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Digital twins can facilitate high-fidelity representations of container terminals by applying various technologies and methods to better measure, understand, and improve operations. In this paper, a decision support system (DSS) based on digital twin and big data technologies is designed to demonstrate how real-time monitoring and an integrated decision support can be established. The DSS provides optimal operation plans and the benchmark for vessel delay early warnings through different resource allocation simulations at the planning level. It further enables real-time operational decision making through real-time monitoring and efficiency analyses using big data engines at the operational level. A case study is conducted for the ultralarge Yangshan Deepwater Automated Container Terminal Phase IV (ACT4) in Shanghai (China) and experimental results have revealed that the proposed digital twin-based DSS can help ACT4 operators to evaluate vessel service using optimized resource allocation plans and operations.

Information systems have become indispensable to the competitiveness of ports, facilitating communication and decision making for enhancing the visibility, efficiency, reliability, and security in port operations under various conditions. Providing value-added information services and analytics is increasingly important to maintain a competitive edge and to fulfill regulatory requirements. Consequently, it is necessary to survey current information systems both from an academic and practical standpoint. In this paper, we present a classification and a comprehensive survey of information systems and related information technologies applied in ports. As such, the paper provides a state-of-the-art information-centric view on port operations and aims to bridge the gap between industry solutions and academic works.

Increasing freight volumes and challenging environments in seaports and container terminals worldwide require streamlined and reliable operations. Digital twins are seen as important drivers of the digitalization in seaports by providing a basis for higher transparency, control and data-driven decision making. In this context, however, the concept is rarely studied, and implementation issues are not comprehensively discussed. The paper presents an exploratory study of digital twins in seaports based on a literature review and case studies. The analysis reveals a standardization deficit for digital twin implementations, an inflationary and improper use of the term digital twin, and fields of research that need to be explored further. The application of optimization methods and the integration of simulation-based optimization in the field of seaports and container terminals is examined, due to its relevance for digital twins. Important lessons learned can be taken from the most advanced implementations, integrating simulations and emulations with optimization methods. An in-depth examination of multiple case studies and discussions with global port leaders yields valuable perspectives on the varied levels of digital twin implementations being applied today, including insights into the most advanced implementations currently being used in ports and container terminals. As a result of the analyses conducted, various research directions and a research agenda are presented.

Digitalization is pushing the maritime industry beyond its traditional limits and provides many new opportunities to enhance the productivity, efficiency, and sustainability of logistics. The concept of smart ports, for instance, aims to adopt modern information technologies to enable a better planning and management within and between ports. Strong facilitators of the digitalization are investments into technology and cooperations for promoting information sharing and a better coordination and collaboration, often regarded as a stumbling block in highly competitive environments. Besides many new opportunities, important economic issues and problems arise. We provide an overview of the development and state-of-the-art of digital transformation in modern seaports in order to identify current potentials and barriers. Focusing on the crucial and challenging aspects of coordination and collaboration, we present a conceptual game theoretic framework that allows benefits and cost allocations considering inter-, intra-, and meta-organizational perspectives. We further demonstrate how this framework can be used to develop tools and methods for supporting strategic decision making for driving the digital transformation in seaports and addressing new economic issues and problems.

Globally, efforts are made to balance energy demands and supplies while reducing CO2 emissions. Germany, in its transition to renewable energies, faces challenges in regulating its energy supply. This study investigates the impact of various technologies, including energy storage solutions, peak shaving, and virtual buffers in a smart energy grid on a large scale. Real-time energy supply and demand data are collected from the Port of Hamburg and HafenCity in Germany to analyze the characteristics of different technologies such as load shifting of reefer containers and private electric vehicles’ energy, as well as pumped hydro storage. Through simulations, we assess the usability of renewable energies in a smart grid with versatile energy demands and determine the effects of peak shaving, storage solutions, and virtual buffers on uncertain energy supply. Our case study reveals that integrating smart grid technologies can reduce the overproduction of renewable energies needed to prevent blackouts from 95% to 65% at the HafenCity and the Port of Hamburg. Notably, large, reliable, and predictable energy consumers like the Port of Hamburg play a vital role in managing the uncontrollable nature of renewables, resulting in up to 31% cost savings for new infrastructure.

The seemingly unlimited growth of containerized transport is nowadays associated with an increasing number of seaport container terminals and facilities as well as demand for port-centric value-added and just-in-time logistics services. Intense global and local competition as well as geographical limitations urgently require efficient means to handle inter-terminal transportation. Many factors influence the productivity and efficiency of inter-terminal transportation as well as its economic and environmental implications. In the last two decades, these aspects have led to a growing interest in research, in particular concerning decision analytics and innovative information technology aiming to better understand, improve, and operate inter-terminal transportation. In this paper, we present a chronological overview of related works as an annotated bibliography in order to reflect the current state of research. Furthermore, we identify future research issues and propose a respective research agenda.

Besides the transport of containers between transshipment areas, an increasing amount of containers needs to be moved to value-added logistics and auxiliary service areas leading to additional container flows within a seaport. Both real-time information exchange and optimization are necessary to efficiently coordinate actors and container movements being involved in respective inter-terminal transport (ITT). However, there is no decision support system facilitating real-time planning and management of ITT taking advantage of modern information technologies and optimization algorithms. In this paper, we formulate the inter-terminal truck routing problem as a novel optimization problem and propose two greedy heuristics and two hybrid simulated annealing algorithms. The computational experiments, conducted using real locations from the Port of Hamburg (Germany), are evaluated extensively. They indicate that the proposed hybrid simulated annealing algorithms are able to report feasible and improved routes within seconds. The optimization component is embedded into a scalable cloud platform that integrates both real-time data from truck drivers using a mobile app and current traffic data. As such, the proposed mobile cloud platform realizes the vision of a decision support system facilitating real-time communication and context-aware ITT planning for reducing costs and the carbon footprint.

Ubiquitous computing technologies and information systems pave the way for real-time planning and management. In the process of dynamic vehicle dispatching, the adherent challenge is to develop decision support systems using real-time information in an appropriate quality and at the right moment in order to improve their value creation. As real-time information enables replanning at any point in time, the question arises when replanning should be triggered. Frequent replanning may lead to efficient routing decisions due to vehicles’ diversions from current routes while less frequent replanning may enable effective assignments due to gained information. In this paper, the authors analyze and quantify the impact of the three main triggers from the literature, exogenous customer requests, endogenous vehicle statuses, and replanning in fixed intervals, for a dynamic vehicle routing problem with stochastic service requests. To this end, the authors generalize the Markov-model of an established dynamic routing problem and embed the different replanning triggers in an existing anticipatory assignment and routing policy. They particularly analyze under which conditions each trigger is advantageous. The results indicate that fixed interval triggers are inferior and dispatchers should focus either on the exogenous customer process or the endogenous vehicle process. It is further shown that the exogenous trigger is advantageous for widely spread customers with long travel durations and few dynamic requests while the endogenous trigger performs best for many dynamic requests and when customers are accumulated in clusters.