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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]

Abstract: Effective berth and quay-crane allocation improves service level of container terminal. For an efficient assignment, stochastic characteristic of containership arrival time and handling time is a key factor. A berth & quay-crane allocation model under stochastic environments is proposed in this article, so as to minimize the average waiting time of containership in terminal. However, it is solved in polynomial CPU time. So to obtain a good solution with considerably small computational effort, a genetic algorithm is developed with a reduced search set on its property. Numerical experiments show that the proposed model is capable of efficiently and dynamically allocating berths and quay-cranes to calling containerships in real stochastic environments and reflects the risk preference of decision-maker. And the solutions of GA are stable and satisfactory in acceptable CPU time. [Copyright &y& Elsevier]