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HUANG Chuan, LYU Jing, AI Yun-fei. Optimization of VTS radar station location and configuration based on fine division of water area[J]. Journal of Traffic and Transportation Engineering, 2020, 20(3): 192-205. doi: 10.19818/j.cnki.1671-1637.2020.03.018
Citation: HUANG Chuan, LYU Jing, AI Yun-fei. Optimization of VTS radar station location and configuration based on fine division of water area[J]. Journal of Traffic and Transportation Engineering, 2020, 20(3): 192-205. doi: 10.19818/j.cnki.1671-1637.2020.03.018
shu

Optimization of VTS radar station location and configuration based on fine division of water area

doi: 10.19818/j.cnki.1671-1637.2020.03.018
  • 1.

    School of Transportation Engineering, Dalian Maritime University, Dalian 116026, Liaoning, China

  • 2.

    China Transport Telecommunications and Information Center, Bejing 100011, China

Funds:

National Key Research and Development Program of China 2017YFC0804904

National Key Research and Development Program of China 2018YFB1601504

National Natural Science Foundation of China 71974023

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    Abstract
  • In order to improve the efficiency of maritime safety supervision and ensure the safety production of maritime transportation, the vessel traffic service(VTS) radar station was viewed as the research object, the optimization of VTS radar station location based on the fine division of water area was studied. The effects of the occlusion factors in the actual environment and maritime risk factors on the monitoring performance of radar were considered, and a fine division of water area was proposed based on the software ArcGIS 10.4.1. The location of radar station and configuration type of radar were viewed as decision variables, the maximum coverage of water area and the lowest total cost were regarded as objective functions, then a mixed integer programming model was constructed. Based on the characteristics of the model, the multi-objective particle swarm optimization was designed, the heuristic rules for generating the initial particle swarms were proposed, and effective mutation operations were introduced into the algorithm. In order to verify the validity of the method, the series of ZDT test function were adopted to evaluate the performance of searching for the optimal solution and the convergence of algorithm. Research result shows that the spatial division of water area in consideration of occlusion factors and risk factors can be realized by the proposed method, and the water area monitored by the radar station is divided into 2 812 water area units in the example with 62 candidate radar stations. The global optimal solution in the ZDT test functions can be effectively found by the improved particle swarm algorithm, and the optimal solution with good convergence and average distribution is realized. 95.92% coverage of water area is achieved in the location optimization model of VTS radar station for the example. The total cost is 33 800 yuan. It can be seen that the location optimization model of VTS radar station considering the environmental occlusion and water risk factors is effective. The scientificity and rationality of VTS radar station location can be improved by the improved multi-objective particle swarm optimization algorithm, so it is an effective method to solve the location optimization problem of VTS radar station.

     

