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A progressive hedging method for the optimization of social engagement and opportunistic IoT problems

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  • Fadda, Edoardo
  • Perboli, Guido
  • Tadei, Roberto

Abstract

Due to the spread of the social engagement paradigm, several companies are asking people to perform tasks in exchange for a reward. The advantages of this business model are savings in economic and environmental terms. In previous works, it has been proved that the problem of finding the minimum amount of reward such that all tasks are performed is difficult to solve, even for medium-size realistic instances (if more than one type of person is considered). In this paper, we propose a customized version of the progressive hedging algorithm that is able to provide good solutions for large realistic instances. The proposed method reaches the goal of defining a procedure that can be used in real environments.

Suggested Citation

  • Fadda, Edoardo & Perboli, Guido & Tadei, Roberto, 2019. "A progressive hedging method for the optimization of social engagement and opportunistic IoT problems," European Journal of Operational Research, Elsevier, vol. 277(2), pages 643-652.
    • Handle: RePEc:eee:ejores:v:277:y:2019:i:2:p:643-652
    DOI: 10.1016/j.ejor.2019.02.052
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    Cited by:

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    2. Farough Motamed Nasab & Zukui Li, 2023. "Multistage Adaptive Robust Binary Optimization: Uncertainty Set Lifting versus Partitioning through Breakpoints Optimization," Mathematics, MDPI, vol. 11(18), pages 1-24, September.
    3. Fadda, Edoardo & Manerba, Daniele & Cabodi, Gianpiero & Camurati, Paolo Enrico & Tadei, Roberto, 2021. "Comparative analysis of models and performance indicators for optimal service facility location," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).
    4. Perboli, Guido & Brotcorne, Luce & Bruni, Maria Elena & Rosano, Mariangela, 2021. "A new model for Last-Mile Delivery and Satellite Depots management: The impact of the on-demand economy," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 145(C).

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