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考虑能耗节约的集装箱码头双小车岸桥与AGV联合配置及调度优化

范厚明 郭振峰 岳丽君 马梦知

范厚明, 郭振峰, 岳丽君, 马梦知. 考虑能耗节约的集装箱码头双小车岸桥与AGV联合配置及调度优化. 自动化学报, 2021, 47(10): 2412−2426 doi: 10.16383/j.aas.c190626
引用本文: 范厚明, 郭振峰, 岳丽君, 马梦知. 考虑能耗节约的集装箱码头双小车岸桥与AGV联合配置及调度优化. 自动化学报, 2021, 47(10): 2412−2426 doi: 10.16383/j.aas.c190626
Fan Hou-Ming, Guo Zhen-Feng, Yue Li-Jun, Ma Meng-Zhi. Joint configuration and scheduling optimization of dual-trolley quay crane and AGV for container terminal with considering energy saving. Acta Automatica Sinica, 2021, 47(10): 2412−2426 doi: 10.16383/j.aas.c190626
Citation: Fan Hou-Ming, Guo Zhen-Feng, Yue Li-Jun, Ma Meng-Zhi. Joint configuration and scheduling optimization of dual-trolley quay crane and AGV for container terminal with considering energy saving. Acta Automatica Sinica, 2021, 47(10): 2412−2426 doi: 10.16383/j.aas.c190626

考虑能耗节约的集装箱码头双小车岸桥与AGV联合配置及调度优化

doi: 10.16383/j.aas.c190626
基金项目: 国家自然科学基金(61473053), 大连市科技创新基金项目(2020JJ26GX033)资助
详细信息
    作者简介:

    范厚明:大连海事大学交通运输工程学院教授, 博士. 主要研究方向为交通运输系统规划与设计, 战略管理与系统规划. 本文通信作者.E-mail: fhm468@163.com

    郭振峰:大连海事大学交通运输工程学院博士研究生. 主要研究方向为交通运输规划与管理.E-mail: guozhenfeng_dl@126.com

    岳丽君:大连海事大学交通运输工程学院博士研究生. 主要研究方向为交通运输规划与管理.E-mail: yuelj11@163.com

    马梦知:大连海事大学交通运输工程学院讲师, 博士. 主要研究方向为交通运输系统规划与设计, 战略管理与系统规划.E-mail: mengzhi1440@126.com

Joint Configuration and Scheduling Optimization of Dual-trolley Quay Crane and AGV for Container Terminal with Considering Energy Saving

Funds: National Natural Science Foundation of China (61473053), the Science and Technology Innovation Foundation of Dalian, China (2020JJ26GX033)
More Information
    Author Bio:

    FAN Hou-Ming Professor at the College of Transportation Engineering, Dalian Maritime University. His research interest covers transportation system planning and design, strategic management and system planning. Corresponding author of this paper

    GOU Zhen-Feng Ph.D. candidate at the College of Transportation Engineering, Dalian Maritime University. His research interest covers transportation planning and management

    YUE Li-Jun Ph. D. candidate at the College of Transportation Engineering, Dalian Maritime University. Her main research interest is transportation planning and management

    MA Meng-Zhi Lecturer at the College of Transportation Engineering, Dalian Maritime University. Her research interest covers transportation system planning and design, strategic management and system planning

  • 摘要: 合理调度集装箱码头的装卸设备以减少生产过程中的能耗, 对实现其低碳绿色化发展具有重要意义. 针对集装箱码头向自动化发展过程中的双小车岸桥与AGV (Automated guided vehicle)联合配置及调度问题, 考虑AGV续航时间、双小车岸桥中转平台容量和堆场缓冲支架容量约束, 以岸桥的能耗最小为第一阶段模型的优化目标, 以AGV运输过程的能耗最小为第二阶段目标建立两阶段优化模型; 设计枚举法求解第一阶段模型, 改进遗传算法求解第二阶段优化模型. 以洋山四期自动化集装箱码头为例进行实验分析, 针对不同船舶在港总装卸时间和AGV配置原则进行实验, 验证了模型和算法的有效性, 结果表明以最小化能耗为目标的双小车岸桥与AGV联合调度可在岸桥主小车不延误的前提下, 显著减少AGV的配置数量.
  • 图  1  双小车岸桥示意图

