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A Method Based on SNSO for Solving Slot Planning Problem of Container Vessel Bays

机译:基于SNSO的集装箱船舱位规划问题解决方法

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Stowage planning has an important effect in container shipping and is also a hard combinatorial problem. In order to improve the operation efficiency and reduce the cost, a new optimization method called Social Network-based Swarm Optimization Algorithm (SNSO) is applied to solve the slot planning problem of container vessel bays. As a swarm intelligence optimization algorithm, SNSO is designed with considering population topology, neighborhood and individual behavior comprehensively to improve the swarm search ability. An effective coding and decoding strategy is proposed to optimize the slot planning problem for using SNSO. Finally, fourteen cases of slot planning with different scales are selected to test the proposed algorithm and five swarm intelligence algorithms are selected for comparison in the experiment. The results show that the SNSO has a better performance on solving stowage plan problem in the terms of convergence and accuracy than other selected algorithms.
机译:积载计划在集装箱运输中具有重要作用,并且也是一个难以解决的组合问题。为了提高作业效率,降低成本,提出了一种基于社交网络的群优化算法(SNSO)的优化方法,以解决集装箱船舱位规划问题。 SNSO作为一种群体智能优化算法,在综合考虑人口拓扑,邻域和个体行为的基础上进行设计,以提高群体搜索能力。提出了一种有效的编码和解码策略,以优化使用SNSO的时隙规划问题。最后,选择了十四种不同规模的时隙规划案例对所提出的算法进行了测试,并选择了五种群体智能算法进行实验比较。结果表明,与其他算法相比,SNSO在收敛性和准确性方面具有更好的解决配载计划问题的性能。

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