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3-D Dynamic UAV Base Station Location Problem

Author

Listed:
  • Cihan Tugrul Cicek

    (Department of Industrial Engineering, Atilim University, 06830, Incek, Ankara, Turkey; Department of Industrial Engineering and Operations Research, University of California, Berkeley, Berkeley, California 94720)

  • Zuo-Jun Max Shen

    (Department of Industrial Engineering and Operations Research, University of California, Berkeley, Berkeley, California 94720)

  • Hakan Gultekin

    (Department of Mechanical & Industrial Engineering, Sultan Qaboos University, AL-Khoud 123, Muscat, Oman; Department of Industrial Engineering, TOBB University of Economics and Technology, 06560, Cankaya, Ankara, Turkey)

  • Bulent Tavli

    (Department of Electrical and Electronics Engineering, TOBB University of Economics and Technology, 06560, Cankaya, Ankara, Turkey)

Abstract

We address a dynamic covering location problem of an unmanned aerial vehicle base station (UAV-BS), in which the location sequence of a single UAV-BS in a wireless communication network is determined to satisfy data demand arising from ground users. This problem is especially relevant in the context of smart grid and disaster relief. The vertical movement ability of the UAV-BS and nonconvex covering functions in wireless communication restrict utilizing classical planar covering location approaches. Therefore, we develop new formulations to this emerging problem for a finite time horizon to maximize the total coverage. In particular, we develop a mixed-integer nonlinear programming formulation that is nonconvex in nature and propose a Lagrangean decomposition algorithm (LDA) to solve this formulation. Because of the high complexity of the problem, the LDA is still unable to find good local solutions to large-scale problems. Therefore, we develop a continuum approximation (CA) model and show that CA would be a promising approach in terms of both computational time and solution accuracy. Our numerical study also shows that the CA model can be a remedy to build efficient initial solutions for exact solution algorithms. Summary of Contribution: This paper addresses a facet of mixed integer nonlinear programming formulations. Dynamic facility location problems (DFLPs) arise in a wide range of applications. However, classical DFLPs typically focus on the two-dimensional spaces. Emerging technologies in wireless communication and some other promising application areas, such as smart grids, have brought new location problems that cannot be solved with classical approaches. For practical reasons, many research attempts to solve this new problem, especially by researchers whose primary research area is not OR, have seemed far from analyzing the characteristics of the formulations. Rather, solution-oriented greedy heuristics have been proposed. This paper has two main objectives: (i) to close the gap between practical and theoretical sides of this new problem with the help of current knowledge that OR possesses to solve facility location problems and (ii) to support the findings with an exhaustive computational study to show how these findings can be applied to practice.

Suggested Citation

  • Cihan Tugrul Cicek & Zuo-Jun Max Shen & Hakan Gultekin & Bulent Tavli, 2021. "3-D Dynamic UAV Base Station Location Problem," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 839-860, July.
  • Handle: RePEc:inm:orijoc:v:33:y:2021:i:3:p:839-860
    DOI: 10.1287/ijoc.2020.1034
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    References listed on IDEAS

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    1. R. Horst & N. V. Thoai, 1999. "DC Programming: Overview," Journal of Optimization Theory and Applications, Springer, vol. 103(1), pages 1-43, October.
    2. Yanfeng Ouyang & Carlos F. Daganzo, 2006. "Discretization and Validation of the Continuum Approximation Scheme for Terminal System Design," Transportation Science, INFORMS, vol. 40(1), pages 89-98, February.
    3. G. F. Newell, 1971. "Dispatching Policies for a Transportation Route," Transportation Science, INFORMS, vol. 5(1), pages 91-105, February.
    4. Marshall L. Fisher, 2004. "The Lagrangian Relaxation Method for Solving Integer Programming Problems," Management Science, INFORMS, vol. 50(12_supple), pages 1861-1871, December.
    5. Brotcorne, Luce & Laporte, Gilbert & Semet, Frederic, 2003. "Ambulance location and relocation models," European Journal of Operational Research, Elsevier, vol. 147(3), pages 451-463, June.
    6. Tingting Cui & Yanfeng Ouyang & Zuo-Jun Max Shen, 2010. "Reliable Facility Location Design Under the Risk of Disruptions," Operations Research, INFORMS, vol. 58(4-part-1), pages 998-1011, August.
    7. Cui, Tingting & Ouyang, Yanfeng & Shen, Zuo-Jun Max J, 2010. "Reliable Facility Location Design under the Risk of Disruptions," University of California Transportation Center, Working Papers qt5sh2c7pw, University of California Transportation Center.
    8. Xin Wang & Michael K. Lim & Yanfeng Ouyang, 2017. "A Continuum Approximation Approach to the Dynamic Facility Location Problem in a Growing Market," Transportation Science, INFORMS, vol. 51(1), pages 343-357, February.
    9. Ansari, Sina & Başdere, Mehmet & Li, Xiaopeng & Ouyang, Yanfeng & Smilowitz, Karen, 2018. "Advancements in continuous approximation models for logistics and transportation systems: 1996–2016," Transportation Research Part B: Methodological, Elsevier, vol. 107(C), pages 229-252.
    10. Dasci, Abdullah & Verter, Vedat, 2001. "A continuous model for production-distribution system design," European Journal of Operational Research, Elsevier, vol. 129(2), pages 287-298, March.
    11. Marshall L. Fisher, 2004. "Comments on ÜThe Lagrangian Relaxation Method for Solving Integer Programming ProblemsÝ," Management Science, INFORMS, vol. 50(12_supple), pages 1872-1874, December.
    12. Wang, Xin & Ouyang, Yanfeng, 2013. "A continuum approximation approach to competitive facility location design under facility disruption risks," Transportation Research Part B: Methodological, Elsevier, vol. 50(C), pages 90-103.
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