the algorithm and could provide a reference for improving the efficiency of port operation.

A New Mathematical Model for Container Stacking in Seaport Terminals: Case Study

Maritime transport is the operation of moving goods from one place to another by sea. It takes into account the loading and unloading of containers in ports. Once the vessels arrived at the quayside, they are unloaded by means of quay cranes and then transferred to the storage or transshipment areas. The Container Stacking Problem (CSP) is one of the major problems in seaport terminals, in particular in view of the considerable increase in the number of containers and the limitation of storage areas in the container ports. It has a considerable impact on the efficiency of port operations. In this paper, we propose a new mathematical model for determining the exact storage locations of incoming containers in the storage zone in order to minimize the total travelling distance from the quay to the storage location. The suggested model takes into account different types of containers. It has been validated based on the real case study of the Tunisian Sfax seaport. The obtained results are promising and the storage responsible is satisfied with the reached solutions.

Efficiency of Port Operation Its Influence on Supply Chain Network Design

Supply Chain Management is a very convoluted network that transports the material or product to the end users. The products are distributed through different channels. Ports integrate the supply chains with the consumption market and production market globally and regionally. Also port being an intermediary for supply chain network, the efficiency of port operation has a great influence on the movement of cargo. Port operational indices like average turnaround time of the vessel, average pre-berthing detention and average output per ship berth day have an impact on supply chain network design. The author makes an attempt to find out the most efficient port, among the major ports, based on the above indices, while keeping other indices as constant. The most efficient port may have a competitive advantage over other ports and could be a preferred port in the supply chain network.

Quay crane and yard truck dual-cycle scheduling with mixed storage strategy

In order to enhance the efficiency of port operations, the scheduling problem of the quay cranes and yard trucks is crucial. Conventional port operation mode lacks optimization research on efficiency of port handling operation, yard truck scheduling, and container storage location. To make quay crane operations and horizontal transportation more efficient, this study uses a dual-cycle strategy to focus on a quay crane and yard truck scheduling problem in conjunction with a mixed storage strategy. A dispatching plan for yard trucks is considered, as well as the storage location of inbound containers. Based on the above factors, a mixed-integer programming model is formulated to minimize vessels’ berth time for completing all tasks. The proposed model is solved using a particle swarm optimization-based algorithm. Validation of the proposed model and algorithm is conducted through numerical experiments. Additionally, some managerial implications which may be potentially useful for port operators are obtained.

항만 터미널의 컨테이너 트럭 턴타임 예측 시스템 구축

In the field of logistics, port congestion can cause a seriously negative impact on the cost and efficiency of port operations, due to increased container processing time. Although numerous logistics companies are trying to make their transportation system more efficient, solving the port congestion problem has seldom been studied. In this paper, we explore methods for turn-time prediction of container trucks for efficient port operations. For the dataset, real-world data containing complex data such as truck license plate number, time, and loading/unloading information accumulated for five years at a port terminal company are used and the turn-time prediction algorithm based-on the LSTM model was constructed. For the implementation of the turn-time prediction algorithm, a given time series data was classified into three types: time, day, and week, and used as the input data for the model. When constructing a prediction algorithm based on the time type, it was found that when the input time interval was 7 hours, the time error was 18.31 minutes, which is about a 27% decrease in the time error compared to the time error of 25.17 minutes at 20 hours, which is the lowest input time interval. In the case of the day type, when the time interval is longer, the higher the prediction accuracy can be obtained. When setting the time interval to 20 days, the time error was the highest at 18.18 minutes and the time error was decreased by 30% compared to the time error of 25.82 minutes at the time interval of the 3-day with the lowest accuracy. For the week type, the time error was the lowest at 32.03 minutes when set to a three-week time interval. On the other hand, when the time interval was set to 7 weeks, the time error was 14.13 minutes, showing the time error reduction of more than 57% and the best performance among the total results. In addition, in order to increase the utilization of the above prediction model, we introduced a system consisting of various components such as data acquisition, processing, and analysis along with a mobile user application.

