Search

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]

The prediction of vessel traffic flow is complicated, its accuracy is influenced by uncertain socio-economic factors, especially by the singular points existed in the statistical data. Recently, the robust v -support vector regression model ( R SVR) has been successfully employed to solve non-linear regression and time-series problems with the singular points. This paper will firstly propose a novel hybrid algorithm, namely chaotic cloud simulated annealing genetic algorithm (C cat CSAGA) for optimizing the parameters of R SVR, to improve the performance of vessel traffic flow prediction. In which, the proposed C cat CSAGA employs cat mapping to carefully expand variable searching space, to overcome premature local optimum, and uses cloud model efficiently to search a better solution in a small neighborhood of the current optimal solution, to improve the search efficiency. Secondly, the kernel principal component analysis (KPCA) algorithm is adopted to determine the final input vectors from the candidate input variables. Finally, a numerical example of vessel traffic flow and its influence factors data from Tianjin are employed to test the forecasting performance of the proposed K R SVR-C cat CSAGA model. [ABSTRACT FROM AUTHOR]

Abstract: In order to improve forecasting accuracy of urban traffic flow, this paper applies support vector regression (SVR) model with Gauss loss function (namely Gauss–SVR) to forecast urban traffic flow. By using the input historical flow data as the validation data, the Gauss–SVR model is dedicated to reduce the random error of the traffic flow data sequence. The chaotic cloud particle swarm optimization algorithm (CCPSO) is then proposed, based on cat chaotic mapping and cloud model, to optimize the hyper parameters of the Gauss–SVR model. Finally, the Gauss–SVR model with CCPSO is established to conduct the urban traffic flow forecasting. Numerical example results have proved that the proposed model has received better forecasting performance compared to existing alternative models. Thus, the proposed model has the feasibility and the availability in urban traffic flow forecasting fields. [Copyright &y& Elsevier]

Support vector regression with chaotic sequence and simulated annealing algorithm in previous forecasting research paper has shown its superiority to effectively avoid trapping into a local optimum. However, the proposed chaotic simulated annealing (CSA) algorithm in previous published literature as well as the original SA algorithm could not realize the mechanism of temperature decreasing continuously. In addition, lots of chaotic sequences adopt Logistic mapping function which is distributed at both ends in the interval [0,1], thus, it could not excellently strengthen the chaotic distribution characteristics. To continue exploring any possible improvements of the proposed CSA and chaotic sequence, this paper employs the innovative cloud theory to be hybridized with CSA to overcome the discrete temperature annealing process, and applies the Cat mapping function to ensure the chaotic distribution characteristics. Furthermore, seasonal mechanism is also proposed to well arrange with the cyclic tendency of electric load, caused by economic activities or climate cyclic nature. This investigation eventually presents a load forecasting model which hybridizes the seasonal support vector regression model and chaotic cloud simulated annealing algorithm (namely SSVRCCSA) to receive more accurate forecasting performance. Experimental results indicate that the proposed SSVRCCSA model yields more accurate forecasting results than other alternatives. [ABSTRACT FROM AUTHOR]