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One of the most important objectives of operations optimization in container terminal is to reduce the reshuffle ratio, whose important way is reshuffle optimization during picking up import containers. Based on analyzing the actual reshuffles of import containers in container yard, a simulation model of reshuffle process is proposed with Petri-net. Using simulation, the paper analyzes the correlation probability of reshuffles and its factors. Then a relocation probability estimate method is suggested with the main influencing factors. A total least-reshuffle-estimate rule based heuristic algorithm for reshuffle optimization of import containers. Simulation experiments results show that the accuracy of proposed algorithm can be 15% times more than OH and IH algorithm while solving larger-scale problems. [ABSTRACT FROM AUTHOR]

To improve urban passenger prediction accuracy with parametric projection pursuit regression, acceleration genetic algorithm was improved with cat map, Gaussian distribution and local searching. A new chaos accelerating genetic algorithm (NCAGA) was presented, used to optimize the best projection direction a of PPPR model. A hybrid algorithm of NCAGA-PPPR urban passenger forecasting model was proposed, in which the best projection direction was hybrid optimized inner by the NCAGA at the time of optimizing outer the number of ridge functions M. The simulation prediction was made with observed data, compared with BP neural network model, traditional PPR model and PPPR model optimized by acceleration genetic algorithm. The urban passenger forecasting accuracy is higher than the others, which the mean absolute relative error is less than 3.1%. The new hybrid algorithm can improve prediction accuracy of urban passenger and can be used efficaciously to forecast the urban passenger. [ABSTRACT FROM AUTHOR]