Abstract
In this paper a network activity planning method based on fuzzy-interval scheduling graphs is introduced. The network activity schedule implementation allows considering temporal-constrained schedules and determining the necessary resources allocation plan for the activities. A case-study example introduces and estimates three methods for constructing and calculating the main indicators necessary for analyzing the scheduling problem. Three mathematical formulations are considered: a crisp temporal statement, a probabilistic model and a newly introduced fuzzy-interval problem formulation for representing the basic temporal parameters of the model. Comparison of the calculation results is presented in this paper, the time lags between activities of the considered network activity model are considered as well. The degree of optimality for the temporal resources allocation for the activities is also considered. The results of comparison for the three methods show that network activity planning in a fuzzy-interval problem formulation provides the best conditions for optimization and transparency of the production process.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhang, Y.: WiMAX Network Activity Planning and Optimization. Auerbach Publications, Boca Raton (2009)
Mishra, A.: Fundamentals of Network activity Planning and Optimization 2G/3G/4G: Evolution to 5G, 2nd edn. Wiley, New York (2018)
Giuliani, M., Castelletti, A.: Is robustness really robust? How different definitions of robustness impact decision-making under climate change. Clim. Change 135(3–4), 409–424 (2016). https://doi.org/10.1007/s10584-015-1586-9
Larsson, C.: 5G Network Activities: Planning. Design and Optimization. Academic Press, London (2018)
Seybold, P.M.: Algorithm engineering in geometric network activity planning and data mining. University of Stuttgart (2018)
Stryczek, R.: Petri Nets for Computer Aided Group Technology. In: Zawiślak, S., Rysiński, J. (eds.) Graph-Based Modelling in Engineering. MMS, vol. 42, pp. 143–164. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-39020-8_11
Zhou, K.-Q., Zain, A.M.: Fuzzy Petri nets and industrial applications: a review. Artif. Intell. Rev. 45(4), 405–446 (2015). https://doi.org/10.1007/s10462-015-9451-9
Yue, W., Liu, X., Li, S., Gui, W., Xie, Y.: Knowledge representation and reasoning with industrial application using interval-valued intuitionistic fuzzy Petri nets and extended TOPSIS. Int. J. Mach. Learn. Cybern. 12(4), 987–1013 (2020). https://doi.org/10.1007/s13042-020-01216-1
Li, Z., Hu, Z., Li, C.: SRv6 Network Activity Programming: Ushering in a New Era of IP Network activitys. CRC Press, New York (2021)
Kerzner, H.: Project Management: A Systems Approach to Planning, Scheduling, and Controlling. Wiley, New York (2003)
Marchau, V.A.W.J., Walker, W.E., Bloemen, P.J.T.M., Popper, S.W.: Decision Making under Deep Uncertainty: From Theory to Practice. Springer, Heidelberg (2019)
Gil-Garcia, J.R., Pardo, T.A., Luna-Reyes, L.F.: Policy Analytics, Modelling, and Informatics: Innovative Tools for Solving Complex Social Problems. Springer, Berlin (2018)
Melin, P., Castillo, O., Kacprzyk, J.: Design of Intelligent Systems Based on Fuzzy Logic, Neural Network and Nature-Inspired Optimization. Studies in Computational Intelligence, Springer (2015)
Nedosekin, A.O., Shmatko, A.D., Abdoulaeva, Z.I.: Fuzzy preliminary evaluation of industrial risks. In Proceedings of 20th IEEE International Conference on Soft Computing and Measurements (SCM 2017), pp.750 - 751 (2017)
Kosenko, O., Bozhenyuk, A., Belyakov, S., Knyazeva, M.: Optimization of Spatial-Time Planning Resource Allocation Under Uncertainty. In: Kahraman, C., Cevik Onar, S., Oztaysi, B., Sari, I.U., Cebi, S., Tolga, A.C. (eds.) INFUS 2020. AISC, vol. 1197, pp. 1475–1482. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-51156-2_171
Dubois, D., Prade, H.: Possibility Theory. Plenum Press, New-York (1988)
Kosenko, O.V., Sinyavskaya, E.D., Shestova, E.A., Kosenko, E.Y., Antipin, S.O.: Method of rational placement of intermediate centers with setting parameters in the form of the fuzzy intervals. In: Proceedings of 19th IEEE International Conference on Soft Computing and Measurements (SCM 2016), pp. 186–189, (2016).
Matveev, M.G., Shevlyakov, A.O., Semenov, M.E., Meleshenko P.A.: Solution of selection problems with fuzzy parameters. In: Proceedings of International Multidisciplinary Scientific GeoConference Surveying Geology and Mining Ecology Management (SGEM), vol. 17, no. 21, pp. 595–602 (2017)
Shevlyakov, A.O., Matveev, M.G.: W-algebra for solving problems with fuzzy parameters. J. Phys. Conf. Ser. 973(1), 012044 (2018)
Acknowledgments
The reported study was funded by the Russian Foundation for Basic Research according to the research project N20–01-00197.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Bozhenyuk, A., Dolgiy, A., Kosenko, O., Knyazeva, M. (2021). The Comparative Approach to Solving Temporal-Constrained Scheduling Problem Under Uncertainty. In: Batyrshin, I., Gelbukh, A., Sidorov, G. (eds) Advances in Soft Computing. MICAI 2021. Lecture Notes in Computer Science(), vol 13068. Springer, Cham. https://doi.org/10.1007/978-3-030-89820-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-89820-5_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-89819-9
Online ISBN: 978-3-030-89820-5
eBook Packages: Computer ScienceComputer Science (R0)