带有多重能力等级选择的内陆港选址问题研究

交通运输系统工程与信息 ›› 2017, Vol. 17 ›› Issue (3): 184-191.

带有多重能力等级选择的内陆港选址问题研究

常征^*a，齐壮^b，吕靖^a

大连海事大学a. 交通运输管理学院; b. 航海学院，辽宁大连116026

收稿日期:2016-09-18 修回日期:2017-01-13 出版日期:2017-06-25 发布日期:2017-06-26
作者简介:常征（1986-），女，吉林德惠人，讲师.
基金资助:
国家自然科学基金/National Natural Science Foundation of China（71603036）；辽宁省自然科学基金/ Liaoning Natural Science Foundation（201602077）；中央高校基本科研业务经费专项资金/ Fundamental Research Funds for the Central Universities（3132017082）

Inland Port Location Problem with Multiple Capacity Level Choices

CHANG Zheng ^a，QI Zhuang ^b，LV Jing ^a

a. Transportation Management College; b. Navigation College, Dalian Maritime University, Dalian 116026, Liaoning, China

Received:2016-09-18 Revised:2017-01-13 Online:2017-06-25 Published:2017-06-26

摘要/Abstract

摘要：

为促进资源的合理利用，减少内陆港的重复建设，以系统总成本，包括建设成本和运输成本最小为目标函数，并考虑内陆港的通过能力限制，构建了两阶段的带有多重能力等级选择的内陆港选址模型，同时设计遗传算法求解.东北地区的实例分析结果表明，从系统最优角度出发，还应增设通过能力分别为5 万TEU和15 万TEU的齐齐哈尔内陆港、佳木斯内陆港，并形成由近、中、远距离内陆港群构成的区域内陆运输节点布局.结果同时表明，在选址决策中加入对通过能力的选择，可以使内陆港的布局与内陆地区的运输需求分布更加趋于均衡，减少不必要投资，提高资源使用效率.

关键词: 综合交通运输, 选址, 多重能力决策, 内陆港, 遗传算法

Abstract:

With the aim of utilizing resources reasonably and decreasing repeated construction, a two-stage inland port location model with multiple capacity level choices is proposed. The model takes capacity constraint into consideration. Its objective is to minimize total cost, which includes fix-up cost and transportation cost. Genetic algorithm is applied as the solving technique. A case study of Northeast China is presented and shows two more optimal inland ports, namely Qiqihar and Jiamusi are selected considering systematic optimum, and the corresponding capacity are 50 and 150 thousand TEU. With respect to the result, a regional inland transportation nodes layout consists of dense, middle range and distant inland ports clusters is formed. Meanwhile, the result also indicates the choice of capacity in inland port’s location decision making is crucial to balance the layout of inland ports and the distribution of inland transportation demands, reduce unnecessary investment and increase utilization efficiency of resources.

Key words: integrated transportation, location, multiple capacity choices, inland port, genetic algorithm

中图分类号:

U115

常征，齐壮，吕靖. 带有多重能力等级选择的内陆港选址问题研究[J]. 交通运输系统工程与信息, 2017, 17(3): 184-191.

CHANG Zheng，QI Zhuang，LV Jing. Inland Port Location Problem with Multiple Capacity Level Choices[J]. Journal of Transportation Systems Engineering and Information Technology, 2017, 17(3): 184-191.

导出引用管理器 EndNote|Ris|BibTeX

链接本文: http://www.tseit.org.cn/CN/abstract/abstract19431.shtml

http://www.tseit.org.cn/CN/Y2017/V17/I3/184

参考文献

[1] CORREIA I, NICKEL S, SALDANHA-DA-GAMA F. Single-assignment hub location problems with multiple capacity levels[J]. Transportation Research Part B, 2010, 44(8-9): 1047-1066.

[2] CONTRERAS I, CORDEAU J F, LAPORTE G. Exact solution of large-scale hub location problems with multiple capacity levels[J]. Transportation Science, 2012, 46(4): 439-459.

[3] ZAREI V, MAHDAVI I, MOGHADDAM T R, et al. A multi-level capacity approach to the hub and spoke network[J]. International Journal of Industrial Engineering & Production Research, 2013, 24(1): 1-15.

[4] HORHAMMER A M C. Dynamic hub location problems with single allocation and multiple capacity levels[C]. Proceedings of the 2014 47th Hawaii International Conference on System Sciences, 2014, 6-9 January,Waikoloa, USA.

