Neuro fuzzy genetic algorithm of optimization of rehabilitation procedures

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Ковалишин О. С. № 2 (57) 72-81 Image Image

The high level and complexity of domestic injuries are associated with an increase in the number of road injuries and the use of modern mechanisms in domestic conditions, non-compliance with safety rules. Modern injuries have a poly structured character, which causes their severe con­se­quences and difficulties in the different stages of rehabilitation. In this regard, in the process of rehabilitation, special attention is given to the issue of individually oriented rehabilitation services, since at present plans for rehabilitation therapy are very limited to the interests of patients themselves, which directly affects the quality of treatment.

In order to solve the problem, it is necessary to take into account the set of restrictions imposed on the process — the need to consolidate schedules of the medical equipment usage, the availability of qualified medical personnel for each specific procedure at a certain time, the availability of off-the-shelf equipment, etc.

In fact, to solve this problem, it is necessary to construct a full functioning schedule of the medical institution, which is superimposed on the set of hard restrictions — the conditions that must necessarily be fulfilled to ensure the correctness of the schedule, and soft re­quirements — the implementation of which is desirable — the actual interests of patients. The following methods are used to solve the problem of constructing a schedule for medical institutions: full selection, method of branches and boundaries, logical programming with constraints, graph coloring, simulated annealing, simulation modeling, genetic algorithm, etc.

Regarding other methods, genetic algorithms have several advantages, such as: simultaneous use of several points of the search space, the use of probabilistic and deterministic rules for the transition between solutions, the absence of the need to use an external extra data and operations. Therefore, it has been suggested to use genetic algorithms to solve the problem of multi-criteria optimization of rehabilitation therapy plans. To assess the progress of optimization, a mechanism for evaluating alternatives generated during the work of evolution operators has been constructed. A fuzzy logic controller, the T-Controller, has several advantages over classical methods: high ac­cu­racy of computations, zero methodological error, high speed of operation.

Indicators based on fines imposed for violation of a certain criterion are used as in­put arguments of the controller. For the organization of the optimization process, the mecha­nisms of population development — crossover and mutation operators have been developed.

Keywords: multicriteria optimization, genetic algorithms, fuzzy logic, rehabilitation therapy.

doi: 10.32403/1998-6912-2018-2-57-72-81


  • 1. Khobzei, M. K., Ipatov, A. V., Korobkin, Yu. I., & Moroz, O. M. (2010). Analiz suchasnoho stanu reabilitatstsii invalidiv v Ukraini: Medychni perspektyvy, 4 (in Ukrainian).
  • 2. Ipatov, A. V., Serhiieni, T. H., & Voitchak, O. V. (2005). Profesiina reabilitatsiia invalidiv. Dnipropetrovsk: Porohy (in Ukrainian).
  • 3. Ipatov, A. V., Moroz, O. M., Holyk, V. A., Perepelychna, R. Ya., Khaniukova, I. Ya., Ko­rob­kin, Yu. I., Molchanov, R. M., Malovychko, H. M., Hondulenko, N. O., & Sanina, N. A. (2015). Osnovni pokaznyky invalidnosti ta diialnosti medyko-sotsialnykh ekspertnykh ko­misii Ukrainy za 2014 rik: Osnovni pokaznyky invalidnosti ta diialnosti medyko-sotsialnykh ekspertnykh komisii Ukrainy za 2014 rik: Analityko-informatsiinyi dovidnyk. Dnipropetrovsk: Roial-Prynt (in Ukrainian).
  • 4. Koffman, E. G. (1984). Teoriia raspisaniĭ i vychislitelnye mashiny. Moskva: Nauka (in Rus­sian).
  • 5. Glibovetc, H. N., & Medvіd, S. A. (2003). Genetichiskie agoritmy i ikh ispolzovanie dlia resheniia zadachi sostavleniia raspisaniia: Kibernetika i sistemnyĭ analiz, 1, 95–108 (in Russian).
  • 6. Tkachenko, R. O., & Kovalyshyn, O. S. (2016). Method of making up clinic schedule with use of finite-state automaton: Econtechmod. An international quarterly journal, 5, 3, 131–134 (in English).
  • 7. Hosny, M., & Shameem, F. (2011). A Survey of Genetic Algorithms for the University Timetabling Problem: International Conference on Future Information Technology. Singpore, 13, 34–39 (in English).
  • 8. Doroshenko, A. V., & Tkachenko, R. O. (2009). Neiropodibni struktury mashyny heomet­rychnykh peretvoren u zavdanniakh intelektualnoho analizu danykh. Mizhnarodna naukova konferentsiia «Intelektualni systemy pryiniattia rishen i problemy obchysliuvalnoho intelektu» ISDMCI’2009: zb. nauk. pr. u 2 t., 18-22 trav. 2009 r. Yevpatoriia, Ukraina. Kherson, T. 2, 309–314 (in Ukrainian).
  • 9. Tkachenko, O., Tkachenko, R., Hirniak, Yu., & Mushenyk, P. Rule based Fuzzy system of Improved accuracy: Proceeding of the 56-th international Colloquium: Innovation of Me­chanical Endineering – Shaping the Future. Ilmenau University of Technology, 1–6 (in English).
  • 10. Verbenko, I., & Tkachenko, R. (2013). Fuzzy Methods and Tools for Grane Management System Based on T-Controller: Journal of Global Research in Computer Science, 4, 3, 1–4 (in English).
  • 11. T-Controller Workshop. Retrieved from http://tkatchenko.com/t-controller/about-t-controller/ (stanom na 21.05.2018) (in English).
  • 12. Odim, O. M., Oguntunde, B. O., & Alli, O. O. (2013). On the Fitness Measure of Genetic Algorithm for Generating Institutional Lecture Timetable: Journal of Emerging Trends in Computing and Information Since, 4, 4, 436–444.0 (in English).