    Fig.  1  The dual-trolley quay crane

    图  2  码头布局和AGV运输流程示意图

    Fig.  2  Automated container terminal layout and AGV transportation process

    图  3  待装卸集装箱分布示意图

    Fig.  3  Containers to be loaded and unloaded

    图  4  岸桥任务编号示意图

    Fig.  4  Task number of quay crane

    图  5  染色体示意图

    Fig.  5  The chromosome

    图  6  目标函数求解流程图

    Fig.  6  Solution flowchart

    图  7  交叉和变异

    Fig.  7  Crossover and variation

    图  8  待装卸集装箱船

    Fig.  8  The ship of containers to be loaded and unloaded

    图  9  岸桥作业路线图

    Fig.  9  Path to the dual-trolley quay cranes

    图  10  不同AGV配置数量下各实验的AGV利用率

    Fig.  10  AGV utilization rate of each experiment under different configurations

    表  1  设备参数取值

    Table  1  Equipment parameter value

    参数取值
    ${\tau _1}\;/\min$1
    ${\tau _2}\;/\min$2
    ${\tau _3}\;/\min$1
    ${\tau _4}\;/\min$3
    ${\tau _5}\;/\min$5
    ${v_1}\;/({\rm m} \cdot {\min ^{ - 1} })$210
    ${v_0}\;/({\rm m} \cdot {\min ^{ - 1} })$350
    ${C_1}\;/\left( { {\rm kW} \cdot {\rm h} \cdot { {\left( { {\rm h} \cdot{\text{台} } } \right)}^{ - 1} } } \right)$91.24
    ${C_2}\;/\left( { {\rm kW} \cdot {\rm h} \cdot { {\left( { {\rm h} \cdot{\text{台} } } \right)}^{ - 1} } } \right)$70.18
    ${C_3}\;/\left( { {\rm kW} \cdot {\rm h} \cdot { {\left( { {\rm h} \cdot{\text{台} } } \right)}^{ - 1} } } \right)$49.6
    ${C_4}\;/\left( { {\rm kW} \cdot {\rm h} \cdot { {\left( { {\rm h} \cdot{\text{台} } } \right)}^{ - 1} } } \right)$49.6
    ${C_5}\;/\left( { {\rm kW} \cdot {\rm h} \cdot { {\left( { {\rm h} \cdot{\text{辆} } } \right)}^{ - 1} } } \right)$21
    ${C_6}\;/\left( { {\rm kW} \cdot {\rm h} \cdot { {\left( { {\rm h} \cdot{\text{辆} } } \right)}^{ - 1} } } \right)$14
    ${C_7}\;/\left( { {\rm kW} \cdot {\rm h} \cdot { {\left( { {\rm h} \cdot{\text{辆} } } \right)}^{ - 1} } } \right)$9
    下载: 导出CSV

    表  2  岸桥调度方案比较

    Table  2  Comparison of dual-trolley quay crane scheduling schemes

    $k$${T_{Ik}}$${F_1}$调度方案开始-完工时刻
    24 70911 507QC1B1-B6卸船作业0 ~ 917 min
    B1-B18装船作业924 ~ 3 789 min
    QC2B7-B20卸船作业0 ~ 3 787 min
    B19-B20装船作业3 789 ~ 3 889 min
    32 60511 504QC1B1-B3卸船作业0 ~ 640 min
    B1-B13装船作业643 ~ 2 605 min
    QC2B3-B10卸船作业0 ~ 1 740 min
    B14-B18装船作业1 743 ~ 2 546 min
    QC3B11-B20卸船作业0 ~ 2 323 min
    B19-B20装船作业2 324 ~ 2 429 min
    42 63611 484QC1B1-B3卸船作业0 ~ 640 min
    B1-B9装船作业644 ~ 1 970 min
    QC2B4-B5卸船作业0 ~ 93 min
    B10-B11装船作业98 ~ 395 min
    QC3B6-B10卸船作业0 ~ 1 646 min
    B12-B16装船作业1 648 ~ 2 636 min
    QC4B11-B20卸船作业0 ~ 2 325 min
    B17-B20装船作业2 331 ~ 2 468 min
    下载: 导出CSV

