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The prediction of port throughput is very complicate, and its accuracy is affected by many socio-economic factors, particularly affected by their embedded distributed randomness of these factors and mixed noises produced in the processes of data collection, transformation, and calculation. Firstly, in view of the v-support vector regression hybridized with Gauss function (briefed as Gauss- vSVR model), to well solve the nonlinear and mixed noises, this paper uses this model to simulate the nonlinear evolving system of port throughput series. Then, to look for more suitable parameter combination of this model and take into account that GA still suffers from the problems of trapped into local optima and time-consuming, this study integrates the global chaotic perturbation algorithm by using Cat mapping function and local acceleration search algorithm by employing cloud theory, i.e., abbreviated as chaotic cloud genetic algorithm (CCGA), to well determine the parameter values for an Gauss- vSVR model. Additionally, based on the principal component analysis and correlation analysis method, an input vector decision method (namely IVD) is proposed to identify the final input variables for Gauss- vSVR model. Finally, hybridization of IVD and CCGA with Gauss- vSVR model, namely IG vSVR-CCGA, is proposed for port throughput forecasting. Subsequently, the port throughput data and its associate socio-economic factors of two largest Chinese ports, Shanghai Port and Tianjin Port, are employed as practical examples to test forecast performance. The numerical results indicate that the proposed hybrid forecasting model receives more satisfied forecasting performance than other classical prediction models; in the meanwhile, the CCGA algorithm also obtains higher optimal efficiency than other alternative algorithms. [ABSTRACT FROM AUTHOR]

The demand for the maritime transportation has significantly increased over the past 20 years due to the rapid pace of globalization. Terminal managers confront the challenge in establishing the appropriate berth and quay crane (QC) coordinated schedule to achieve the earliest departure time of ship and to provide efficient service. In this paper, we propose a multi-objective berth and QC coordinated scheduling model, namely M-B&QC, by taking the minimum additional trucking distance and the port time of ships as the optimization objectives, with the constraints based on demand of port operations and vessel berthing. To solve the M-B&QC model, the particle coding rule and the particle feasible-integer processing module (namely PF-IP) for improving PSO performance are employed to determine the computation strategies of individual historical optimal value $$p_{i}^{G}$$ and global optimal value $$P_{g}^{G}$$ of particle for the multi-objective optimization. In addition, the global disturbance with cat mapping function (namely GDCM) and local search with cloud model (namely LSCM) are also hybridized, namely PM&CCPSO algorithm, to solve the M-B&QC model. Numerical experiments including eight combined examples are conducted to test the performance of the proposed programming model and the modified solving algorithm. [ABSTRACT FROM AUTHOR]