The hybrid intelligence swam algorithm for berth-quay cranes and trucks scheduling optimization problem

Yi Liu, Tieqiao Liu
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引用次数: 1

Abstract

Considered the cooperation of the container truck and quayside container crane in the container terminal, this paper constructs the model of the quay cranes operation and trucks scheduling problem in the container terminal. And the hybrid intelligence swarm algorithm combined the particle swarm optimization algorithm(PSO) with artificial fish swarm algorithm (AFSA) was proposed. The hybrid algorithm (PSO-AFSA) adopt the particle swarm optimization algorithm to produce diverse original paths, optimization of the choice nodes set of the problem, use AFSA's preying and chasing behavior improved the ability of PSO to avoid being premature. the proposed algorithm has more effectiveness, quick convergence and feasibility in solving the problem. The results of stimulation show that the scheduling operation efficiency of container terminal is improved and optimized.
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基于混合智能游动算法的泊位码头起重车辆调度优化问题
考虑到集装箱码头中集装箱汽车与岸旁集装箱起重机的协同工作,构建了集装箱码头岸旁起重机作业与车辆调度问题的模型。提出了将粒子群优化算法(PSO)与人工鱼群算法(AFSA)相结合的混合智能群算法。混合算法(PSO-AFSA)采用粒子群优化算法产生多样化的原始路径,优化问题的选择节点集,利用AFSA的捕食和追逐行为提高了PSO避免早熟的能力。该算法具有更强的有效性、收敛速度快和求解问题的可行性。仿真结果表明,该方法提高和优化了集装箱码头的调度作业效率。
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