    表  3  AGV调度结果

    Table  3  The scheduling results of AGV

    AGV集装箱作业序列
    11→7→11→16→22→25→36→47→54→59→63→70→78→83→86→89→96→107→113→131→···→
    3530→3537→3545→3548→3555→3562→3567→3572→3575→3580→3585→3588
    22→5→8→13→17→18→21→27→31→37→48→58→61→67→74→80→82→90→94→98→115→···→
    3336→3344→3348→3354→3355→3361→3367→3377→3382→3393→3398
    33→4→9→14→20→23→29→32→40→44→46→49→56→62→72→85→87→92→102→114→···→
    3747→3750→3751→3753→3754→3755→3757→3758→3760→3761→3762→3766→3767
    46→10→12→24→34→38→42→51→60→64→69→73→76→84→88→93→99→100→105→110→···→
    3653→3655→3658→3661→3664→3665→3668→3674→3677→3680→3687→3691→3697
    515→19→26→33→43→75→95→109→112→118→122→125→130→141→146→150→154→162→···→
    3538→3543→3551→3565→3569→3574→3578→3584→3592→3598→3605→3610→3614
    628→35→39→45→50→53→55→65→71→79→91→97→101→104→108→120→123→133→136→···→
    3742→3744→3746→3748→3749→3752→3756→3759→3763→3764→3765→3768→3769
    730→41→52→57→66→68→77→81→103→106→111→117→121→138→142→167→170→185→···→
    3702→3703→3707→3709→3710→3712→3716→3718→3721→3727→3730→3733→3738
    下载: 导出CSV

    表  4  平均计算结果与下界的比较

    Table  4  Comparison of average calculation results with lower boundary

    实验$N$${T_{{q_{\max }}}}$${f_1}$${A_1}$${f_2}$${A_2}$${f_2}^*$$\underline {{f_2}} $$GA{P_1}$$GA{P_2}$
    1240187763.96197.736333.55190.133.85 %43.00 %
    24082901 287.76339.076612.01335.980.91 %45.10 %
    38295752 558.76706.8161 160.60671.085.05 %42.18 %
    41 1297903 467.661 005.1161 749.47861.6514.27 %50.75 %
    51 5051 1024 613.561 379.9462 114.891 190.3413.74 %43.72 %
    61 9761 3916 045.962 323.1472 996.721 927.8817.01 %35.67 %
    72 4191 6427 394.472 492.0783 634.972 191.9112.04 %39.70 %
    82 6491 7928 091.682 789.6283 941.742 571.837.81 %34.75 %
    下载: 导出CSV

    表  5  不同船舶在港总装卸时间和不同AGV配置原则下调度结果比较

    Table  5  The results of different allowable laytime and different AGV configuration principles

    实验${t_f}/{\rm h}$${T_{ {q_{\max } } } }/{\rm h}$K/台${f_1}/{\rm kW} \cdot {\rm h}$$\min ({f_1} + {f_2})/{\rm kW} \cdot {\rm h}$AGV配置原则一AGV配置原则二AGV配置原则三
    V/辆${f_2}/{\rm kW} \cdot {\rm h}$V/辆${f_2}/{\rm kW} \cdot {\rm h}$V/辆${f_2}/{\rm kW} \cdot {\rm h}$
    94846.46311 499.115 374.673 875.564 417.494 198.1
    104443.28311 505.315 362.373 857.064 030.494 168.6
    114037.9411 482.715 308.1103 825.493 847.2123 977.6
    123635.23411 483.815 136.893 653.073 659.6123 806.2
    133232411 487.315 097.593 610.293 610.2123 879.0
    下载: 导出CSV

    表  6  考虑随机因素影响的实验设计

    Table  6  Experimental design considering the influence of random factors

    实验实验内容
    14完全确定性系统. 在实验10中岸桥调度方案的基础上, ${\tau _2}$、${\tau _5}$、${v_0}$和${v_1}$均设为定值
    15在实验14的基础上, 每个集装箱的岸桥主小车作业时间设定为服从均值为${\tau _2}$的泊松分布
    16在实验15的基础上, AGV往返充电站时间设定为服从均值为${\tau _5}$负指数分布的类型
    17在实验16的基础上, AGV的空载速度、重载速度分别设定为服从均值为${v_0}$和${v_1}$的正态分布
    下载: 导出CSV

