系统仿真学报 ›› 2021, Vol. 33 ›› Issue (7): 1689-1698.doi: 10.16182/j.issn1004731x.joss.20-0161

• 国民经济仿真 • 上一篇    下一篇

集装箱码头船舶多贝位配载与堆场取箱协同优化研究

刘志雄1, 钱翰文2, 颜家岚2   

  1. 1.武汉科技大学 机械自动化学院,湖北 武汉 430081;
    2.武汉科技大学 汽车与交通工程学院,湖北 武汉 430065
  • 收稿日期:2020-04-04 修回日期:2020-07-24 出版日期:2021-07-18 发布日期:2021-07-20
  • 作者简介:刘志雄(1975-),男,博士,教授,研究方向为系统仿真与优化。E-mail:lzx_brad@126.com
  • 基金资助:
    国家自然科学基金(71372202)

Collaborative Optimization of Ship Stowage Plan and Yard Container Unloading for Container Terminal

Liu Zhixiong1, Qian Hanwen2, Yan Jialan2   

  1. 1. School of Mechanical Automation, Wuhan University of Science and Technology, Wuhan 430081, China;
    2. School of Automotive and Traffic Engineering, Wuhan University of Science and Technology, Wuhan 430065, China
  • Received:2020-04-04 Revised:2020-07-24 Online:2021-07-18 Published:2021-07-20

摘要: 船舶配载是集装箱码头生产作业的重要内容,直接关系着集装箱船舶的装卸作业效率和航行安全性。以最小化堆场取箱翻箱次数与船舶贝位翻箱次数之和为目标,考虑船舶贝位横倾稳定性和重不压轻等约束条件,针对多目的港船舶多贝位配载与堆场取箱顺序协同优化问题进行建模分析。采用与局部搜索相结合的混合演化策略算法对船舶配载问题进行优化计算。针对船舶多贝位配载,提出一种面向船舶贝位分配的启发式规则,用于算法中个体编码初始化阶段的船舶贝位分配。设计了一种三维个体编码,提出了基于堆场取箱顺序生成、船舶贝位装箱规则和贝位列装箱方法相结合的解码方法。通过不同规模算例说明,相比粒子群算法和启发式算法,采用启发式规则的混合演化策略算法具有较好的优化性能。

关键词: 集装箱码头, 船舶配载, 堆场取箱, 演化策略算法, 启发式规则

Abstract: Stowage plan is an important component of the operation production for the container terminal, which directly affects the handling efficiency and shipping safety. The aim is minimizing the total reloading numbers of both the yard and the ship bay, in view of the constraint of ship heeling moment and loading for light upon heavy, the model is employed for collaborative optimization of stowage plan and yard unloading for multiple destination ports and multiple bays. The hybrid evolutionary strategy algorithm is adopted with the local search algorithm. As to ship stowage play for multiple bays, a heuristic rule for the ship bay assignment is presented to assign the ship bay in the initialization phase of the individual coding for the algorithm. A three-dimension individual coding is designed and the encoding method is employed based on the yard unloading generation combining with the ship bay loading rule and the bay column load method. The computational results of different size examples show that the hybrid evolutionary strategy algorithm based on the heuristic rule has better optimization performance than particle swarm optimization algorithm and heuristic algorithm.

Key words: container terminal, stowage plan, yard container unloading, evolutionary strategy algorithm, heuristic rule

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