上海交通大学学报

上海交通大学学报（自然版） ›› 2017, Vol. 51 ›› Issue (11): 1367-1373.doi: 10.16183/j.cnki.jsjtu.2017.11.013

• 兵器工业 • 上一篇    下一篇

集装箱堆场箱位分配及多场桥调度协同优化问题

范厚明，马梦知，姚茜,郭振峰   

  1. 大连海事大学 交通运输管理学院， 辽宁 大连 116026
  • 出版日期:2017-11-30 发布日期:2017-11-30
  • 基金资助:
    国家自然科学基金项目（61473053），辽宁省社会科学规划基金重点项目（L16AGL004），大连市科学技术计划项目（2015D12ZC181）

Integrated Optimization of Storage Space Allocation and Multiple Yard Cranes Scheduling in a Container Terminal Yard

FAN Houming,MA Mengzhi,Yao Xi,GUO Zhenfeng   

  1. Transportation Management College, Dalian Maritime University, Dalian 116026, Liaoning, China
  • Online:2017-11-30 Published:2017-11-30

摘要： 为提高集装箱码头堆场作业效率和降低运营成本，对堆场箱位分配及多场桥调度协同优化问题进行了研究.采用分区域平衡策划方法进行场地策划，考虑场桥间安全距离和场桥作业量均衡，以场桥移动成本和空闲成本之和最小为目标，建立集装箱堆场箱位分配及多场桥调度协同优化模型.为增强遗传算法的全局搜索能力，将模拟退火思想引入到遗传算法进化过程中，设计模拟退火遗传算法进行求解.通过数值实验验证了算法的有效性，并针对箱区不同规模的出口箱量进行实验分析，结果表明分区域平衡策划方法可以更好地解决箱位分配及多场桥调度协同优化问题.

关键词: 集装箱堆场, 箱位分配, 场桥调度, 模拟退火遗传算法

Abstract: In order to improve the operational efficiency and reduce the operational cost of container terminal yard, this paper studies the integrated optimization of storage space allocation and multiple yard cranes scheduling in a container terminal yard. The regional workload balance planning method is adopted to make yard working plan, and an integrated optimization model of storage space allocation and multiple yard cranes scheduling is established. It reduces the idle cost and moving cost of yard cranes considering the safety distance and workload balance between yard cranes. The simulated annealing operation is introduced to improve the global search ability of genetic algorithm. A simulated annealing genetic algorithm is designed to solve the model. The effectiveness of the adopted algorithm is verified by numerical experiments and the results show that the regional workload balance planning method can better solve the integrated optimization of storage space allocation and multiple yard cranes scheduling.

Key words: container yard, storage space allocation, yard cranes scheduling, simulated annealing genetic algorithm

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