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摘要: 按照不同的研究区域, 总结了集成调度在码头前沿、码头堆场以及码头前沿和堆场间等方面的国内外文献, 并提出了未来的研究方向。研究结果表明: 多数文献研究的是传统集装箱码头的集成调度问题, 并取得了较多成果, 也有少数文献研究了自动化码头的集成调度问题, 随着科技的发展和自动化码头的增多, 在今后的研究中, 应结合自动化码头的特点, 研究自动化码头内的设备集成调度问题; 多数文献都是在静态或确定情况下研究集装箱码头的集成调度问题, 只有极少数文献考虑了码头生产调度过程的动态或不确定性, 因此, 随着研究的逐步深入, 动态或不确定因素下的集成调度问题将成为未来的研究重点; 多数文献均建立了以时间、成本最小或二者相结合的目标函数, 虽然也有助于提高码头的生产效率, 但是依旧存在一定的局限性, 随着能源问题的日益严重, 在未来的研究中, 应建立均衡能耗和其他优化目标的多目标模型; 多数文献中设计了启发式算法, 并获得了较好的优化解, 为了使求解方法更加多元化, 应将算法计算与仿真验算相结合; 随着多式联运的兴起, 应在考虑铁路作业区对整个集装箱码头影响的基础上研究铁路作业区与码头间的集成调度问题。Abstract: According to different research areas, the domestic and foreign literatures on the integrated scheduling in the quayside area, container yard and between quayside area and container yard were summarized. The future research direction was proposed. Analysis result shows that the most literatures study the integrated scheduling problem at the traditional container terminals, and make rich achievements. Only a few literatures focus on the integrated scheduling problem at the automatic container terminals. With the development of technology and the increase at the automatic container terminals, the integrated scheduling problem at the automatic container terminals should be studied by combining the characteristics of automatic container terminals in the future. Most literatures concentrate on the integrated scheduling problems of container terminals under the static or certain conditions. Only a few literatures focus on the integrated scheduling problem of container terminals under the dynamic or uncertain conditions, Therefore, with the research deepen progressively, the integrated scheduling problem under the dynamic or uncertain factors will be the focus of future research. The objective functions are developed based on the time, cost or the combination of time and cost in most literatures. Although these can enhance the efficiency of container terminals, but still have some limitations. As the energy problems get increasingly serious, a multi-objective model for establishing the balanced energy consumption and other optimization objectives should be put forward. Most literatures propose the heuristic algorithms to solve the problem and get better optimal solutions. To increase the diversification of solution methods, a solution strategy combining algorithm computation with simulation checking should be proposed. With the rapid development of intermodal transportation, the influence of railway operation area on the container terminal should be considered, and the integrated scheduling between the container terminal and railway operation area should be studied.
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Key words:
- waterway transportation /
- integrated scheduling /
- modeling /
- algorithm design /
- container terminal
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表 1 集成调度在码头前沿的研究汇总
Table 1. Research summary on integrated scheduling in quayside
