• صفحه اصلی
  • ارائه روش نگاشت شناختی زی-آر نامبر برای مدل سازی روابط علّی استراتژی ها (مورد مطالعه: سازمان بیمه سلامت ایران)

اشتراک گذاری

آدرس مقاله


کد مقاله : IMJ-2006-1405 (R5) بازدید : 201 صفحه: 156 - 174

نوع مقاله: پژوهشی

ارائه روش نگاشت شناختی زی-آر نامبر برای مدل سازی روابط علّی استراتژی ها (مورد مطالعه: سازمان بیمه سلامت ایران)

محورهای موضوعی : مدیریت صنعتی

Mostafa Izadi 1 , Rassoul Noorossana 2 , Hamidreza Izadbakhsh 3 , Saber Saati 4 , Mohammad Khalilzadeh@srbiau.ac.ir 5

1 - گروه مهندسی صنایع، دانشکده فنی و مهندسی، دانشگاه ازاد اسلامی واحد علوم تحقیقات، تهران، ایران
2 - Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran
3 - Department of Industrial Engineering, Kharazmi University, Tehran, Iran.
4 - Department of Mathematics, Tehran-North Branch, Islamic Azad University, Tehran, Iran.
5 - Department of Industrial Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

تاریخ دریافت : 1399/04/07 تاریخ پذیرش : 1399/09/15 تاریخ انتشار : 1399/11/20

کلید واژه: ریسک, اعداد زی - آر, عدم اطمینان, استراتژی, نگاشت شناختی,

چکیده مقاله :

همواره در مسائل کیفی و غیرعددی و خبره محور با طیفی از ابهام، عدم اطمینان و ریسک در متغییرها مواجه هستیم. ریشه این ابهام می تواند در خود متغییر و یا سایر متغییرهای مرتبط با آن و اظهارات خبرگان باشد. نگاشت ادراکی فازی یکی از مدل های رایج برای شناخت بهتر مسائل است که به ارتباط علت و معلولی بین متغییرها توجه می کند. زمانی که با مسئله های سرکارداریم که داده های عددی مرتبط با آن در دسترس نباشد و یا ماهیت خود مسئله کیفی باشد، نگاشت ادارکی بوسیله اظهار نظر خبرگان ساخته می شود. یکی از مشکلات استفاده از مدل رایج نگاشت ادراکی در نظر نگرفتن عدم اطمینان، ریسک و خطا در اظهار نظر های خبرگان می باشد. این ایراد کیفیت و اعتبار مدلهای ایجاد شده در مسائل پیچیده را تحت تاثیر قرار می دهد. در این مقاله بمنظور کمک به درک صحیحی از مسئله و رفع عدم اطمینان، ابهام و ریسک در اظهار نظر خبرگان در خصوص متغییرها و ارتباطات علت ومعلولی بین آنها از رویکرد ترکیبی زی نامبر و آر نامبر در نگاشت ادراکی فازی استفاده شده است. روش پیشنهادی در این مقاله با لحاظ کردن خبرگان خوشبینانه، بدبینانه و بی طرف به مدل سازی روابط علت و معلولی میان استراتژی های موثر بر توانمندسازی افراد در نظام سلامت پرداخته است. رویکرد پیشنهادی این پژوهش می تواند بعنوان یک روش پشتیبان تصمیم در مسائلی که ماهیت کیفی داشته و خبره محورند موثر باشد.

چکیده انگلیسی:

We always face a range of ambiguity, uncertainty and risk in variables in qualitative, non-numerical and expert-centered issues. The root of this ambiguity can be in the variable itself or other variables related to it and the statements of experts. Fuzzy cognitive mapping is one of the common models for better understanding of problems that pays attention to the cause-and-effect relationship between variables. When dealing with problems where the numerical data associated with it are not available or the nature of the problem is qualitative, perceptual mapping is made by experts. One of the problems of using the common cognitive mapping model is not considering the uncertainty, risk and error in the opinions of experts. This problem affects the quality and validity of models created in complex problems. In this paper, in order to help understand the problem correctly and eliminate uncertainty, ambiguity and risk in experts' comments on variables and cause-and-effect relationships between them, the combined approach of Z-number and R-number in fuzzy cognitive mapping has been used. The proposed method in this article, by considering optimistic, pessimistic and neutral experts, has modeled the cause-and-effect relationships between strategies affecting the empowerment of individuals in the health system. The proposed approach of this research can be effective as a decision support method in issues that are qualitative and expert in nature.