    表  7  考虑随机因素影响的实验结果

    Table  7  Experimental results considering the influence of random factors

    $A$实验14 实验15 实验16 实验17
    $Del$$\rho $$f'_2$$Del$$\rho $$f'_2$$Del$$\rho $$f'_2$$Del$$\rho $$f'_2$
    7055.56 %3 857.00 2.3033.80 %6 393.5349.9834.87 %6 196.18 128.0835.55 %6 076.48
    8051.42 %4 171.032.5231.97 %6 766.147.8132.51 %6 629.521.0334.00 %6 355.70
    9051.52 %4 168.56030.32 %7 125.263.0933.71 %6 407.801.8731.34 %6 897.39
    10050.79 %4 049.60031.63 %6 817.67032.64 %6 604.884.2531.17 %6 919.12
    11050.42 %4 122.80030.53 %7 069.71030.10 %7 164.511.2629.55 %7 325.53
    下载: 导出CSV
  • [1] 丁进良, 杨翠娥, 陈远东, 柴天佑. 复 杂工业过程智能优化决策系统的现状与展望. 自 动化学报, 2018, 44(11): 1931-1943

    Ding Jin-Liang, Yang Cui-E, Chen Yuan-Dong, Chai Tian-You. Research progress and prospects of intelligent optimization decision making in complex industrial process. Acta Automatica Sinica, 2018, 44(11): 1931-1943
    [2] Sim J. A carbon emission evaluation model for a container terminal. Journal of Cleaner Production, 2018, 186(10): 526-533
    [3] 郑松, 吴晓林, 王飞跃, 林东东, 郑蓉, 柯伟林, 池新栋, 陈德旺. 平 行系统方法在自动化集装箱码头中的应用研究. 自 动化学报, 2019, 45(3): 490-504

    Zheng Song, Wu Xiao-Lin, Wang Fei-Yue, Lin Dong-Dong, Zheng Rong, Ke Wei-Lin, Chi Xin-Dong, Chen De-Wang. Applying the parallel systems approach to automatic container terminal. Acta Automatica Sinica, 2019, 45(3): 490-504
    [4] He J, Huang Y, Yan W. Yard crane scheduling in a container terminal for the trade-off between efficiency and energy consumption. Advanced Engineering Informatics, 2015, 29(1): 59-75 doi: 10.1016/j.aei.2014.09.003
    [5] Chang D, He J, Bian Z. An investigation into berth and quay crane scheduling for container terminals based on knowledge. In: Proceedings of International Conference on Future Information Technology and Management Engineering. Changzhou, China: IEEE, 2010. 63−66
    [6] He, J. Berth allocation and quay crane assignment in a container terminal for the trade-off between time-saving and energy-saving. Advanced Engineering Informatics, 2016, 30(3): 390-405 doi: 10.1016/j.aei.2016.04.006
    [7] Chang, D, Fang T, Fan Y. Dynamic rolling strategy for multi-vessel quay crane scheduling. Advanced Engineering Informatics, 2017, 34(10): 60-69
    [8] Zhang, Z, Zhang Z, Liu M, Lee C Y, Wang J. The quay crane scheduling problem with stability constraints. IEEE Transactions on Automation Science and Engineering, 2018, 15(3): 1399-1412 doi: 10.1109/TASE.2018.2795254
    [9] Liu D, Ge Y E. Modeling assignment of quay cranes using queueing theory for minimizing CO2 emission at a container terminal. Transportation Research Part D: Transport and Environment, 2018, 61(6): 140-151
    [10] Liang C, Fan L, Xu D, Ding Y, Gen M. Research on coupling scheduling of quay crane dispatch and configuration in the container terminal. Computers & Industrial Engineering, 2018, 125(11): 649-657
    [11] Msakni M K, Diabat A, Rabadi G, Salem M A, Kotachi M. Exact methods for the quay crane scheduling problem when tasks are modeled at the single container level. Computers & Operations Research, 2018, 99(11): 218-233
    [12] Kim K H, Park Y M. A crane scheduling method for port container terminals. European Journal of Operational Research, 2004, 156(3): 752-768 doi: 10.1016/S0377-2217(03)00133-4
    [13] Nguyen S, Zhang M, Johnston M, Tan K C. Hybrid evolutionary computation methods for quay crane scheduling problems. Computers & Operations Research, 2013, 40(8): 2083-2093
    [14] Kim K H, Bae J W. A look-ahead dispatching method for automated guided vehicles in automated port container terminals. Transportation Science, 2004, 38(2): 224-234 doi: 10.1287/trsc.1030.0082
    [15] Choe R, Kim J, Ryu K R. Online preference learning for adaptive dispatching of AGVs in an automated container terminal. Applied Soft Computing, 2016, 38(1): 647-660
    [16] Kim J, Choe R, Ryu K R. Multi-objective optimization of dispatching strategies for situation-adaptive AGV operation in an automated container terminal. In: Proceedings of Research in Adaptive and Convergent Systems. New York, USA: ACM, 2013. 1−6
    [17] Xin J, Negenborn R R, Lodewijks G. Energy-aware control for automated container terminals using integrated flow shop scheduling and optimal control. Transportation Research Part C: Emerging Technologies, 2014, 44(7): 214-230
    [18] Peng Y, Wang W, Liu K, Li X, Tian Q. The Impact of the allocation of facilities on reducing carbon emissions from a green container terminal perspective. Sustainability, 2018, 10(6): 1813 doi: 10.3390/su10061813
    [19] Yang Y, Zhu X, Haghani A. Multiple equipment integrated scheduling and storage space allocation in rail–water intermodal container terminals considering energy efficiency. Transportation Research Record, 2019, 2673(3): 199-209 doi: 10.1177/0361198118825474
    [20] Dkhil H, Yassine A, Chabchoub H. Optimization of container handling systems in automated maritime terminal. Advanced Methods for Computational Collective Intelligence. Berlin Heidelberg: Springer, 2013. 301−312
    [21] Yang Y, Zhong M, Dessouky Y, Postolache O. An integrated scheduling method for AGV routing in automated container terminals. Computers & Industrial Engineering, 2018, 126(12): 482-493
    [22] Singgih I K, Hong S, Kim K H. Flow path design for automated transport systems in container terminals considering traffic congestion. Industrial Engineering & Management Systems, 2016, 15(1): 19-31
    [23] Legato P, Mazza R M, Trunfio R. Simulation-based optimization for discharge/loading operations at a maritime container terminal. OR Spectrum, 2010, 32(3): 543-567 doi: 10.1007/s00291-010-0207-2
    [24] Xin, J, Negenborn R R, Corman F, Lodewijks, G. Control of interacting machines in automated container terminals using a sequential planning approach for collision avoidance. Transportation Research Part C: Emerging Technologies, 2015, 60(11): 377-396
    [25] 原豪男, 郭戈. 交 通信息物理系统中的车辆协同运行优化调度. 自 动化学报, 2019, 45(01): 143-152

