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Revenue maximization of Internet of things provider using variable neighbourhood search

Author

Listed:
  • Vesna Radonjić Ɖogatović

    (University of Belgrade)

  • Marko Ɖogatović

    (University of Belgrade)

  • Milorad Stanojević

    (University of Belgrade)

  • Nenad Mladenović

    (Khalifa University)

Abstract

Internet of things (IoT) covers various aspects of collecting and exchanging data between diverse entities. From IoT provider’s perspective, one of the most significant issues is how to set the price that maximizes its revenue while meeting users’ requirements. In this paper, we focus on revenue maximization of the IoT service provider by applying pay per use pricing within the combinatorial sealed-bid auction. Pay per use pricing option implies that each user is charged per unit of consumption according to the actual usage. We assume that a user pays a threshold price for a unit of consumption, which is determined based on the auction. The auction is conducted with bidding prices set up in advance within service level agreement (SLA). We use variable neighbourhood search (VNS) in order to derive the optimal threshold price that maximizes IoT provider’s revenue, and users’ satisfaction. In addition, the optimization within the auction mechanism is conducted using different metaheuristics, which are compared with two types of VNS algorithms.

Suggested Citation

  • Vesna Radonjić Ɖogatović & Marko Ɖogatović & Milorad Stanojević & Nenad Mladenović, 2020. "Revenue maximization of Internet of things provider using variable neighbourhood search," Journal of Global Optimization, Springer, vol. 78(2), pages 375-396, October.
    • Handle: RePEc:spr:jglopt:v:78:y:2020:i:2:d:10.1007_s10898-020-00894-z
    DOI: 10.1007/s10898-020-00894-z
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    References listed on IDEAS

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    1. Pierre Hansen & Nenad Mladenović & Jack Brimberg & José A. Moreno Pérez, 2010. "Variable Neighborhood Search," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 61-86, Springer.
    2. Lokketangen, Arne & Glover, Fred, 1998. "Solving zero-one mixed integer programming problems using tabu search," European Journal of Operational Research, Elsevier, vol. 106(2-3), pages 624-658, April.
    3. Sergey V. Ivanov & Andrey I. Kibzun & Nenad Mladenović & Dragan Urošević, 2019. "Variable neighborhood search for stochastic linear programming problem with quantile criterion," Journal of Global Optimization, Springer, vol. 74(3), pages 549-564, July.
    4. Jack Brimberg & Nenad Mladenović & Raca Todosijević & Dragan Urošević, 2019. "Solving the capacitated clustering problem with variable neighborhood search," Annals of Operations Research, Springer, vol. 272(1), pages 289-321, January.
    5. Jun Pei & Zorica Dražić & Milan Dražić & Nenad Mladenović & Panos M. Pardalos, 2019. "Continuous Variable Neighborhood Search (C-VNS) for Solving Systems of Nonlinear Equations," INFORMS Journal on Computing, INFORMS, vol. 31(2), pages 235-250, April.
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    Cited by:

    1. Angelo Sifaleras & Nenad Mladenović & Panos M. Pardalos, 2020. "Preface to the special issue “ICVNS 2018”," Journal of Global Optimization, Springer, vol. 78(2), pages 239-240, October.

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