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Simulation-Based Optimization for Storage Allocation Problem of Outbound Containers in Automated Container Terminals

机译:基于仿真的自动化集装箱码头出港集装箱存储分配问题优化

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Storage allocation of outbound containers is a key factor of the performance of container handling system in automated container terminals. Improper storage plans of outbound containers make QC waiting inevitable; hence, the vessel handling time will be lengthened. A simulation-based optimization method is proposed in this paper for the storage allocation problem of outbound containers in automated container terminals (SAPOBA). A simulation model is built up by Timed-Colored-Petri-Net (TCPN), used to evaluate the QC waiting time of storage plans. Two optimization approaches, based on Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), are proposed to form the complete simulation-based optimization method. Effectiveness of this method is verified by experiment, as the comparison of the two optimization approaches.
机译:出站集装箱的存储分配是自动化集装箱码头中集装箱处理系统性能的关键因素。出站容器的存储计划不当,不可避免地要进行质量检查。因此,将延长船只的处理时间。针对自动化集装箱码头(SAPOBA)中出站集装箱的存储分配问题,提出了一种基于仿真的优化方法。定时着色Petri网(TCPN)建立了一个仿真模型,用于评估存储计划的QC等待时间。提出了两种基于粒子群优化(PSO)和遗传算法(GA)的优化方法,形成了完整的基于仿真的优化方法。通过对两种优化方法的比较,通过实验验证了该方法的有效性。

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