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    • References(31)
  • [1]
    PRAETORIUS G, HOLLNAGEL E, DAHLAMN J. Modelling vessel traffic service to understand resilience in everyday operations[J]. Reliability Engineering and System Safety, 2015, 141: 10-21. doi: 10.1016/j.ress.2015.03.020
    [2]
    JEON H S, LEE B G. Redundancy method for VTS system[C]//IEEE. 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI 2016). New York: IEEE, 2016: 99-103.
    [3]
    PRAETORIUS G, HOLLNAGEL E, DAHLMAN J. Modelling vessel traffic service to understand resilience in everyday operations[J]. Reliability Engineering and System Safety, 2015, 141: 10-21. doi: 10.1016/j.ress.2015.03.020
    [4]
    UCHACZ W, GALOR W. Optimization model of radar stations location in vessel traffic system[C]//IEEE. 18th International Conference on Methods and Models in Automation and Robotics. New York: IEEE, 2013: 426-429.
    [5]
    CAO De-sheng, LYU Jing, JIANG Xiao-lin. Coastal VTS classification based on hierarchical clustering analysis[J]. Journal of Wuhan University of Technology (Transportation Science and Engineering), 2014, 38(3): 576-584. (in Chinese). doi: 10.3963/j.issn.2095-3844.2014.03.023
    [6]
    CAO De-sheng, LYU Jing, AI Yun-fei, et al. Optimization model of VTS radar station location problem[J]. Journal of Beijing University of Aeronautics and Astronautics, 2014, 40(6): 727-731. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-BJHK201406002.htm
    [7]
    CAO De-sheng, LYU Jing, AI Yun-fei, et al. Optimization location model of VTS radar stations based on set covering theory[J]. Transactions of Beijing Institute of Technology, 2014, 34(7): 752-756. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-BJLG201407019.htm
    [8]
    AI Yun-fei, CAO De-sheng, SHEN Bing, et al. Bi-level optimization model of VTS center layout and radar station location-configuration[J]. Journal of Dalian Maritime University, 2017, 43(3): 107-111. (in Chinese). doi: 10.3969/j.issn.1671-7031.2017.03.020
    [9]
    SUN Yao-hua, CHEN Chang-yuan, CHEN Xiao, et al. Optimization model of VTS radar station allocation configuration based on LINGO[J]. Ship and Ocean Engineering, 2017, 46(S2): 275-278. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-BJLG201407019.htm
    [10]
    ZHENG Yuan-zhou, CHENG Xiao-dong, GAN Lang-xiong, et al. Shadowing effect of coastal building on VTS radar[J]. Navigation of China, 2018, 41(1): 1-6, 12. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGHH201801001.htm
    [11]
    ŁUBCZONEK J. Application of GIS techniques in VTS radar stations planning[C]//IEEE. International Radar Symposium. New York: IEEE, 2008: 1-4.
    [12]
    CAO Shi-lian, JIN Yi-cheng, YIN Yong. On simulating marine radar echo intensity generated by 3D graphic rendering[J]. Journal of System Simulation, 2016, 28(9): 2076-2084. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-XTFZ201609023.htm
    [13]
    CAO Shi-lian, JIN Yi-cheng, YIN Yong. Key technologies for generating marine radar image by scene rendering[J]. Journal of Harbin Engineering University, 2017, 38(5): 711-718, 790. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HEBG201705009.htm
    [14]
    WIERSMA E, MASTENBROEK N. Measurement of vessel traffic service operator performance[J]. IFAC Proceedings Volumes, 1997, 30(24): 61-64. doi: 10.1016/S1474-6670(17)42223-3
    [15]
    LEE G, KIM S Y, LEE M K. Economic evaluation of vessel traffic service (VTS): a contingent valuation study[J]. Marine Policy, 2015, 61: 149-154. doi: 10.1016/j.marpol.2015.08.011
    [16]
    HU Wei-xuan, LIN Dan, DU Yan. Application research of comprehensive evaluation of VTS based on combination evaluation[J]. Science Research Management, 2016, 37(S): 533-546. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-KYGL2016S1078.htm
    [17]
    MOU Jun-min, ZHOU Cui, DU Yu, et al. Evaluate VTS benefits: a case study of Zhoushan Port[J]. International Journal of e-Navigation and Maritime Economy, 2015, 3: 22-31. doi: 10.1016/j.enavi.2015.12.003
    [18]
    CHENG Yuan-e, ZHOU Shao-guang, YUAN Chun-qi, et al. Water boundary extraction method combining LiDAR and remote sensing image[J]. Geospatial Information, 2017, 15(2): 76-79. (in Chinese). doi: 10.3969/j.issn.1672-4623.2017.02.024
    [19]
    ZHANG Wei, YANG Xin, TANG Guo-an, et al. DEM-based flow direction algorithms study of stream extraction and watershed delineation in the low relief areas[J]. Science of Surveying and Mapping, 2012, 37(2): 94-96. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-CHKD201202033.htm
    [20]
    MARTÍNEZ-LÓPEZ J, CARREÑO M F, PALAZÓN-FERRANDO J A, et al. Free advanced modeling and remote-sensing techniques for wetland watershed delineation and monitoring[J]. International Journal of Geographical Information Science, 2014, 28(8): 1610-1625. doi: 10.1080/13658816.2013.852677
    [21]
    LIANG Jia-yong, LIU De-sheng. A local thresholding approach to flood water delineation using Sentinel-1 SAR imagery[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 159: 53-62. doi: 10.1016/j.isprsjprs.2019.10.017
    [22]
    HU Da-wei, CHEN Xi-qiong, GAO Yang. Review on location-routing problem[J]. Journal of Traffic and Transportation Engineering, 2018, 18(1): 111-129. (in Chinese). doi: 10.3969/j.issn.1671-1637.2018.01.011
    [23]
    ZHANG Qian, GAO Li-qun, HU Xiang-pei. Review on optimal algorithms of location-routing problem (LRP) in integrated logistics[J]. Journal of Northeastern University(Natural Science), 2003, 24(1): 31-34. (in Chinese). doi: 10.3321/j.issn:1005-3026.2003.01.009
    [24]
    MI Yang. Bi-objective optimal locations of charging stations for electric vehicles[J]. Journal of Harbin Engineering University, 2018, 39(8): 1264-1268, 1342. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-HEBG201808002.htm
    [25]
    FINK M, DESAULNIERS G, FREY M, et al. Column generation for vehicle routing problems with multiple synchronization constraints[J]. European Journal of Operational Research, 2019, 272: 699-711. doi: 10.1016/j.ejor.2018.06.046
    [26]
    LI Shu-qin, JIA Shuai. The seaport traffic scheduling problem: formulations and a column-row generation algorithm[J]. Transportation Research Part B: Methodological, 2019, 128: 158-184. doi: 10.1016/j.trb.2019.08.003
    [27]
    MIRJALILI S. Moth-flame optimization algorithm: a novel nature-inspired heuristic paradigm[J]. Knowledge-Based Systems, 2015, 89: 228-249. doi: 10.1016/j.knosys.2015.07.006
    [28]
    YUAN Qun, ZUO Yi. Selection of cold chain logistics distribution center location based on improved hybrid genetic algorithm[J]. Journal of Shanghai Jiaotong University, 2016, 50(11): 1795-1800. (in Chinese). https://www.cnki.com.cn/Article/CJFDTOTAL-SHJT201611023.htm
    [29]
    WEI Mei-yi, LI Xiang, YU Le-an. Time-dependent fuzzy random location-scheduling programming for hazardous materials transportation[J]. Transportation Research Part C: Emerging Technologies, 2015, 57: 146-165. doi: 10.1016/j.trc.2015.06.012
    [30]
    ZHENG Yu-jun, CHEN Sheng-yong. Cooperative particle swarm optimization for multiobjective transportation planning[J]. Applied Intelligence, 2013, 39(1): 202-216. doi: 10.1007/s10489-012-0405-5
    [31]
    AMELI A, BAHRAMI S, KHAZAELI F, et al. A multiobjective particle swarm optimization for sizing and placement of DGs from DG owner's and distribution company's viewpoints[J]. IEEE Transactions on Power Delivery, 2014, 29(4): 1831-1840. doi: 10.1109/TPWRD.2014.2300845
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