[5] 任鸣鸣. 供应链系统节点设施选址研究[D]. 武汉：华中科技大学, 2008. [REN M M. Facilities location study for supply chain system[D]. Wuhan: Huazhong University of Science and Technology, 2008.]

[6] NGUYENA L C, NOTTEBOOM T. A multi- criteria approach to dry port location in developing economies with application to Vietnam[J]. The Asian Journal of Shipping and Logistics, 2016, 32(1): 23-32.

[7] 李强, 陈雪玫. 基于主成分分析法的江西无水港选址[J]. 水运管理, 2016, 38(2): 31-33. [LI Q, CHEN X M. Inland port location in Jiangxi using principal components analysis[J]. Shipping Management, 2016, 38 (2): 31-33.]

[8] AMBROSINO D, SCIOMACHEN A. Location of midrange dry ports in multimodal logistic networks[J]. Procedia-Social and Behavioral Sciences, 2014, 108(8): 118-128.

[9] RUDOLPH G. Convergence analysis of canonical genetic algorithms[J]. IEEE Transactions on Neural Networks, 1994, 5(1): 96-101.

[10] DE JONG K A. An analysis of the behavior of a class of genetic adaptive systems[D]. USA: University of Michigan, 1975.

[11] 何小明, 王薇. 江门市外贸集装箱生成量预测[J]. 集装箱化, 2014(9): 9-10. [HE X M, WANG W. Forecast of foreign container growing volume in Jiangmen[J]. Containerization, 2014(9): 9-10.]

[1]	刘忠波, 王淇娴, 郑红星. 海空协同的远海岛礁战储物资供给优化模型与算法[J]. 交通运输系统工程与信息, 2022, 22(2): 268-279.
[2]	靳志宏, 焉红, 王小寒, 邢磊, 徐奇. 滚装甩挂运输牵引车调度优化及作业模式类比分析[J]. 交通运输系统工程与信息, 2022, 22(1): 142-151.
[3]	薛运强, 郭军, 钟蒙, 安静. 基于不确定理论的常规公交车辆调度优化[J]. 交通运输系统工程与信息, 2021, 21(6): 115-122.
[4]	珠兰, 马潇, 刘卓凡. 时间依赖型绿色车辆路径问题研究[J]. 交通运输系统工程与信息, 2021, 21(6): 187-194.
[5]	吕彪, 管心怡, 高自强. 地铁网络服务韧性评估与最优恢复策略[J]. 交通运输系统工程与信息, 2021, 21(5): 198-205.
[6]	楚金华, 李俊鹤, 王春娟, 陈超. 排放控制区下集装箱班轮航线路径规划与航速调度集成决策[J]. 交通运输系统工程与信息, 2021, 21(4): 230-238.
[7]	赵旭，张汇妍，黄瑞. 节点中断与需求不确定下的港口供应链整合[J]. 交通运输系统工程与信息, 2021, 21(3): 34-39.
[8]	徐智勇. 铁路电分相布设及纵断面协同优化研究[J]. 交通运输系统工程与信息, 2021, 21(3): 170-175.
[9]	刘显贵, 王晖年, 洪经纬, 郝雷. 网联环境下信号交叉口车速控制策略及优化[J]. 交通运输系统工程与信息, 2021, 21(2): 82-90.
[10]	汪茜，柏赟，李佳杰，朱巧珍，冯旭杰. 城市轨道交通地下线车站选址与平面线形优化[J]. 交通运输系统工程与信息, 2021, 21(2): 119-125.
[11]	谌微微，许茂增，邢青松. 考虑续航能力的新能源汽车充电站递阶延时布局研究[J]. 交通运输系统工程与信息, 2020, 20(6): 156-162.
[12]	成英，赵建有，汪磊. 基于多车协作优化的冲突消解模型[J]. 交通运输系统工程与信息, 2020, 20(6): 205-211.
[13]	郭野晨风，胡明华，张颖，谢华. 改进的协同航路分配优化模型及算法研究[J]. 交通运输系统工程与信息, 2020, 20(6): 226-232.
[14]	左大杰，肖国胜，王孟云. 产业转移背景下西部运输需求的完全分解模型[J]. 交通运输系统工程与信息, 2020, 20(5): 15-20.
[15]	冯涛，彭其渊，陶思宇，陈昕梅. 站城融合模式下既有铁路车站城市功能开发体量预测研究[J]. 交通运输系统工程与信息, 2020, 20(5): 21-28.