作者(年份) 研究内容 研究目标 模型类型 算法类型 试验规模 Imai等(2008) 泊位和岸桥分配的集成调度问题 减少总服务时间 集成调度模型 遗传算法 小规模 Liang等(2009) 泊位和岸桥调度的集成调度问题 减少每艘船的装卸时间、等待时间以及延误时间 集成调度模型 混合进化算法 大规模(684个集装箱) Tavakkoli-Moghaddam等(2009) 岸桥分配和岸桥调度的集成调度问题 减少装卸船舶总费用 混合整数规划模型 遗传算法 小规模(16个工作任务) Meisel等(2009) 泊位和岸桥分配的集成调度问题 减少船舶的总服务费用 优化模型 结构启发式算法、局部优化算法、2个启发式算法 大规模(40艘船) Han等(2010) 泊位调度和岸桥调度的集成调度问题 减少期望值与船舶的总服务时间的标准差和延误时间加权值之和 非线性混合整数规划模型 基于仿真的遗传算法 未提及 Chang等(2010) 泊位和岸桥分配的集成调度问题 减少实际和最好的停泊位置水平之间的总偏差、船舶停泊和离开时间延误的总惩罚费用以及岸桥的总能耗 动态分配模型 混合并行遗传算法 大规模(4 490个集装箱) Zhang等(2010) 泊位和岸桥分配的集成调度问题 减少集装箱装卸成本的加权和 混合整数规划模型 子梯度优化算法 大规模(3 052个集装箱) 曾庆成等(2010) 泊位和岸桥调度的集成调度问题 减少船舶未停靠在最佳位置而引起的额外成本以及装卸作业成本 干扰管理模型 仿真优化算法 大规模(实际港口数据) Raa等(2011) 泊位和岸桥分配的集成调度问题 减少船舶装卸时间、船舶停泊位置以及分配给船舶的岸桥数变化的惩罚 混合整数规划模型 混合启发式求解程序 大规模(实际港口数据) Elwany等(2013) 泊位和岸桥分配的集成调度问题 减少服务质量成本和运营成本 集成调度模型 模拟退火算法 大规模(40艘船) Fu等(2014) 岸桥分配和岸桥调度的集成调度问题 减少总完成时间 集成调度模型 遗传算法、拉格朗日松弛算法 大规模(100艘船) Türko■ullari等(2014) 泊位和岸桥分配的集成调度问题 减少总费用 二进制整数线性规划模型 后处理算法 大规模(60艘船) Diabat等(2014) 岸桥分配和岸桥调度的集成调度问题 减少船舶卸载过程的总完成时间 混合整数规划模型 遗传算法 大规模(7 175个集装箱) Theodorou等(2014) 岸桥分配和岸桥调度的集成调度问题 减少总完成时间 拉格朗日松弛算法 大规模(6 450个集装箱) 
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表 2 集成调度在码头堆场的研究汇总
Table 2. Research summary on integrated scheduling in container yard
作者(年份) 研究内容 研究目标 模型类型 算法类型 试验规模 是否同时考虑装载和卸载过程 Kozan等(2006) 堆场分配和场吊调度的集成调度问题 减少船舶的停泊时间 集成调度模型 禁忌搜索与遗传算法的混合算法 大规模(500个集装箱) 未涉及 Cao等(2008) 集卡调度与堆场分配的集成调度问题 减少卸载过程总完成时间 整数规划模型 遗传算法与贪婪算法的混合算法 大规模(100个集装箱) 否 Lee等(2009) 集卡调度与堆场分配的集成调度问题 减少集装箱从起点到终点移动中的总延误时间及集卡总走行时间的加权和 整数规划模型 混合插入算法, 遗传算法和贪婪启发式算法 大规模(100个集装箱) 否 Cao等(2010) 场吊调度与集卡调度的集成调度问题 减少总完成时间 集成调度模型 通用Benders切割方法和组合Benders切割方法 大规模(500个工作任务) 否 Luo等(2015) 集卡调度与堆场分配的集成调度问题 减少船舶的停泊时间 混合整数规划模型 遗传算法 大规模(100个集装箱) 是 Luo等(2016) 集卡调度与堆场位置分配的集成调度问题 减少船舶的停泊时间 混合整数规划模型 遗传算法 大规模(200个集装箱) 否 Niu等(2016) 集卡调度与堆场分配的集成问题 减少集装箱的总延误时间 集成调度模型 粒子群算法和细菌菌落优化算法 大规模(100个集装箱) 未提及 鲁渤等(2017) 自动升降车与堆场位置分配的集成调度问题 减少作业的总完成时间 集成优化模型 遗传算法 大规模(200个集装箱) 是
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表 3 两种设备间的集成调度研究汇总
Table 3. Research summary on integrated scheduling between two equipments
作者(年份) 研究内容 研究目标 模型类型 算法类型 试验规模 是否同时考虑装载和卸载过程 Lee等(2011) 泊位分配和场吊调度的集成调度问题 减少集装箱码头的总成本 混合整数规划模型 模拟退火启发式算法 中等规模(30个工作任务) 是 Tang等(2014) 岸桥和集卡间的集成调度问题 减少总完成时间 改进的粒子群算法 大规模(300个集装箱) 是 Assadipour等(2014) 带有时间窗的岸桥和场吊间集成调度问题 减少总完成时间 混合整数规划模型 改进的遗传算法 大规模(375个集装箱) 是 Shu等(2015) 岸桥与自动堆垛机间的集成调度问题 减少倒箱操作次数和岸桥前后移动的频率 多目标数学模型 基于普通遗传算法的双种群遗传算法 否 Kaveshgar等(2015) 岸桥和集卡间的集成调度问题 减少总完成时间 混合整数规划模型 遗传算法与贪婪算法相结合的算法 中等规模(30个工作任务) 是
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表 4 三种设备间的集成调度研究汇总
Table 4. Research summary on integrated scheduling of three equipments
作者(年份) 研究内容 研究目标 模型类型 算法类型 试验规模 是否同时考虑装载和卸载过程 Chen等(2006) 岸桥、集卡和场吊间的集成调度问题 减少总完成时间 混合整数规划模型 禁忌搜索算法 大规模(100个集装箱) 是 Chen等(2007) 岸桥、集卡和场吊间的集成调度问题 减少总完成时间 集成模型 禁忌搜索算法 是 Lau等(2008) 岸桥、自动引导车、自动堆垛机间的集成调度问题 减少自动引导车的总走行时间、岸桥操作的延误时间以及自动堆垛机的总走行时间 混合整数规划模型 多层遗传算法和遗传算法与最大匹配算法相结合的混合算法 中等规模(64个集装箱) 是 Zeng等(2009) 岸桥、集卡和场吊间的集成调度问题 减少装载过程的总完成时间 基于神经网络的代理模型 仿真优化方法 大规模(500个集装箱) 否 Xue等(2013) 岸桥调度、集卡调度以及场区位置分配间的集成调度问题 减少岸桥总完成时间和集卡走行时间的加权和 混合整数规划模型 两阶段启发式算法 大规模(200个集装箱) 是 Chen等(2013) 岸桥、集卡和场吊间的集成调度问题 减少总完成时间 约束规划模型 三阶段算法 大规模(500个集装箱) 是 Lu等(2014) 岸桥、集卡和场吊间的集成调度问题 减少场吊、岸桥和集卡的操作时间 集成调度优化模型 粒子群算法 中等规模(30个工作任务) 否 Homayouni等(2014) 岸桥、自动引导车和存储平台间的集成调度问题 减少自动引导车和存储平台间的总走行时间与岸桥的总延误时间 混合整数规划模型 遗传算法 中等规模(60个工作任务) 是 Homayouni等(2015) 岸桥、自动引导车和存储平台间的集成调度问题 减少自动引导车和存储平台间的总走行时间与岸桥的总延误时间 多目标混合整数规划模型 模拟退火算法 中等规模 是 He等(2015) 与能耗有关的岸桥、集卡和场吊间的集成调度问题 减少船舶的总离开延误时间和所有任务的总运输能耗 混合整数规划模型 遗传算法和粒子群算法结合的混合算法 大规模(500个工作任务) 是
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