Maritime transportation plays a critical role in global trade as it accounts for over 80% of all merchandise movement. Given the growing volume of maritime freight, it is vital to have an efficient system for handling ships and cargos at ports. The current first-come-first-serve method is insufficient in maintaining operational efficiency, especially under complicated conditions such as parallel scheduling with different cargo setups. In addition, in the face of rising demand, data-driven strategies are necessary. To tackle this issue, this paper proposes a mixed-integer model for allocating quay cranes, terminals, and berths. It considers not only cargo types, but also the time required for a quay crane setup. The proposed model features a greedy-insert-based offline algorithm that optimizes berth allocation when vessel information is available. In situations where vessel information is uncertain, the model utilizes an online optimization strategy based on a reinforcement-learning algorithm that is capable of learning from feedback and of adapting quickly in real time. The results of the numerical experiments demonstrate that both the offline and online algorithms can significantly enhance cargo handling efficiency and overall harbor operation. Furthermore, they have the potential to be extended to other complex settings.
With the development of human beings and the improvement of science and technology, computer information technology has played a huge role in various industries, food safety is always the focus of human attentions, and makes it more convenient and quick to grasp the dynamic of food safety due to the application of information technology. This paper will discuss the application of information technology in the detection of food safety and food safety traceability system. If you want to control our food security, you must strengthen the application of information technology.
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