计算机工程与应用 ›› 2021, Vol. 57 ›› Issue (19): 290-298.DOI: 10.3778/j.issn.1002-8331.2006-0129

• 工程与应用 • 上一篇    

考虑作业状态能耗的集装箱码头设备协调调度

代江涛,韩晓龙   

  1. 上海海事大学 物流科学与工程研究院,上海 201306
  • 出版日期:2021-10-01 发布日期:2021-09-29

Coordinated Scheduling of Equipment in Container Terminals Considering Energy Consumption Under Different Job Status

DAI Jiangtao, HAN Xiaolong   

  1. Institute of Logistics Science and Engineering, Shanghai Maritime University, Shanghai 201306, China
  • Online:2021-10-01 Published:2021-09-29

摘要:

绿色港口日渐成为港口发展的必然趋势,为了提高集装箱码头的服务水平及降低其能耗,综合分析了集装箱码头的装卸作业流程,考虑岸桥、场桥、集卡在不同作业状态下的能耗,且以总完工时间和总作业能耗最小为目标建立了多目标混合整数规划模型。使用MATLAB编码改进自适应遗传算法求解所建模型,并分别与CPLEX和原始遗传算法的求解结果作对比,证明了该算法的优秀性。更改能耗目标和作业时间目标所占权重进行求解,发现考虑各设备在不同作业状态下的能耗会影响总完工时间,且能耗与作业时间是相互冲突的目标,追求低能耗会造成作业效率的牺牲。分析结果表明,所建模型和算法在岸桥、场桥和集卡的协调调度问题中可以帮助决策者更好地权衡作业时间和能耗目标。

关键词: 能耗, 协调调度, 作业状态, 改进自适应遗传算法

Abstract:

Green port has become the inevitable trend of port development, to reduce the energy consumption and improve the service level of container terminal, analyze the handling process, consider the energy consumption of Quay Cranes(QC)、Yard Cranes(YC)and Trucks(IT) under different job status. A multi-objective mixed integer programming model is established to minimize the operation time and total energy consumption, improved adaptive genetic algorithm is coded by MATLAB to solve the model, and the results of CPLEX and genetic algorithm are compared to prove the excellence of the proposed algorithm. Changing the weight of the energy consumption target and the operation time target and found that considering the energy consumption of each equipment under different job status will affect the operation time, the energy consumption and the operation time are conflicting targets, and the pursuit of low energy consumption will cause sacrifice of operation efficiency. The analysis results show that the model and the algorithm can help the decision makers to balance the operation time and the energy consumption targets in the coordinated scheduling problem of QC, YC and IT.

Key words: energy consumption, coordinated scheduling, job status, improved adaptive genetic algorithm