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A chance-constraint approach for optimizing Social Engagement-based services

Wybrane pełne teksty z tego czasopisma
Identyfikatory
Warianty tytułu
Konferencja
Federated Conference on Computer Science and Information Systems (17 ; 04-07.09.2022 ; Sofia, Bulgaria)
Języki publikacji
EN
Abstrakty
EN
Social Engagement is a novel business model whose goal is transforming final users of a service from passive components into active ones. In this framework, people are contacted by the decision-maker (generally a company) and they are asked to perform tasks in exchange for a reward. This paves the way to the interesting optimization problem of allocating the different types of workforce so as to minimize costs. Despite this problem has been investigated within the operations research community, there is no model that allows to solve it by explicitly and appropriately modeling the behavior of contacted candidates through consolidated concepts from utility theory. This work aims at filling this gap. We propose a stochastic optimization model including a chance constraint that puts in relation, under probabilistic terms, the candidate willingness to accept a task and the reward actually offered by the decision-maker. The proposed model aims at optimally deciding which user to contact, the amount of the reward proposed, and how many employees to use in order to minimize the total expected costs of the operations. A solution approach is proposed to address the formulated stochastic optimization problem and its computational efficiency and effectiveness are investigated through an extensive set of computational experiments.
Rocznik
Tom
Strony
301--304
Opis fizyczny
Bibliogr. 11 poz., wz., tab., wykr.
Twórcy
  • Transport and Mobility Laboratory, Ecole Polytechnique Fédérale de Lausanne - CH-1015 EPFL, Lausanne (Switzerland)
  • Dept. of Mathematical Sciences, Politecnico di Torino - corso Duca degli Abruzzi 24, Torino (Italy)
  • Dept. of Control and Computer Engineering, Politecnico di Torino - corso Duca degli Abruzzi 24, Torino (Italy)
  • Dept. of Information Engineering, Università degli Studi di Brescia, via Branze 38, Brescia (Italy)
Bibliografia
  • 1. F. Corno, L. D. Russis, and T. Montanaro, “Estimate user meaningful places through low-energy mobile sensing,” in 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, Oct. 2016. http://dx.doi.org/10.1109/smc.2016.7844703
  • 2. A. Santini, A. Viana, X. Klimentova, and J. P. Pedroso, “The probabilistic travelling salesman problem with crowdsourcing,” Computers & Operations Research, vol. 142, p. 105722, Jun. 2022. http://dx.doi.org/10.1016/j.cor.2022.105722
  • 3. A. Alnaggar, F. Gzara, and J. H. Bookbinder, “Crowdsourced delivery: A review of platforms and academic literature,” Omega, vol. 98, p. 102139, Jan. 2021. http://dx.doi.org/10.1016/j.omega.2019.102139
  • 4. B. Guo, D. Zhang, Z. Wang, Z. Yu, and X. Zhou, “Opportunistic IoT: Exploring the harmonious interaction between human and the internet of things,” Journal of Network and Computer Applications, vol. 36, no. 6, pp. 1531–1539, Nov. 2013. http://dx.doi.org/10.1016/j.jnca.2012.12.028
  • 5. E. Fadda, G. Perboli, and R. Tadei, “A progressive hedging method for the optimization of social engagement and opportunistic IoT problems,” European Journal of Operational Research, vol. 277, no. 2, pp. 643–652, Sep. 2019. http://dx.doi.org/10.1016/j.ejor.2019.02.052
  • 6. E. Fadda, G. Perboli, and R. Tadei, “Customized multi-period stochastic assignment problem for social engagement and opportunistic IoT,” Computers & Operations Research, vol. 93, pp. 41–50, May 2018. http://dx.doi.org/10.1016/j.cor.2018.01.010
  • 7. E. Fadda, D. Mana, G. Perboli, and R. Tadei, “Multi period assignment problem for social engagement and opportunistic IoT,” in 2017 IEEE 41st Annual Computer Software and Applications Conference (COMP-SAC). IEEE, Jul. 2017. http://dx.doi.org/10.1109/compsac.2017.173
  • 8. W. M. Hanemann, “Willingness to pay and willingness to accept: how much can they differ?” The American Economic Review, vol. 81, no. 3, pp. 635–647, 1991.
  • 9. P. Li, H. Arellano-Garcia, and G. Wozny, “Chance constrained programming approach to process optimization under uncertainty,” Computers & chemical engineering, vol. 32, no. 1-2, pp. 25–45, 2008.
  • 10. G. A. Godfrey and W. B. Powell, “An adaptive, distribution-free algorithm for the newsvendor problem with censored demands, with applications to inventory and distribution,” Management Science, vol. 47, no. 8, pp. 1101–1112, Aug. 2001. http://dx.doi.org/10.1287/mnsc.47.8.1101.10231
  • 11. A. Cuzzocrea, E. Fadda, and A. Baldo, “Lyapunov central limit theorem: Theoretical properties and applications in big-data-populated smart city settings,” in 2021 5th International Conference on Cloud and Big Data Computing (ICCBDC). ACM, Aug. 2021. http://dx.doi.org/10.1145/3481646.3481652
Uwagi
1. The work has been supported by ULTRAOPTYMAL - Urban Logistics and sustainable TRAnsportation: OPtimization under uncertainTY and MAchine Learning, a PRIN2020 project funded by the Italian University and Research Ministry (grant n. 20207C8T9M, website: https://ultraoptymal.unibg.it).
2. Short article
3. Track 6: 15th International Workshop on Computational Optimization
4. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-4d1badf7-6839-4d14-9a90-2e8d6e81634a
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