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Container throughput analysis and seaport operations management using nonlinear control synthesis

机译:使用非线性控制合成的集装箱吞吐量分析与海港运营管理

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This paper is to explore the dynamical analysis and active control synthesis for improving seaport operations through system optimization. First, the multi-dimensional system of the fractional Lotka-Volterra model is utilized to investigate its underlying dynamics of port competition and cooperation for container throughput. The nonlinear dynamical behaviors are intensely examined by using eigenvalue evaluation, bifurcation analysis and time series analysis to illustrate the stability of the competition and collaboration model. With the system theory based on fractional order calculus, various dynamic properties of ports system have been discovered. Based on the dynamical analysis, adaptive fractional-order sliding mode control with artificial neural network algorithm has been realized for port growth rate improvement against disruptions. Furthermore, the proposed approach is successfully validated by the case study of major Korean seaports for improving port operations through neural network prediction. The decision making policies implemented by control algorithms are guaranteed to improve the port growth rate against disruptions. Specifically, the novel management strategy is to provide managerial insights and innovative solutions for seaport authorities who can make more efficient strategic planning for handling risk management in maritime logistics.
机译:本文是通过系统优化探讨改善海港操作的动态分析和主动控制合成。首先,利用分数Lotka-Volterra模型的多维系统来研究其港口竞争和集装箱吞吐量合作的潜在动态。通过使用特征值评估,分岔分析和时间序列分析来阐述非线性动力学行为,以说明竞争和协作模型的稳定性。利用基于分数阶微积分的系统理论,已经发现了端口系统的各种动态特性。基于动力学分析,实现了具有人工神经网络算法的自适应分数级滑动模式控制,用于对中断的港口增长速度改善。此外,通过神经网络预测来改善港口运营的主要韩国海港的案例研究,成功验证了拟议的方法。由控制算法实施的决策政策得到保证,以提高抵御中断的港口增长率。具体而言,新颖的管理策略是为海港机构提供管理洞察和创新解决方案,他们可以在海上物流中处理风险管理的更有效的战略规划。

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