منابع و مأخذ:


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_||_

  1. Amirkhani A, Mosavi MR, Mohammadi K, Papageorgiou EI. (2018). A novel hybrid method based on fuzzy cognitive maps and fuzzy clustering algorithms for grading celiac disease. Neural Computing and Applications. Sep 1;30(5):1573-88.
    2.
  2. Alipour M, Hafezi R, Amer M, Akhavan AN. (2017). A new hybrid fuzzy cognitive map-based scenario planning approach for Iran's oil production pathways in the post–sanction period. Energy. Sep 15;135:851-64.
    3.
  3. Avery, J. (2018). A Grounded Theory of Empowerment in Cancer Survivorship and Rehabilitation (Doctoral dissertation, Université d'Ottawa/University of Ottawa).
    4.
  4. Aguilar J. (2003). A dynamic fuzzy-cognitive-map approach based on random neural networks. International Journal of Computational Cognition. 1(4):91-107.
    5.
  5. Axelrod R, editor. (2015). Structure of decision: The cognitive maps of political elites. Princeton university press; Mar 8.
    6.
  6. Bottero M, Datola G, Monaco R. (2018 May). The use of fuzzy cognitive maps for evaluating the reuse project of military barracks in northern Italy. InInternational Symposium on New Metropolitan Perspectives  (pp. 691-699). Springer, Cham.
    7.
  7. Carvalho JP. (2019). On the Implementation of Evolving Dynamic Cognitive Maps. In International Fuzzy Systems Association World Congress. (pp. 201-213). Springer, Cham.
    8.
  8. Cerezo, P. G., Juvé-Udina, M. E., & Delgado-Hito, P. (2016). Concepts and measures of patient empowerment: a comprehensive review. Revista da Escola de Enfermagem da USP, 50(4), 667-674.
    9.
  9. Christens BD, Winn LT, Duke AM. (2016). Empowerment and critical consciousness: A conceptual cross-fertilization. Adolescent Research Review. 1(1):15-27.
    10.
  10. Christens, B. D., & Peterson, A. (2012). The role of empowerment in youth development: A study of sociopolitical control as mediator of ecological systems’ influence on developmental outcomes. Journal of Youth and Adolescence, 41(5), 623–635.
    11.
  11. Eyüboğlu, E., & Schulz, P. J. (2016). Do health literacy and patient empowerment affect self-care behaviour? A survey study among Turkish patients with diabetes. BMJ open, 6(3), e010186.
    12.
  12. Flaherty A, O'Dwyer A, Mannix-McNamara P, Leahy JJ. (2017). The influence of psychological empowerment on the enhancement of chemistry laboratory demonstrators' perceived teaching self-image and behaviours as graduate teaching assistants. Chemistry Education Research and Practice. 18(4):710-36.
    13.
  13. Grabe, S. (2012). An empirical examination of women’s empowerment and transformative change in the context of international development. American Journal of Community Psychology, 49(1–2): 233–245.
    14.
  14. Gray, S., Gray, S., De Kok, J. L., Helfgott, A., O'Dwyer, B., Jordan, R., & Nyaki, A.        (2015). Using fuzzy cognitive mapping as a participatory approach to analyze change,     preferred states, and perceived resilience of social-ecological systems. Ecology and Society, 20(2).
    15.
  15. Iakovidis DK, Papageorgiou E. (2010). Intuitionistic fuzzy cognitive maps for medical decision making. IEEE Transactions on Information Technology in Biomedicine. 15(1):100-7.
    16.
  16. Izadbakhsh HR, Barzegar B, Zarinbal M, Ataeipoor S, Tehrani NA, Mohseni A. Insured Empowermwnt (2015). An Approach for Iran Health System. Pub, Kharazmi University. (in Persian).
    17.
  17. Jo, S. J., & Park, S. (2016). Critical review on power in organization: empowerment in human resource development. European Journal of Training and Development.
    18.
  18. John, J. R., Ghassempour, S., Girosi, F., & Atlantis, E. (2018). The effectiveness of patient-centred medical home model versus standard primary care in chronic disease management: protocol for a systematic review and meta-analysis of randomised and non-randomised controlled trials. Systematic reviews, 7(1), 215.
    19.
  19. Kang, B., Wei, D., Li, Y. and Deng, Y. (2012). Decision making using Z-numbers under uncertain environment. Journal of computational Information systems, 8(7), pp. 2807-2814.
    20.
  20. Kalantari T, Khoshalhan F. (2018). Readiness assessment of leagility supply chain based on fuzzy cognitive maps and interpretive structural modeling: a case study. Journal of Business & Industrial Marketing.
    21.
  21. Kosko B.( 1986). Fuzzy cognitive maps. International journal of man-machine studies. Jan 1;24(1):65-75.
    22.
  22. Kottas TL, Boutalis YS, Christodoulou MA. (2007). Fuzzy cognitive network: A general framework. Intelligent Decision Technologies. 1(4):183-96.
    23.
  23. Köhler, A. K., Tingström, P., Jaarsma, T., & Nilsson, S. (2018). Patient empowerment and general self-efficacy in patients with coronary heart disease: a cross-sectional study. BMC family practice, 19(1), 76.
    24.
  24. Lardier DT, Garcia-Reid P, Reid RJ. (2019). The examination of cognitive empowerment dimensions on intrapersonal psychological empowerment, psychological sense of community, and ethnic identity among urban youth of color. The Urban Review. 51(5):768-88.
