IDEAS home Printed from https://ideas.repec.org/a/eee/ejores/v283y2020i1p265-278.html
   My bibliography  Save this article

Adaptive policies for perimeter surveillance problems

Author

Listed:
  • Grant, James A.
  • Leslie, David S.
  • Glazebrook, Kevin
  • Szechtman, Roberto
  • Letchford, Adam N.

Abstract

We consider the problem of sequentially choosing observation regions along a line, with an aim of maximising the detection of events of interest. Such a problem may arise when monitoring the movements of endangered or migratory species, detecting crossings of a border, policing activities at sea, and in many other settings. In each case, the key operational challenge is to learn an allocation of surveillance resources which maximises successful detection of events of interest. We present a combinatorial multi-armed bandit model with Poisson rewards and a novel filtered feedback mechanism - arising from the failure to detect certain intrusions - where reward distributions are dependent on the actions selected. Our solution method is an upper confidence bound approach and we derive upper and lower bounds on its expected performance. We prove that the gap between these bounds is of constant order, and demonstrate empirically that our approach is more reliable in simulated problems than competing algorithms.

Suggested Citation

  • Grant, James A. & Leslie, David S. & Glazebrook, Kevin & Szechtman, Roberto & Letchford, Adam N., 2020. "Adaptive policies for perimeter surveillance problems," European Journal of Operational Research, Elsevier, vol. 283(1), pages 265-278.
    • Handle: RePEc:eee:ejores:v:283:y:2020:i:1:p:265-278
    DOI: 10.1016/j.ejor.2019.11.004
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0377221719309063
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ejor.2019.11.004?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Helmers, Roelof & Mangku, I. Wayan & Zitikis, Ricardas, 2005. "Statistical properties of a kernel-type estimator of the intensity function of a cyclic Poisson process," Journal of Multivariate Analysis, Elsevier, vol. 92(1), pages 1-23, January.
    2. Juha Heikkinen & Elja Arjas, 1999. "Modeling a Poisson Forest in Variable Elevations: A Nonparametric Bayesian Approach," Biometrics, The International Biometric Society, vol. 55(3), pages 738-745, September.
    3. Weinberg, Jonathan & Brown, Lawrence D. & Stroud, Jonathan R., 2007. "Bayesian Forecasting of an Inhomogeneous Poisson Process With Applications to Call Center Data," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1185-1198, December.
    4. Peter Diggle, 1985. "A Kernel Method for Smoothing Point Process Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 34(2), pages 138-147, June.
    5. Roberto Szechtman & Moshe Kress & Kyle Lin & Dolev Cfir, 2008. "Models of sensor operations for border surveillance," Naval Research Logistics (NRL), John Wiley & Sons, vol. 55(1), pages 27-41, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Grant, James A. & Szechtman, Roberto, 2021. "Filtered poisson process bandit on a continuum," European Journal of Operational Research, Elsevier, vol. 295(2), pages 575-586.
    2. Darlington, Matthew & Glazebrook, Kevin D. & Leslie, David S. & Shone, Rob & Szechtman, Roberto, 2023. "A stochastic game framework for patrolling a border," European Journal of Operational Research, Elsevier, vol. 311(3), pages 1146-1158.
    3. Keskin, Burcu B. & Griffin, Emily C. & Prell, Jonathan O. & Dilkina, Bistra & Ferber, Aaron & MacDonald, John & Hilend, Rowan & Griffis, Stanley & Gore, Meredith L., 2023. "Quantitative Investigation of Wildlife Trafficking Supply Chains: A Review," Omega, Elsevier, vol. 115(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    2. Rouba Ibrahim & Pierre L'Ecuyer, 2013. "Forecasting Call Center Arrivals: Fixed-Effects, Mixed-Effects, and Bivariate Models," Manufacturing & Service Operations Management, INFORMS, vol. 15(1), pages 72-85, May.
    3. Giuseppe Espa & Giuseppe Arbia & Diego Giuliani, 2013. "Conditional versus unconditional industrial agglomeration: disentangling spatial dependence and spatial heterogeneity in the analysis of ICT firms’ distribution in Milan," Journal of Geographical Systems, Springer, vol. 15(1), pages 31-50, January.
