求解集装箱码头泊位-岸桥分配多目标算法

doi:10.16182/j.issn1004731x.joss.201803051

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (3): 1178-1189.doi: 10.16182/j.issn1004731x.joss.201803051

求解集装箱码头泊位-岸桥分配多目标算法

王旭^1,2, 刘士新², 张瑞友², 王佳³

1.河北环境工程学院经济学院, 秦皇岛 066102;
2.东北大学信息科学与工程学院流程工业综合自动化国家重点实验室,沈阳 110819;
3.东北大学秦皇岛分校经济学院,秦皇岛 066004

收稿日期:2016-03-22 出版日期:2018-03-08 发布日期:2019-01-02
作者简介:王旭(1982-),男,辽宁沈阳,博士,研究方向为仿真优化建模;刘士新(1968-),男,辽宁调兵山,博士,教授,研究方向为项目管理与优化调度。
基金资助:
国家自然科学基金(71601040, 61573089,61333006,71471034)

Integrated Berth and Quay Crane Allocation Multi-objective Algorithm for Container Terminal

Wang Xu^1,2, Liu Shixin², Zhang Ruiyou², Wang Jia³

1.School of Economics, Hebei University of Environmental Engineering, Qinhuangdao 066102, China;
2.College of Information Science & Engineering, Northeastern University, State Key Laboratory of Synthetical Automation for Process Industries, Shenyang 110819, China;
3.School of Economics, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China

Received:2016-03-22 Online:2018-03-08 Published:2019-01-02

摘要/Abstract

摘要： 针对低碳型集装箱码头连续泊位-岸桥分配问题,考虑到港时间不确定性和集卡运输能力受限等约束,建立了以最小化船舶碳排放和平均延迟时间为目标的多目标非线性混合整数规划模型,采用高效非支配排序的遗传算法(Efficient Non-dominate Sort Genetic AlogrithmII,ENSGA-II)对模型进行求解。针对问题的特点,设计了4链编码规则和基于滚动仿真的解码规则、带精英选择的PPX交叉算子、多约束下变异算子和不可行解修复算子。通过多组测试算例对算法进行测试,实验结果表明：与NSGA-II和MNSGA-II相比,ENSGA-II能快速求得满意解,适合求解本文问题。

关键词: 低碳, 泊位岸桥分配, 多目标, ENSGA-II, 仿真

Abstract: This paper researches on a continuous berth and quay crane allocation problem for low carbon container terminal. Considering the constraints of stochastic arrival time and limited truck transportation ability, a multi-objective nonlinear mixed integer programming model is established to minimize carbon emissions and average delay time. An efficient non-dominate sorting genetic algorithm II (ENSGA-II) is designed to deal with this problem. According to the characteristics of the problem, encoding rule and decoding rule based on rolling-horizon simulation, PPX crossover operator with elite selection, multi constraints mutation operator and repair operator are adopted. The results of several simulation experiments show that ENSGA-II can get the satisfied solution more quickly than NSGAII and MNSGA-II. Moreover, it’s suitable for solving integrated berth and quay crane allocation problem.

Key words: low carbon, berth and quay crane allocation, multi-objective, ENSGA-II, simulation

中图分类号:

TP391

王旭, 刘士新, 张瑞友, 王佳. 求解集装箱码头泊位-岸桥分配多目标算法[J]. 系统仿真学报, 2018, 30(3): 1178-1189.

Wang Xu, Liu Shixin, Zhang Ruiyou, Wang Jia. Integrated Berth and Quay Crane Allocation Multi-objective Algorithm for Container Terminal[J]. Journal of System Simulation, 2018, 30(3): 1178-1189.