    Yuan Hao-Nan, Guo Ge. Vehicle cooperative optimization scheduling in transportation cyber physical systems. Acta Automatica Sinica, 2019, 45(01): 143-152
    [26] Lee D H, Wang H Q, Miao L. Quay crane scheduling with non-interference constraints in port container terminals. Transportation Research Part E: Logistics and Transportation Review, 2008, 44(1): 124-135 doi: 10.1016/j.tre.2006.08.001
    [27] 罗勋杰. 全 自动化集装箱码头水平运输方式对比. 水 运工程, 2016, 42(09): 76-82

    Luo Xun-Jie. Comparison of horizon transportation system of full automatic container terminal. Port &Waterway Engineering, 2016, 42(09): 76-82
    [28] 陈超, 张哲, 曾庆成. 集 装箱码头混合交叉作业集成调度模型. 交 通运输工程学报, 2012, 12(03): 92-100

    Chen Chao, Zhang Zhe, Zeng Qing-Cheng. Integrated scheduling model of mixed cross-operation for container terminal. Journal of Traffic and Transportation Engineering. 2012, 12(03): 92-100
    [29] 邢曦文, 毛钧, 张睿, 靳志宏. 基 于混合流水作业组织的集装箱码头装卸作业集成调度优化. 中 国管理科学, 2014, 22(10): 97-105

    Xin Xi-Wen, Mao Jun, Zhang Rui, Jin Zhi-Hong. Optimization of container loading/unloading integrated scheduling in a container terminal based on hybrid flowshop. Chinese Journal Of Management Science, 2014, 22(10): 97-105
    [30] 韩晓龙, 樊加伟. 自 动化港口AGV调度配置仿真分析. 重 庆交通大学学报(自然科学版), 2016, 35(05): 151-154+164

    Han Xiao-Long, Fan Jia-Wei. Analysis of AGV dispatching and configuration simulation of automated container terminals. Journal Of Chongqing Jiaotong University (Natural Science). 2016, 35(05): 151-154+164
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  • 收稿日期:  2019-09-03
  • 录用日期:  2019-12-15
  • 网络出版日期:  2020-01-04
  • 刊出日期:  2021-10-20

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