计算机集成制造系统 ›› 2017, Vol. 23 ›› Issue (第4期): 892-902.DOI: 10.13196/j.cims.2017.04.025

• 产品创新开发技术 • 上一篇    下一篇

集装箱港口集卡—轮胎吊集成调度方法

丁一1,杨阳1,沙梅2+,林国龙1   

  1. 1.上海海事大学物流研究中心
    2.上海海事大学交通运输学院
  • 出版日期:2017-04-30 发布日期:2017-04-30
  • 基金资助:
    国家自然科学基金资助项目(71301101)。

Integrated methodology for scheduling of yard cranes and internal trucks in container terminal

  • Online:2017-04-30 Published:2017-04-30
  • Supported by:
    Project supported by the National Natural Science Foundation,China(No.71301101).

摘要: 为了更好地协同调度集卡和轮胎吊,以桥吊调度计划为依据,给定轮胎吊和集卡关联任务集与作业要求时间,研究了轮胎吊—集卡的调度问题特性,以最小化完成任务的累计延误时间为目标,分别建立集装箱码头轮胎吊和集卡的调度问题整数规划模型。考虑到两者之间的并发耦合关系,通过建立分布式到达时间控制方法模型,并融合离散粒子群优化算法对集卡—轮胎吊集成调度问题进行求解。在算例分析中,通过与CPLEX计算结果进行比较,验证了分布式到达时间控制方法及离散粒子群优化算法对求解集卡—轮胎吊集成调度的收敛特性和可靠性。结果表明,不同时间阶段按当前配置的集卡—轮胎吊资源进行作业,均有从85~215 min不同程度的累计作业延误时间,这将导致桥吊的效率损失,进而延长船舶作业时间,影响船舶实际靠离泊时间。

关键词: 集卡调度, 轮胎吊调度, 耦合模型, 离散粒子群算法, 分布式到达时间控制

Abstract: For better integrated scheduling of yard cranes and internal trucks,based on Quay Crane Schedule Plan (QCSP),the characteristics of Rubber Tyred Gantry (RTG) and truck scheduling problems were researched with given RTG,internal truck assignment-task set and required operational time.By taking the minimum cumulative delay time of all complete assignment-tasks as an objective,the integer programming models for yard cranes and internal trucks in container terminal were formulated separately.A Distributed Arrival Time Control (DATC) model by integrating with Binary Particle Swarm Optimization algorithm (BPSO) was proposed for their concurrent coupling relationship.The case study results by comparing with CPLEX indicated that DATC with BPSO was effective for finding high-quality solutions and could efficiently solve the large problems.Results showed that there were different degrees of cumulative work delay within given truck and RTG resources for each period,ranging from 85 to 215 minutes,which led to Quay Crane (QC) efficiency loss and vessel's estimated berthing time was further affected.

Key words: internal trucks scheduling, yard cranes scheduling, coupling model, binary particle swarm optimization, distributed arrival time control

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