    25.
  25. Laschinger HKS, Wong CA, Grau AL. (2013). Authentic leadership, empowerment and burnout: A comparison in new graduates and experienced nurses. Journal of Nursing Management, 21(3): 541–552.
    26.
  26. Martinez P, Blanco M, Castro-Campos B. (2018). The water–energy–food nexus: a fuzzy-cognitive mapping approach to support nexus-compliant policies in Andalusia (Spain). Water. 10(5):664.
    27.
  27. Madmoli, M. (2019). A systematic Review Study on the Results of Empowerment-Based Interventions in Diabetic Patients. International Research in Medical and Health Science, 2(1), 1-7.
    28.
  28. Miao Y, Liu ZQ, Siew CK, Miao CY. (2001). Dynamical cognitive network-an extension of fuzzy cognitive map. IEEE transactions on Fuzzy Systems. 9(5):760-70.
    29.
  29. Morley J, Floridi L. (2019). Enabling digital health companionship is better than empowerment. The Lancet Digital Health. 1(4): 155-6.
    30.
  30. Najafi A, Amirkhani A, Papageorgiou EI, Mosavi MR. (2017). Medical decision making based on fuzzy cognitive map and a generalization linguistic weighted power mean for computing with words. In2017 IEEE international conference on fuzzy systems (FUZZ-IEEE) (pp. 1-6). IEEE.
    31.
  31. Papageorgiou EI. (2011). Learning algorithms for fuzzy cognitive maps—a review study. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews). 42(2):150-63.
    32.
  32. Papageorgiou E, Stylios C, Groumpos P. (2003). Fuzzy cognitive map learning based on nonlinear Hebbian rule. In Australasian Joint Conference on Artificial Intelligence (pp. 256-268). Springer, Berlin, Heidelberg.
    33.
  33. Papageorgiou EI, Salmeron JL. (2012). A review of fuzzy cognitive maps research during the last decade. IEEE Transactions on Fuzzy Systems. 21(1):66-79.
    34.
  34. Papageorgiou E, Kontogianni A. (2012). Using fuzzy cognitive mapping in environmental decision making and management: a methodological primer and an application. International Perspectives on Global Environmental Change.  427-50.
    35.
  35. Tengland PA. (2016). Behavior change or empowerment: On the ethics of health-promotion goals. Health Care Analysis. 24(1): 24–46.
    36.
  36. Osoba OA, Kosko B.( 2017). Fuzzy cognitive maps of public support for insurgency and terrorism. The Journal of Defense Modeling and Simulation. Jan;14(1):17-32.
    37.
  37. Rodriguez-Repiso, L., Setchi, R., & Salmeron, J. L. (2007). Modelling IT projects success with fuzzy cognitive maps. Expert Systems with Applications, 32(2), 543-559.
    38.
  38. Salmeron JL, Vidal R, Mena A. (2012). Ranking fuzzy cognitive map based scenarios with TOPSIS. Expert Systems with Applications. 39(3):2443-50.
    39.
  39. Salmeron JL. (2010). Modelling grey uncertainty with fuzzy grey cognitive maps. Expert Systems with Applications. 37(12):7581-8.
    40.
  40. Seiti H, Hafezalkotob A, Martínez L. (2019). R-numbers, a new risk modeling associated with fuzzy numbers and its application to decision making. Information Sciences 483, 206-31.
    41.
  41. Seiti H, Fathi M, Hafezalkotob A, Herrera-Viedma E, Hameed IA. (2020). Developing the modified R-numbers for risk-based fuzzy information fusion and its application to failure modes. effects, and system resilience analysis (FMESRA), ISA transactions.
    42.
  42. Shin S, Park H. (2017). Effect of empowerment on the quality of life of the survivors of breast cancer: The moderating effect of self‐help group participation. Japan Journal of Nursing Science. 14(4):311-9.
    43.
  43. Shaukat, N., Ali, S. M., Mehmood, C. A., Khan, B., Jawad, M., Farid, U & Majid, M. (2018). A survey on consumers empowerment, communication technologies, and renewable generation penetration within Smart Grid. Renewable and Sustainable Energy Reviews, 81, 1453-1475.
    44.
  44. Syahza, A., & Asmit, B. (2019). Regional economic empowerment through oil palm economic institutional development. Management of Environmental Quality: An International Journal.
    45.
  45. Song HJ, Miao CY, Wuyts R, Shen ZQ, D’Hondt M, Catthoor F.( 2010). An extension to fuzzy cognitive maps for classification and prediction. IEEE Transactions on Fuzzy Systems. 19(1):116-35.
    46.
  46. Wei Z, Lu L, Yanchun Z. (2008). Using fuzzy cognitive time maps for modeling and evaluating trust dynamics in the virtual enterprises. Expert Systems with Applications. 1;35(4):1583-92.
    47.
  47. WHO. WHO Globl Strategy on People-centred and Integrated Health Services. 2014. Available: http://www.who.int/servicedeliverysafety/areas/people-centred-car/en/.[Accessed:24-Jul-2014].
    48.
  48. Yeh, M. Y., Wu, S. C., & Tung, T. H. (2018). The relation between patient education, patient empowerment and patient satisfaction: A cross-sectional-comparison study. Applied Nursing Research, 39, 11-17.
    49.
  49. Zadeh, L. A. (2011). A note on Z-numbers. Information Sciences, 181(14), 2923-2932.
    50.

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