参考文献 25

[1]	Sakai H, Watanabe Y.The estimation model of carbon dioxide emission at container terminals[J]. Journal of Japan Industrial Management Association(S2187-9079), 2006, 57(1): 68-79.
[2]	Imai A, Chen H C, Nishimura E.The simultaneous berth and quay crane allocation problem[J]. Transportation Research Part E(S1336-5545), 2008, 44(5): 900-920.
[3]	Kim K H, Moon K C.Berth scheduling by simulated annealing[J]. Transportation Research Part B (S0191-2615), 2003, 37(6): 541-560.
[4]	Legato P, Trunfio R, Meisel F.Modeling and solving rich quay crane scheduling problems[J]. Computers & Operations Research(S0305-0548), 2012, 33(9): 2063-2078.
[5]	Fu Y M, Diabat A.A multi-vessel quay crane assignment and scheduling problem: formulation and heuristic solution approach[J]. Expert Systems with Applications (S0957-4174), 2014, 41(15): 6959-6956.
[6]	Rodriguez-Molins M, Ingolotti L, Barber F.A genetic algorithm for robust berth allocation and quay crane assignment[J]. Progress in Artificial Intelligence (S2192-6352), 2014, 2(4): 177-192.
[7]	Raa B, Dullaert W.An Enrich model for the integrated berth allocation and quay crane assignment problem[J]. Expert Systems with Application (S0957-4174), 2011, 38(11): 14136-14147.
[8]	肖玲, 胡志华. 基于滚动策略的集装箱码头连续泊位与桥吊集成调度[J]. 计算机应用, 2013, 33(10): 2969-2973.Xiao L, Hu Z H. Berth allocation problem with quay crane assignment of container terminals based on rolling- horizon strategy[J]. Journal of Computer Applications, 2013, 33(10): 2969-2973.
[9]	Golias M, Boile M.The berth allocation problem: optimizing vessel arrival time[J]. Martime Economics & Logistics(S1479-2931), 2009, 11(4): 358-377.
[10]	Du Y Q, Chen Q S.Berth allocation considering fuel consumption and vessel emissions[J]. Transportation Research Part E(S1336-5545), 2011, 47(6): 1021-1037.
[11]	Song D P, Xu J J.An operational activity-based method to estimate CO2 emissions from container shipping considering empty container repositioning[J]. Transportation Research Part D(S1361-9209), 2012, 17(1): 91-96.
[12]	赵刚. 国际航运管理[M]. 大连: 大连海事大学出版社, 2006: 93-94. Zhao G. International shipping management[M]. Dalian: DALIAN Martime Publiser, 2006: 93-94.
[13]	Deb K, Pratap A, Agarwal S.A fast and elitist multiobject genetic algorithm:NSGAII[J]. IEEE Transaction on Evolutionary Computation(S1089-778X), 2002, 6(2): 184-197.
[14]	Ebrahimi M, Ghomi S M T F, Karimi B. Hybrid flow shop scheduling with sequence dependent family setup time and uncertain due dates[J]. Applied Mathematical Modelling(S0307-904X), 2014, 38(9): 2490-2504.
[15]	Abedi M, Seidgar H.Bi-objective optimization for scheduling the identical parallel batch-processing machines with arbitrary job sizes, unequal job release times and capacity limits[J]. International Journal of Production Research(S0020-7543), 2015, 53(6): 1680-1711.
[16]	Zarandi M H F, Kayvanfar V. A bi-objective identical parallel machine scheduling problem with controllable processing times: a just-in-time approach[J]. International Journal of Advanced Manufacturing Technology (S0268-3768), 2014, 77(1): 545-563.
[17]	Zhang X Y, Tian Y, Cheng R, et al.An efficient approach to nondominated sorting for evolutionary multiobjective optimization[J]. IEEE Transctions on Evolutionary Computation(S1089-778X), 2015, 19(2): 201-213.
[18]	Mishra S, Mondal S, Saha S.Improved solution to the non-domination level update problem[J]. Applied Soft Computing(S1568-4946), 2015, 8(1): 1-18.
[19]	Wang H, Zhang Q, Jiao L.Regularity model for noisy multiobjective optimization[J]. IEEE Transactions on Cybernetics(S1083-4419), 2015, 8(99): 1-13.
[20]	Tang S, Cai Z, Zheng J.A Fast method of constructing the non-dominated set: Arena’s Principle[C]// The 4th International Conference on Natural Computation, Jinan, 2008: 391-395.
[21]	Mcclymont K, Keedwell E.Deductive sort and climbing sort: new methods for non-dominated sorting[J]. Evolutionary Computation(S1063-6560), 2012, 20(1): 1-26.
[22]	Bandyopadhyay S, Bhattacharya R.Solving multi- objective parallel machine scheduling problem by a modified NSGA-II[J]. Applied Mathematical Modelling (S0307-904X), 2013, 37(10): 6718-6729.
[23]	张红菊, 乐美龙. 基于多目标粒子群算法的泊位-岸桥分配研究[J]. 武汉理工大学学报, 2012, 34(2): 59-64. Zhang H J, Le L M. Research on container berth-quay crane allocation based on multi-objective PSO[J]. Journal of Wuhan University of Technology, 2012, 34(2): 59-64.
[24]	杨春霞, 王诺. 基于多目标遗传算法的集装箱码头的泊位岸桥分配问题研究[J]. 计算机应用研究, 2010, 27(5): 1720-1722. Yang C X, Wang N. Berth-quay crane allocation in container terminal based on multi-objective genetic algorithm[J]. Application Research of Computers, 2010, 27(5): 1720-1722.
[25]	Zitzler E.Evolutionary algorithms for multiobjective optimization: methods and applications[D]. Switzerland: Swiss Federal Institute of Technology, 1999.

求解集装箱码头泊位-岸桥分配多目标算法

Integrated Berth and Quay Crane Allocation Multi-objective Algorithm for Container Terminal

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