The volume of instant delivery has witnessed a significant growth in recent years. Given the involvement of numerous heterogeneous stakeholders, instant delivery operations are inherently characterized by dynamics and uncertainties. This study introduces two order dispatching strategies, namely task buffering and dynamic batching, as potential solutions to address these challenges. The task buffering strategy aims to optimize the assignment timing of orders to couriers, thereby mitigating demand uncertainties. On the other hand, the dynamic batching strategy focuses on alleviating delivery pressure by assigning orders to couriers based on their residual capacity and extra delivery distances. To model the instant delivery problem and evaluate the performances of order dispatching strategies, Adaptive Agent-Based Order Dispatching (ABOD) approach is developed, which combines agent-based modelling, deep reinforcement learning, and the Kuhn-Munkres algorithm. The ABOD effectively captures the system's uncertainties and heterogeneity, facilitating stakeholders learning in novel scenarios and enabling adaptive task buffering and dynamic batching decision-makings. The efficacy of the ABOD approach is verified through both synthetic and real-world case studies. Experimental results demonstrate that implementing the ABOD approach can lead to a significant increase in customer satisfaction, up to 275.42%, while simultaneously reducing the delivery distance by 11.38% compared to baseline policies. Additionally, the ABOD approach exhibits the ability to adaptively adjust buffering times to maintain high levels of customer satisfaction across various demand scenarios. As a result, this approach offers valuable support to logistics providers in making informed decisions regarding order dispatching in instant delivery operations.

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Land subsidence severely threatens most of the coastal plains around the world where high productive industrial and agricultural activities and urban centers are concentrated. Coastal subsidence damages infrastructures and exacerbates the effect of the sea-level rise at regional scale. Although it is a well-known process, there is still much more to be improved on the monitoring, mapping and modeling of ground movements, as well as the understanding of controlling mechanisms. The International Geoscience Programme recently approved an international project (IGCP 663) aiming to bring together worldwide researchers to share expertise on subsidence processes typically occurring in coastal areas and cities, including basic research, monitoring and observation, modelling and management. In this paper, we provide the research communities and potential stakeholders with the basic information to join the participating teams in developing this project. Specifically, major advances on coastal subsidence studies and information on well-known and new case studies of land subsidence in China, Italy, The Netherlands, Indonesia, Vietnam and Thailand are highlighted and summarized. Meanwhile, the networking, dissemination, annual meeting and field trip are briefly introduced.

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Offshore wind is a relatively new industry and it is generally more expensive to generate electricity than many alternative renewable sources. Operation & Maintenance (O&M) makes up a significant part of the overall cost of running Offshore Wind Turbines (OWT). Since the O&M associated responsibility is shared among turbine manufacturers, wind farm operators and the offshore transmission owners, this has inevitably led to lack of information, duplication of effort and less efficiency. Big data analytics is one great technique that will drive future growth. In this paper, an integrated operational system of offshore wind farm is proposed deploying big data analytics. Firstly, the current state of the O&M of offshore wind farm and the big data analytics are introduced. Afterwards, a predictive maintenance model and a maintenance implementation model are proposed, and an integrated operational system is developed incorporating those two models in order to optimize maintenance planning and implementation. Finally, the possible contribution of such a system to a more effective O&M of offshore wind farm is discussed. @en

Maritime accidents and incidents, such as ship to ship collision or ship grounding, most often occur near ports due to the intensive water traffic and shallow water depth. The occurrence of those accidents place a port in a very dangerous situation, since the port could suffer from a high risk of blockage of port entry, and the consequent economic loss can be substantial. In the case of maritime accidents, it is of great significance for sea-going ships outside of the accident zone to determine a safe and smooth sailing route and efficient speeds at a busy port, thus to reduce the risk of blockage and the economic loss of the port, whilst not disturbing the current emergency logistic response system. In this regard, a decision-making method for safe and smooth sailing route and efficient speeds for sea-going ships encountering an accident can be very valuable. Because of the high nonlinearity of these decisions, PSO algorithm is used to search the safe and smooth sailing route and efficient speeds. The study results show that the proposed method can effectively achieve the safe and smooth sailing route and efficient speeds for ships encountering an accident, thus to be more economic and reduce the risky chance of blockage of the port entry.@en