Complex Models of Ordering Multi-Sequences with Fuzzy Parameters

Authors

DOI:

https://doi.org/10.15802/stp2021/237291

Keywords:

constructive modeling, multi-sequences, sequence ordering, multilayer models, fuzzy parameters, formation operations complexity, fuzzy classification, clinical monitoring, individual fuzzy process models

Abstract

Purpose. The aim of the article is to develop complex constructive mathematical models of ordering processes for multi-sequences of elements with fuzzy parameters. At the same time, the following requirements for fuzzy ordering of multi-sequences with complexity evaluation (FOMSCE) were established: accounting fuzzy estimates of the formation operations complexity, the need to define fuzzy classes for ordering the initial elements, as well as building individual fuzzy models for the processes of receiving orders from different sources. Methodology. To solve the problems of optimal planning of non-deterministic processes of clinical monitoring of the patients’ treatment, the formation of complex constructive mathematical models of the processes of ordering multi-sequences of elements with fuzzy FMLCPM parameters was applied. For forming models of FOMSCE tasks, a methodology is used to create models with multilayer structures. To implement fuzzy problems, methods and procedures for discretizing a system of fuzzy quantities using sets of α-levels are applied. Findings. The article proposes an approach to solving the problems of analysis and optimal planning of the processes of clinical monitoring of the patients’ treatment, represented as flow control in service systems under uncertainty. For its formalization and implementation, complex multilayer constructive-production models for ordering multi-sequences with fuzzy parameters have been developed. Originality. The work has developed constructive-production methods for modeling complex systems, presented in the form of a multilayer model FMLCPM, which are designed for the processes of ordering multi-sequences of elements with fuzzy parameters. In FMLCPM, layer models are proposed that provide accounting for fuzzy estimates of the complexity of ordering operations, classification of fuzzy parameters of output elements, the formation and analysis of individual fuzzy models of the processes of receipt of orders in service systems. Practical value. The practical value of the results obtained lies in the spectrum development of applications of the problems of optimal planning of the processes in the service systems, presented as an ordering of multi-sequences with fuzzy parameters. The complex models of FOMSCE processes developed in the article are suitable and effective for formalizing the tasks of analysis and optimal planning of clinical monitoring processes, as well as a wide range of other tasks for monitoring non-deterministic transport processes, logistics and service systems.

References

Zhukovyts’kyy, I. V., Skalozub, V. V., Ustenko, A. B., & Klymenko, I. V. (2018). Formation of intelligent infor-mation technologies of railway transport based on models of analytical servers and ontological systems. Information and control systems at railway transport, 6, 3-11. DOI: https://doi.org/10.18664/ikszt.v0i6.151635 (in Ukrainian)

Kormen, T., Leyzerson, Ch., Rivest, R., & Shtayn, K. (2010). Kharakteristiki Algoritmy: postroenie i analiz (Vol. 2). Moscow: Izdatelskiy dom «Vilyams». (in Russian)

Pegat, A. (2009). Nechetkoe modelirovanie i upravlenie. Moscow: BINOM. (in Russian)

Skalozub, V., Biliy, B., Galabut, A., & Murashov, O. (2020). Methods of intelligent modeling of processes with a variable observation interval and constructive ordering «with weight». System Technologies, 3(128), 127-143. DOI: https://doi.org/10.34185/1562-9945-3-128-2020-12 (in Ukrainian)

Skalozub, V. V., Ilman, V. M., & Bilyy, B. B. (2020). Constructive multiplayer models for ordering a set of se-quences, taking into account the complexity operations of formations. Science and Transport Progress, 4(88), 61-76. DOI: https://doi.org/10.15802/stp2020/213232 (in Ukrainian)

Skalozub, V., & Murashov, O. (2021). Modeling of monitoring processes with uneven and fuzzy observation intervals. System Technologies, 4(135), 135-148. DOI: https://doi.org/10.34185/1562-9945-4-135-2021-14 (in Ukrainian)

Yakhyaeva, G. E. (2012). Nechetkie mnozhestva i neyronnye seti. Moscow: BINOM. (in Russian)