STOCHASTIC MODEL FOR FORECASTING OF CRUISE OR FERRY SHIP ARRIVAL AT SEAPORT FOR INFRASTRUCTURE ASSESSMENT

The topic of the research is the development of new models and methods to assess the efficiency of port operations of sea passenger ports and terminals in order to effectively plan the work and accurate forecasting of infrastructure development. It is noted that changes in cruise and ferry route networks, especially relevant in the conditions of passenger traffic recovery after the gradual lifting of restrictions due to Covid-19, will directly affect port congestion. It is proposed to include into the sphere of decision-making on management of the sea passenger port the models based on consideration of probabilistic processes of cruise or ferry vessels calls. The Baltic Sea region is chosen as an object of research. The main modern directions in the sphere of sea passenger transportations confirming the gradual recovery of the route networks operation are presented. The flow of cruise and ferry vessels of the sea passenger port “Passenger port St. Petersburg “Marine Facade” is chosen as an object of research. The analysis of loading of berths, the analysis of the size of cruise and ferry ships, the route networks of ferry and cruise lines are carried out. As a result of the research a new stochastic model is presented and comparison with known distribution laws is carried out. Based on the data obtained, a confidence zone for making decisions on port congestion is determined. This area is formed on the basis of an assessment of the study of the dynamics of ship traffic nonstationary intensity, followed by the application of correlation-regression analysis and the characteristics of the random function describing the intervals between individual arrivals of cruise and ferry ships. The proposed model includes the ability to consider different priorities, primarily in terms of vessel length. Digital transport model is built in AnyLogic environment for port congestion study. The received data are used for optimization experiments in AnyLogic software environment with the purpose of complex estimation of the seaport operation for a year. Based on the optimization experiment and simulation, data are generated considering different distribution laws and used for decision making in case of uncertainty for infrastructure modernization. The presented model can be used for substantiation of management decisions both on strategic and tactical levels for assessing the efficiency of investment projects on development of sea passenger ports and terminals.

Port Sustainability as a Service: The Design of Bespoke Service Level Agreements (SLAs) to Improve Operational Efficiency at Harbours by Prioritising Social Satisfaction

The maritime industry is a complex ecosystem which is important to manage carefully given the role it plays in handling global trade. Effective operation at a port is dependent on a timely passage of goods, involving multiple competing objectives, one of which is sustainability. Unsurprisingly, given the extent of a port's operations, it is a significant contributor of emissions. A port is a physically demanding industry in which to work, and any degradation in workforce productivity can have a detrimental effect on the port's effective running. Slow operations, combined with dependencies between port stakeholders, can further amplify unsustainability. There are some efforts to explore the digitalization of ports, including the creation of green and smart ports. However, there is general widespread resistance to the introduction of technology in this domain. There are therefore a number of areas in which to make technical contributions to improve the efficiency of port operations. In this paper, we propose using the satisfaction of staff at a port to influence the efficiency of its operations. In recognition of widespread low staff morale at ports in general, we recognize that staff become dissatisfied as operations move out of their control and the resulting consequences of interruptions to performance can lead to disruptions unable to be rectified quickly or easily, with staff motivation remaining low. Once low and unmotivated, there is a possibility that motivation will remain low even for operations which staff are in control of, with a detrimental effect on the efficiency, and therefore sustainability of the port. We believe that measuring and improving the satisfaction of staff for the efficiency it can bring will be possible through the roll-out of sensors supporting an Internet of Things (IoT) architecture across a port. With satisfied staff, they are likely to be motivated to work in an efficient manner, which will carry through to situations when unexpected circumstances outside their control occur. It is therefore through increased IoT use that port sustainability will be supported in our proposal. Enabling this, staff efficiency and satisfaction can be tracked using a connected service made available using Service Level Agreements (SLAs). The cost of the service and subsequent extent to which staff can be monitored and managed for sustainability objectives can be agreed with a service provider in advance. When staff are satisfied, the port will be operated to sustain low costs. When staff satisfaction begins to decline, however, operation will become focused on the performance of the port to identify where bottlenecks exist in inefficient operations and staff output. While simultaneously managing both cost and performance through the satisfaction of staff, the goal is an overall positive contribution to a port's efficiency and sustainability.

Modified Yolov3 for Ship Detection with Visible and Infrared Images

As the demands for international marine transportation increase rapidly, effective port management has become an important issue. Automatic ship recognition can facilitate the realization of smart ports, and improve the efficiency of port operation and management. In order to take into account the processing efficiency and detection accuracy at the same time, the study presented an improved deep-learning network based on You only look once version 3 (Yolov3) for all-day ship detection with visible and infrared images. Yolov3 network can simultaneously improve the recognition ability of large and small objects through multiscale feature-extraction architecture. Considering reducing computational time and network complexity with relatively competitive detection accuracy, the study modified the architecture of Yolov3 by choosing an appropriate input image size, fewer convolution filters, and detection scales. In addition, the reduced Yolov3 was further modified with the spatial pyramid pooling (SPP) module to improve the network performance in feature extraction. Therefore, the proposed modified network can achieve the purpose of multi-scale, multi-type, and multi-resolution ship detection. In the study, a common self-built data set was introduced, aiming to conduct all-day and real-time ship detection. The data set included a total of 5557 infrared and visible light images from six common ship types in northern Taiwan ports. The experimental results on the data set showed that the proposed modified network architecture achieved acceptable performance in ship detection, with the mean average precision (mAP) of 93.2%, processing 104 frames per second (FPS), and 29.2 billion floating point operations (BFLOPs). Compared with the original Yolov3, the proposed method can increase mAP and FPS by about 5.8% and 8%, respectively, while reducing BFLOPs by about 47.5%. Furthermore, the computational efficiency and detection performance of the proposed approach have been verified in the comparative experiments with some existing convolutional neural networks (CNNs). In conclusion, the proposed method can achieve high detection accuracy with lower computational costs compared to other networks.

