考虑能耗节约的集装箱码头装卸设备集成调度

Integrated scheduling of handling operations in container terminal with considering energy saving

针对自动化集装箱码头中岸桥与人工智能运输机器人(ART)的集成调度问题,依据作业阶段将设备能耗划分成多个表现形式,构建以最小化岸桥和ART的总能耗为目标的整数规划模型。为提高求解质量,提出一种具有重组变异和随机扰动的自适应粒子群算法。根据不同时期的搜索需求,对惯性权重实行自适应调整;并引入随机粒子增强个体的交互能力,结合迭代进程对最优粒子实施不定维更新,为其摆脱局部困境提供更多机会。最后,以天津港北疆C段自动化集装箱码头为研究背景设计了不同规模算例,将改进算法与GUROBI求解器以及其他算法进行比较,验证了模型和算法的有效性。结果表明,随着岸桥和ART的配置数量逐渐增加,作业进程加快,码头总能耗分别呈现降低和先降后升的趋势;此外,相比于传统的调度模型,所提方法能够在较短的完工时间里节约更多的作业能耗。

Aiming at the integrated scheduling problem of quay crane and Artificial Intelligence Robot of Transportation(ART)in automated container terminal,the energy consumption were divided into multiple forms according to the different operation states of the equipment.An integrated scheduling model was established,which took the minimum energy consumption of quay crane and ART as optimization target.To improve the solution quality,an adaptive particles swarm optimization with recombination variation and random disturbance was proposed.According to the search requirements in different stages,the inertia weight was adjusted adaptively.In addition,the random particles were introduced to enhance the interaction ability of individuals.Combined with the iterative process,some dimensions of the optimal particle were updated indefinitely,which provided more opportunities for getting rid of local difficulties.Taking the automated container terminal in section C of Tianjin Beijiang as the research background,the numerical experiments with instances of different sizes were conducted to compare improved algorithm with the GROBI solver and other algorithms,and the effectiveness of the proposed model and algorithm were verified.The results showed that the operation process was accelerated with the gradual increase of the number of quay crane and ART,and the total energy consumption of the terminal was decreased and then increased respectively.Compared with the traditional scheduling model,the proposed method could save more energy consumption in shorter completion time.