交通运输系统工程与信息

交通运输系统工程与信息 ›› 2011, Vol. 11 ›› Issue (3): 58-64.

• 系统工程理论与方法 • 上一篇    下一篇

基于泊位计划的集装箱码头岸桥动态调度优化

靳志宏，李娜*   

  1. 大连海事大学 交通运输管理学院，辽宁 大连 116026
  • 收稿日期:2011-04-11 修回日期:2011-04-22 出版日期:2011-06-25 发布日期:2011-07-18
  • 作者简介:靳志宏（1963-），男，辽宁沈阳人，教授，博士生导师，物流工程与管理学科主任.
  • 基金资助:

    教育部高等学校博士学科点专项科研基金(20070151002)；辽宁省自然科学基金项目(20082141)；辽宁省教育厅高校科研项目（2008S028).

Optimization of Quay Crane Dynamic Scheduling Based on Berth Schedules in Container Terminal

JIN Zhi-hong, LI Na   

  1. Transportation Management College, Dalian Maritime University, Dalian 116026, Liaoning, China
  • Received:2011-04-11 Revised:2011-04-22 Online:2011-06-25 Published:2011-07-18

被引次数

112

摘要： 所谓基于泊位计划的集装箱码头岸桥动态调度问题，是指在计划周期内，在每艘船舶均已安排了靠泊时间和靠泊位置的前提下，将有限的岸桥资源在船舶上的装卸任务间进行动态分配与排序，以最大限度地减少计划期内所有船舶的滞港时间. 在充分考虑岸桥不能交叉作业、装卸任务有作业顺序要求等现实约束下，对此问题构建了非线性数学规划模型，基于问题自身的特点设计了基于任务排序的染色体结构，用遗传算法进行求解. 通过与文献中单船的调度结果的对比、单船岸桥调度与多船动态岸桥调度结果的对比，以及多船动态岸桥调度的仿真实验，证明了模型及算法的有效性.

关键词: 系统工程, 水路运输, 岸桥分配, 动态调度, 非线性规划, 遗传算法

Abstract: The quay crane dynamic scheduling problem based on berth schedules in container terminal involves the allocation of limited quay crane resources and the scheduling of loading and unloading tasks on each ship, with its berth time and location known, in order to reduce the total time of all the ships arriving at the terminal in the planning horizon. Under the real constraints of non-crossing of quay cranes and the sequence requires among tasks, the non-linear mathematical planning model is set up. A genetic algorithm is proposed according to the characteristics of the problem, and the chromosome representation is structured on the sequence of tasks. The model and the algorithm are proved effectiveness via the comparisons of scheduling results with literature on a single ship, the comparisons of scheduling results between multi-ships and single ship, and the simulation of quay crane dynamic scheduling among multi-ships.

Key words: system engineering, waterway transportation, quay crane allocation, dynamic scheduling, non-linear planning, genetic algorithm

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