运筹与管理 ›› 2022, Vol. 31 ›› Issue (3): 9-16.DOI: 10.12005/orms.2022.0071

• 理论分析与方法探讨 • 上一篇    下一篇

汽车滚装码头泊位调度优化模型与算法研究

张帝1, 梅子翘2, 陈峰3, 赵一飞3   

  1. 1.上海交通大学 机械与动力工程学院,上海 200240;
    2.上海交通大学 安泰经济与管理学院,上海 200030;
    3.上海交通大学 中美物流研究院,上海 200030
  • 收稿日期:2020-03-16 出版日期:2022-03-25 发布日期:2022-04-12
  • 通讯作者: 陈峰(1971-),男,江苏人,副教授,研究方向:物流与算法。
  • 作者简介:张帝(1995-),男,山西人,硕士,研究方向:算法设计与分析;梅子翘(1997-),女,湖北人,博士,研究方向:算法设计与分析。
  • 基金资助:
    国家自然科学基金资助项目(71672115)

Research of Berth Scheduling Optimization Model and Algorithm on Automotive RO-RO Terminals

ZHANG Di1, MEI Zi-qiao2, CHEN Feng3, ZHAO Yi-fei3   

  1. 1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;
    2. Antai College of Economics & Management, Shanghai Jiao Tong University, Shanghai 200030, China;
    3. Sino-US Global Logistics Institute, Shanghai Jiao Tong University, Shanghai 200030, China
  • Received:2020-03-16 Online:2022-03-25 Published:2022-04-12

摘要: 本文研究滚装码头混合泊位分配和劳动力分配的联合调度优化问题。首先,考虑潮汐时间窗约束、装卸劳动力约束、泊位缆桩分布约束以及泊位不规则布局因素,建立以最小化船舶总服务时间为目标的混合整数规划模型。其次,采用内外嵌套算法设计策略,提出求解该类问题的组合算法。其中,外层是多种群并行进化的遗传算法,生成多种船舶计划顺序,内层为基于规则的启发式算法,用于计算给定计划顺序的目标函数值。然后,基于实际运营数据,生成多组不同规模的算例进行全面数值实验,结果表明所提出的算法可在10分钟内求解包含50艘船、100个泊段的算例。最后,开展基于真实滚装码头运营实例的案例分析,对所提模型和算法在实际码头调度问题中的适用性与高效性进行验证。

关键词: 汽车物流, 滚装码头, 优化模型, 遗传算法, 启发式算法

Abstract: This paper studies anunprece dented joint scheduling problem of mixed berth allocation and manpower assignment in RO-RO terminals. Firstly, a mixed integer programming model is established with the objective of minimizing the total servicing time of vessels, which considers tidal time window constraints, handling manpower constraints, berth bollards distribution constraints, and irregular berth layout factors. Then, a combinational algorithm is proposed by adopting an interactive strategy, which contains a multi-group parallel evolutionary genetic algorithm that generates plan sequences and a rule-based heuristic algorithm that obtains individual fitness values for given sequences. Furthermore, based on the real operation data, several sets of numerical experiments are generated and the results show that the genetic algorithm can solve an example which contains 50 vessels and 100 berth sections within 10 minutes. Finally, real RO-RO terminal operation cases are carried out to verify the applicability and efficiency of the proposed model and algorithm in the actual scheduling problem.

Key words: automotive logistics, RO-RO terminals, optimization model, genetic algorithm, heuristic algorithm

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