    4. Fang Lu & John J. Hasenbein & David P. Morton, 2016. "Modeling and Optimization of a Spatial Detection System," INFORMS Journal on Computing, INFORMS, vol. 28(3), pages 512-526, August.
    5. M. N. M. Lieshout, 2020. "Infill Asymptotics and Bandwidth Selection for Kernel Estimators of Spatial Intensity Functions," Methodology and Computing in Applied Probability, Springer, vol. 22(3), pages 995-1008, September.
    6. Barrow, Devon K., 2016. "Forecasting intraday call arrivals using the seasonal moving average method," Journal of Business Research, Elsevier, vol. 69(12), pages 6088-6096.
    7. Kinshuk Jerath & Anuj Kumar & Serguei Netessine, 2015. "An Information Stock Model of Customer Behavior in Multichannel Customer Support Services," Manufacturing & Service Operations Management, INFORMS, vol. 17(3), pages 368-383, July.
    8. Andrea Bastianin & Marzio Galeotti & Matteo Manera, 2019. "Statistical and economic evaluation of time series models for forecasting arrivals at call centers," Empirical Economics, Springer, vol. 57(3), pages 923-955, September.
    9. Zhengyi Zhou & David S. Matteson & Dawn B. Woodard & Shane G. Henderson & Athanasios C. Micheas, 2015. "A Spatio-Temporal Point Process Model for Ambulance Demand," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(509), pages 6-15, March.
    10. James W. Taylor, 2012. "Density Forecasting of Intraday Call Center Arrivals Using Models Based on Exponential Smoothing," Management Science, INFORMS, vol. 58(3), pages 534-549, March.
    11. Mola-Yudego, Blas & Selkimäki, Mari & González-Olabarria, José Ramón, 2014. "Spatial analysis of the wood pellet production for energy in Europe," Renewable Energy, Elsevier, vol. 63(C), pages 76-83.
    12. Albrecht, Tobias & Rausch, Theresa Maria & Derra, Nicholas Daniel, 2021. "Call me maybe: Methods and practical implementation of artificial intelligence in call center arrivals’ forecasting," Journal of Business Research, Elsevier, vol. 123(C), pages 267-278.
    13. Yingqi Zhao & Donglin Zeng & Amy H. Herring & Amy Ising & Anna Waller & David Richardson & Michael R. Kosorok, 2011. "Detecting Disease Outbreaks Using Local Spatiotemporal Methods," Biometrics, The International Biometric Society, vol. 67(4), pages 1508-1517, December.
    14. François Sémécurbe & Cécile Tannier & Stéphane G. Roux, 2019. "Applying two fractal methods to characterise the local and global deviations from scale invariance of built patterns throughout mainland France," Journal of Geographical Systems, Springer, vol. 21(2), pages 271-293, June.
    15. Merve Bodur & James R. Luedtke, 2017. "Mixed-Integer Rounding Enhanced Benders Decomposition for Multiclass Service-System Staffing and Scheduling with Arrival Rate Uncertainty," Management Science, INFORMS, vol. 63(7), pages 2073-2091, July.
    16. Peng Hou & Xiaojian Yi & Haiping Dong, 2020. "A Spatial Statistic Based Risk Assessment Approach to Prioritize the Pipeline Inspection of the Pipeline Network," Energies, MDPI, vol. 13(3), pages 1-16, February.
    17. Haensel, Alwin & Koole, Ger, 2011. "Booking horizon forecasting with dynamic updating: A case study of hotel reservation data," International Journal of Forecasting, Elsevier, vol. 27(3), pages 942-960, July.
    18. Ondřej Šedivý & Antti Penttinen, 2014. "Intensity estimation for inhomogeneous Gibbs point process with covariates-dependent chemical activity," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(3), pages 225-249, August.
    19. Bouezmarni, Taoufik & Rombouts, Jeroen V.K., 2010. "Nonparametric density estimation for positive time series," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 245-261, February.
    20. Theresa Maria Rausch & Tobias Albrecht & Daniel Baier, 2022. "Beyond the beaten paths of forecasting call center arrivals: on the use of dynamic harmonic regression with predictor variables," Journal of Business Economics, Springer, vol. 92(4), pages 675-706, May.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ejores:v:283:y:2020:i:1:p:265-278. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/eor .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.