A Collaborative Berth Planning Approach for Disruption Recovery

Traditionally, terminal operators create an initial berthing plan before the arrival of incoming vessels. This plan involves decisions on when and where to load or discharge containers for the calling vessels. However, disruptive unforeseen events (i.e., arrival delays, equipment breakdowns, tides, or extreme weather) interfere with the implementation of this initial plan. For terminals, berths and quay cranes are both crucial resources, and their capacity limits the efficiency of port operations. Thus, one way to minimize the adverse effects caused by disruption is to ally different terminals to share berthing resources. In some challenging situations, terminal operators also need to consider the extensive transshipment connections between feeder and mother vessels. Therefore, in this work, we investigate a collaborative variant of the berth allocation recovery problem which focuses on the collaboration among terminals and transshipment connections between vessels. We propose a mixed-integer programming model to (re)-optimize the initial berth and quay crane allocation plan and develop a Squeaky Wheel Optimization metaheuristic to find near-optimal solutions for large-scale instances. The results from the performed computational experiments, considering multiple scenarios with disruptive events, show consistent improvements of up to 40% for the suggested collaborative strategy (in terms of costs for the terminal operators).

Design and Development of an AIoT Architecture for Introducing a Vessel ETA Cognitive Service in a Legacy Port Management Solution.

Current Internet of Things (IoT) stacks are frequently focused on handling an increasing volume of data that require a sophisticated interpretation through analytics to improve decision making and thus generate business value. In this paper, a cognitive IoT architecture based on FIWARE IoT principles is presented. The architecture incorporates a new cognitive component that enables the incorporation of intelligent services to the FIWARE framework, allowing to modernize IoT infrastructures with Artificial Intelligence (AI) technologies. This allows to extend the effective life of the legacy system, using existing assets and reducing costs. Using the architecture, a cognitive service capable of predicting with high accuracy the vessel port arrival is developed and integrated in a legacy sea traffic management solution. The cognitive service uses automatic identification system (AIS) and maritime oceanographic data to predict time of arrival of ships. The validation has been carried out using the port of Valencia. The results indicate that the incorporation of AI into the legacy system allows to predict the arrival time with higher accuracy, thus improving the efficiency of port operations. Moreover, the architecture is generic, allowing an easy integration of the cognitive services in other domains.

Hybrid Scheduling for Multi-Equipment at U-Shape Trafficked Automated Terminal Based on Chaos Particle Swarm Optimization

Aimed to improve the efficiency of port operations, Shanghai Zhenhua Heavy Industries Co., Ltd. (ZPMC) proposed a new U-shape trafficked automated terminal. The new U-shape trafficked automated terminal brings a new hybrid scheduling problem. A hybrid scheduling model for yard crane (YC), AGV and ET in the U-shape trafficked automated terminal yard is established to solve the problem. The AGV and ET yard lanes are assumed to be one-way lane. Take the YC, AGV and ET scheduling results (the container transportation sequences) as variables and the minimization of the maximum completion time as the objective function. A scheduling model architecture with hierarchical abstraction of scheduling objects is proposed to refine the problem. The total completion time is solved based on a static and dynamic mixed scheduling strategy. A chaotic particle swarm optimization algorithm with speed control (CCPSO) is proposed, which include a chaotic particle strategy, a particle iterative speed control strategy, and a particle mapping space for hybrid scheduling. The presented model and algorithm were applied to experiments with different numbers of containers and AGVs. The parameters of simulation part refer to Qinzhou Port. The simulation results show that CCPSO can obtain a near-optimal solution in a shorter time and find a better solution when the solution time is sufficient, comparing with the traditional particle swarm optimization algorithm, the adaptive particle swarm optimization algorithm and the random position particle swarm optimization algorithm.

Information technologies and seaport operational efficiency

PurposeThe introduction of information technology (IT) in port operations has been a tremendous contributor to transformation in world trade. This study was carried out to examine the influence of IT on the efficiency of seaport operations.Design/methodology/approachThe study is quantitative in nature, and it relied on a closed-ended self-administered questionnaire to collect primary data. Structural equation modeling (SEM) was used to test the theoretical model and hypothesis.FindingsThe results indicate that IT has a positive direct effect on port operational efficiency (OE) and an indirect effect on port OE through organizational culture (OC). The mediating role of OC is statistically insignificant.Originality/valueThis is among the first few attempts in Sub-Saharan Africa (SSA) that provides researchers with a contemporary view of IT and seaport operations.