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Forecasting cargo throughput is an essential albeit challenging task in ensuring efficient seaport management. In this study, data analytics is employed to analyze the nonlinear dynamic behaviors, as well as disruptions in port throughputs. Further, nonlinear analytical methods, including the Lyapunov exponent (LE), information entropy, Hurst exponent, and wavelet decomposition, are employed to explore the complex dynamic behavior of port throughput under supply chain disruptions. By employing the discrete wavelet transform (DWT) and the long short-term memory (LSTM) network, we develop a novel hybrid model of port throughput forecasting. DWT is employed to decompose the original data into a finite set of frequency components, so that the various hidden features of cargo throughput can be extracted via different modes, such as the trend, residual, and seasonal components. Thereafter, each component, obtained from the DWT spectra, is predicted via a machine learning model. Additionally, hypothesis testing, model evaluation, and statistical significance tests are employed to comprehensively evaluate the introduced forecasting models. Regarding prediction accuracy and efficiency, our extensive simulation results confirm the superiority of the hybrid strategy over five benchmarked models. Finally, by employing business forecasting software, we show that the robust hybrid strategy achieves accurate predictions of port throughputs against market disruptions. Our findings can help decision-makers understand disruption mechanisms in port systems, thus enabling them to successfully achieve their business goals.

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Pricing and production policies play a key role in ensuring the added value of supply chain systems. For perishable inventory management, the pricing and production lines must be manipulated dynamically since several uncertainties are involved in the system’s behavior. This study discusses the impact of dynamic pricing and production policies on an uncertain stochastic inventory system with perishable products. The mathematical model of the inventory management system under external disturbance is formulated using a continuous differential equation in which the price and production rates are considered as control factors to optimize total profits, which is described as an objective function. An analytical solution for the optimal pricing and production rate was obtained using the Hamilton-Jacobi-Bellman equation. The unknown disturbance was approximated using an intelligent approach called radial basis function neural network. Finally, extensive numerical simulations were presented to validate the theoretical results and optimization solutions (including the efficiency of the approximation of the unknown disturbance) for the dynamic pricing and production management strategy of an uncertain stochastic inventory system against volatile markets. The performance of the proposed method was analyzed under different stock level conditions, which highlighted the importance of keeping the inventory levels at an optimal range to ensure the profitability of business operations. This management strategy can assist a business with solutions for inventory policies while supporting decision-making processes to facilitate coping with production management disruptions.