A Modelling Framework of Drone Deployment for Monitoring Air Pollution from Ships

  • Jingxu Chen
  • Shuaian WangEmail author
  • Xiaobo Qu
  • Wen Yi
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 98)


Sulphur oxide (SOx) emissions impose a serious health threat to the residents and a substantial cost to the local environment. In many countries and regions, ocean-going vessels are mandated to use low-sulphur fuel when docking at emission control areas. Recently, drones have been identified as an efficient way to detect non-compliance of ships, as they offer the advantage of covering a wide range of surveillance areas. To date, the managerial perspective of the deployment of a fleet of drones to inspect air pollution from ships has not been addressed yet. In this paper, we propose a modelling framework of drone deployment. It contains three components: drone scheduling at the operational level, drone assignment at the tactical level and drone base station location at the strategic level.



This research is sponsored by Environment and Conservation Fund Project 92/2017 and the Youth Program (No. 71501038), General Project (No. 71771050), Key Projects (No. 51638004) of the National Natural Science Foundation of China, and the Natural Science Foundation of Jiangsu Province in China (BK20150603). The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official policy or position of any agency of government.


  1. Fung, F.: Enforcement of fuel switching regulations – practices adopted in the US, EU and other regions, and lessons learned for China (2016). Accessed April 2017
  2. Ning, Z.: “Drone” technology update in port and ship emission monitoring and management (2016). Accessed April 2017
  3. Collum, J.: Meeting the SECA Challenge (2015). Accessed April 2017
  4. Roberts, L.: Drone detection of marine fuel sulphur emissions (2016). Accessed April 2017
  5. Marine Electronics and Communication. Drones lead the way in emissions compliance (2015). Accessed April 2017
  6. Kuang, Y., Qu, X., Wang, S.: A tree-structured crash surrogate measure for freeways. Accid. Anal. Prev. 77, 137–148 (2015)CrossRefGoogle Scholar
  7. Qu, X., Wang, S., Zhang, J.: On the fundamental diagram for freeway traffic: a novel calibration approach for single-regime models. Transp. Res. Part B: Methodological 73, 91–102 (2015)CrossRefGoogle Scholar
  8. Qu, X., Zhang, J., Wang, S.: On the stochastic fundamental diagram for freeway traffic: model development, analytical properties, validation, and extensive applications. Transp. Res. Part B: Methodological 104, 256–271 (2017)CrossRefGoogle Scholar
  9. Liu, Z., Wang, S., Chen, W., Zheng, Y.: Willingness to board: a novel concept for modeling queuing up passengers. Transp. Res. Part B 90, 70–82 (2016)CrossRefGoogle Scholar
  10. Liu, Z., Yi, W., Wang, S., Chen, J.: On the uniqueness of user equilibrium flow with speed limit. Netw. Spat. Econ. 17(3), 763–775 (2017a). Scholar
  11. Liu, Z., Wang, S., Zhou, B., Cheng, Q.: Robust optimization of distance-based tolls in a network considering stochastic day to day dynamics. Transp. Res. Part C 79, 58–72 (2017b)CrossRefGoogle Scholar
  12. Huang, D., Liu, Z., Liu, P., Chen, J.: Optimal transit fare and service frequency of a nonlinear origin destination based fare structure. Transp. Res. Part E 96, 1–19 (2016)CrossRefGoogle Scholar
  13. Zhen, L., Wang, S., Zhuge, D.: Analysis of three container routing strategies. Int. J. Prod. Econ. 193, 259–271 (2017a)CrossRefGoogle Scholar
  14. Zhen, L., Liang, Z., Zhuge, D., Lee, L.H., Chew, E.P.: Daily berth planning in a tidal port with channel flow control. Transp. Res. Part B: Methodological 106, 193–217 (2017b)CrossRefGoogle Scholar
  15. Zhen, L., Wang, K., Wang, S., Qu, X.: Tug scheduling for hinterland barge transport: a branch-and-price approach. Eur. J. Oper. Res. 265(1), 119–132 (2018)MathSciNetCrossRefGoogle Scholar
  16. Marine Traffic. Accessed April 2017
  17. Cordeau, J.F., Desaulniers, G., Desrosiers, J., Solomon, M.M., Soumis, F.: The VRP with Time Windows. Montréal: Groupe d’études et de recherche en analyse des décisions (2000)Google Scholar
  18. Desrochers, M., Desrosiers, J., Solomon, M.: A new optimization algorithm for the vehicle routing problem with time windows. Oper. Res. 40(2), 342–354 (1992)MathSciNetCrossRefGoogle Scholar
  19. Ho, S.C., Haugland, D.: A tabu search heuristic for the vehicle routing problem with time windows and split deliveries. Comput. Oper. Res. 31(12), 1947–1964 (2004)CrossRefGoogle Scholar
  20. Bräysy, O., Gendreau, M.: Vehicle routing problem with time windows, Part I: route construction and local search algorithms. Transp. Sci. 39(1), 104–118 (2005)CrossRefGoogle Scholar
  21. Kallehauge, B.: Formulations and exact algorithms for the vehicle routing problem with time windows. Comput. Oper. Res. 35(7), 2307–2330 (2008)MathSciNetCrossRefGoogle Scholar
  22. Wang, S., Liu, Z., Meng, Q.: Systematic network design for liner shipping services. Trans. Res. Rec. J. Transp. Res. Board 2330, 16-23. (2013a)CrossRefGoogle Scholar
  23. Liu, Z., Meng, Q., Wang, S., Sun, Z.: Global intermodal liner shipping network design. Transp. Res. Part E: Logistics Transp. Rev. 61, 28–39 (2014)CrossRefGoogle Scholar
  24. Zhen, L., Wang, S., Wang, K.: Terminal allocation problem in a transshipment hub considering bunker consumption. Naval Res. Logistics 63(7), 529–548 (2016a)MathSciNetCrossRefGoogle Scholar
  25. Wang, S., Wang, X.: A polynomial-time algorithm for sailing speed optimization with containership resource sharing. Transp. Res. Part B Methodological 93, 394–405 (2016)CrossRefGoogle Scholar
  26. Zhen, L., Wang, S., Zhuge, D.: Dynamic programming for optimal ship refueling decision. Transp. Res. Part E: Logistics Transp. Rev. 100, 63–74 (2017c)CrossRefGoogle Scholar
  27. Zhen, L., Zhuge, D., Zhu, S.L.: Production stage allocation problem in large corporations. Omega 73, 60–78 (2016b)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Logistics and Maritime StudiesThe Hong Kong Polytechnic UniversityHung Hom, KowloonHong Kong
  2. 2.Jiangsu Key Laboratory of Urban ITS, Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic TechnologiesSoutheast UniversityNanjingChina
  3. 3.Department of Architecture and Civil EngineeringChalmers University of TechnologyGothenburgSweden
  4. 4.School of Engineering and Advanced Technology, College of SciencesMassey UniversityAucklandNew Zealand

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