Shuttle-based storage and retrieval systems (SBS/RSs), a new type of “part-to-picker” system, have attracted public attention because of their flexibility and robustness. The systems consist of two sub-systems—shuttles and lifts—which are responsible for horizontal movements and vertical movements, respectively. SBS/RSs are categorized into tier-to-tier shuttles and tier-captive shuttles. This paper mainly researches SBS/RSs with tier-to-tier shuttles. New challenges have emerged since the commercial simulation-based optimization software has become widely applied for, for instance, task scheduling problems. Specifically, to decrease the idle time of lifts and the waiting time of shuttles, an integer programming scheduling model is proposed to minimize total task time with constraints on orders and devices. The Gurobi linear programming solver was utilized to obtain the optimal order sequence and the shortest operation time. The overall system efficiency was improved using our model. Moreover, the results are significant for reducing capital investment and operating cost.
The submarine cable guarantees the electricity and communication of the island residents. The operation of fishing boats poses a major threat to the submarine cable. Due to the complex environment, manual monitoring has defects such as strong subjective factors and easy fatigue. The paper adopts the intelligent monitoring method, using the object detection algorithm based on deep learning and the camera to monitor the boats on the sea. Use feature pyramid network to enhance the detection of smaller and farther boats. Use deformable convolution to solve the problem of few samples. Experimental results show that model can detect boats. The detection ability of the feature pyramid network is stronger, especially for the distant and smaller boat targets. Using deformable convolution can improve the accuracy of models trained on small dataset.
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