基于哈佛结构和群集智能的集装箱码头物流系统建模优化

Modeling and optimization of container terminal logistics systems based on Harvard architecture and swarm intelligence

在当前港口间竞争日益白热化的背景下,集装箱码头物流系统(Container Terminal Logistics Systems,CTLS)生产调度和管理决策水平的高低对于其提高自身运作效率和通过能力,具有至关重要的作用.利用基于Agent的计算及源于Agent技术的群集智能,在基于仿真的优化的思想下,提出基于哈佛结构和群集智能的CTLS建模优化体系.通过对CTLS中场桥调度问题的实例仿真分析,可得出上述体系可明显提高作业设备的利用率和堆场吞吐量,从而验证了该建模优化方法的可行性与可信性.

Under the background of cut-throat competition among the ports day by day,the scheduling and decision-making level of container terminal logistics systems（CTLS） possess the significance to improve the efficiency and throughput of harbors.Integrating and fusing agent-based computing and swarm intelligence that also roots in multi-agent,this paper presents the modeling and optimization framework based on Harvard architecture and swarm intelligence.Simulation and analysis of yard crane dispatching show that the framework can improve the utilization rate of equipments and throughput of yard,which validates the feasibility and creditability of the methodology consequently.