Bernal, J. L., Cummins, S., & Gasparrini, A. (2017). Interrupted time series regression for the evaluation of public health interventions: a tutorial. International Journal of Epidemiology, 46(1), 348-355. DOI: https://doi.org/10.1093/ije/dyw098 (in English)

Chen, S.-M. (1996). Forecasting enrollments based on fuzzy time series. Fuzzy Sets and Systems, 81(3), 311-319. DOI: https://doi.org/10.1016/0165-0114(95)00220-0 (in English)

Chen, S.-M., & Phuong, B. D. H. (2017). Fuzzy time series forecasting based on optimal partitions of intervals and optimal weighting vectors. Knowledge-Based Systems, 118, 204-216. DOI: https://doi.org/10.1016/j.knosys.2016.11.019 (in English)

Gao, R., & Duru, O. (2020). Parsimonious fuzzy time series modelling. Expert Systems with Applications, 156, 113447. DOI: https://doi.org/10.1016/j.eswa.2020.113447 (in English)

Koenker, R. (2005). Quantile Regression (pp. 137-143). Cambridge University Press. DOI: https://doi.org/10.1017/cbo9780511754098 (in English)

Kozachenko, D., Bobrovskiy, V., Gera, B., Skovron, I., & Gorbova, A. (2020). An optimization method of the multi-group train formation at flat yards. International Journal of Rail Transportation, 9(1), 61-78. DOI: https://doi.org/10.1080/23248378.2020.1732235 (in English)

Möller-Levet, C. S., Klawonn, F., Cho, K.-H., & Wolkenhauer, O. (2003). Fuzzy Clustering of Short Time-Series and Unevenly Distributed Sampling Points. Advances in Intelligent Data Analysis V, 330-340. DOI: https://doi.org/10.1007/978-3-540-45231-7_31 (in English)

Pardo-Igúzquiza, E., & Rodríguez-Tovar, F. J. (2012). Spectral and cross-spectral analysis of uneven time series with the smoothed Lomb-Scargle periodogram and Monte Carlo evaluation of statistical significance. Computers & Geosciences, 49, 207-216. DOI: https://doi.org/10.1016/j.cageo.2012.06.018 (in English)

Shang, Z., & Li, M. (2016). Feature Selection Based on Grouped Sorting. 2016 9th International Symposium on Computational Intelligence and Design (ISCID) (pp. 451-454). DOI: https://doi.org/10.1109/ISCID.2016.1111 (in English)

Shynkarenko, V. I., & Ilman, V. M. (2014). Constructive-Synthesizing Structures and Their Grammatical Inter-pretations. i. Generalized Formal Constructive-Synthesizing Structure. Cybernetics and Systems Analysis, 50(5), 655-662. DOI: https://doi.org/10.1007/s10559-014-9655-z (in English)

Tahseen, A. J., Aqil, S. B., & Cemal, A. (2008). A New Quantile Based Fuzzy Time Series Forecasting Model. International Scholarly and Scientific Research & Innovation, 2(3), 995-1001. (in English)

Tricahya, S., & Rustam, Z. (2019). Forecasting the Amount of Pneumonia Patients in Jakarta with Weighted High Order Fuzzy Time Series. IOP Conference Series: Materials Science and Engineering (Vol. 546, Iss. 3, pp. 1-10). DOI: https://doi.org/10.1088/1757-899x/546/5/052080 (in English)

Yadavalli, V. S. S., & Balcou, C. (2017). A supply chain management model to optimise the sorting capability of a «third party logistics» distribution centre. South African Journal of Business Management, 48(1), 77-84. DOI: https://doi.org/10.4102/sajbm.v48i1.22 (in English)

Zhukovyts’kyy, I. (2018). Use of an automaton model for the designing of real-time information systems in the railway stations. Transport Problems, 12(4), 101-108. DOI: https://doi.org/10.20858/tp.2017.12.4.10 (in English)

Published

2021-04-15

How to Cite

Skalozub, V. V., Horiachkin, V. M., & Murachov, O. V. (2021). Complex Models of Ordering Multi-Sequences with Fuzzy Parameters. Science and Transport Progress, (2(92), 50–64. https://doi.org/10.15802/stp2021/237291

Issue

Section

INFORMATION AND COMMUNICATION TECHNOLOGIES AND MATHEMATICAL MODELING