交通运输系统工程与信息

交通运输系统工程与信息 ›› 2019, Vol. 19 ›› Issue (6): 206-214.

• 系统工程理论与方法 • 上一篇    下一篇

集卡提箱预约配额与场桥配置的联合优化

李娜1，边展2，徐奇1，靳志宏*1   

  1. 1. 大连海事大学交通运输工程学院，辽宁大连 116026；2. 首都经济贸易大学工商管理学院，北京 100070
  • 收稿日期:2019-05-24 修回日期:2019-08-08 出版日期:2019-12-25 发布日期:2019-12-25
  • 作者简介:李娜(1981-)，女，辽宁丹东人，讲师.
  • 基金资助:

    国家自然科学基金/ National Natural Science Foundation of China(71702019)；教育部人文社科基金/ Humanities and Social Sciences Fund of Chinese Ministry of Education(14YJC630064)；中央高校基本科研业务费专项资金/Fundamental Research Funds for the Central Universities of Education of China(3132019160).

Optimization of Quotas and Yard Crane Allocation in Pick-up Truck Appointment

LI Na1, BIAN Zhan2, XU Qi1, JIN Zhi-hong1   

  1. 1. College of Transportation Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China; 2. College of Business Administration, Capital University of Economics and Business, Beijing 100070, China
  • Received:2019-05-24 Revised:2019-08-08 Online:2019-12-25 Published:2019-12-25

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摘要：

在集卡提箱预约中，集装箱码头场桥配置和预约配额的联合优化是缩短集卡等待时间，提升资源利用率的关键. 针对提箱作业的实际特点，构建了双目标整数规划模型，设计了基于非支配排序遗传算法的求解方法. 当码头需求较少时，采取动态调整随机分配场桥最佳；当码头需求较高时，采取动态调整与按需配置相组合的方式最佳. 以国内某码头为原型，通过不同规模的算例实验，验证了模型和算法的有效性. 所提方法可快速生成高质量、多样化的前沿解，实现了场桥资源与集卡作业需求的匹配优化，为码头管理者在资源投入与集卡等待时间的权衡中，提供了决策支持.

关键词: 水路运输, 集卡提箱预约, 场桥配置, 多目标优化, 非支配排序遗传算法

Abstract:

In pick- up truck appointment at container terminal, combined optimization of yard crane allocation and quotas is the key to reduce truck waiting time and improve the efficiency of resource utility. Under the real features of pick-up operation, a bi-objective integer programming model is set up. Based on non-dominated sorting genetic algorithm, a fast and efficient algorithm is developed. When the requirement of terminal is lower, dynamic random should be adopted to allocate yard cranes. If that is higher, combination of dynamic ratio and minimum allocation should be adopted. Numerical experiments of different sizes are proposed based a container terminal in China. The experiments prove the effective of the model and the algorithm, which could generate high-quality and multiple pareto front solutions. The optimized matching of yard crane resource and operation requirement from trucks is achieved. It provides decision support for terminal operators in balancing resource input and truck waiting time.

Key words: waterway transportation, pick-up truck appointment, yard crane allocation, multi-objective optimization, non-dominated sorting genetic algorithm

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