Zeszyty Naukowe Uniwersytetu Szczecińskiego. Studia Informatica

Aktualnie: Studia Informatica Pomerania

ISSN: 0867-1753     eISSN: 2300-410X     DOI: 10.18276/si.2015.38-11
CC BY-SA   Open Access 

Lista wydań / ZN 878 SI nr 38
Identyfikacja ekspertowego modelu decyzyjnego w problemach wielokryterialnych z zastosowaniem metody obiektów charakterystycznych

Rok wydania:2015
Liczba stron:14 (145-158)
Słowa kluczowe: wielokryterialne wspomaganie procesu decyzyjnego metoda obiektów charakterystycznych teoria zbiorów rozmytych zjawisko rank reversal metoda COMET
Autorzy: Wojciech Sałabun
Zachodniopomorski Uniwersytet Technologiczny w Szczecinie, Wydział Informatyki

Abstrakt

W artykule przedstawiono nowe podejście do rozwiązywania wielokryterialnych problemów decyzyjnych, polegające na identyfikacji ekspertowego modelu decyzyjnego w przestrzeni stanu problemu. Metoda obiektów charakterystycznych identyfikuje model decyzyjny z wykorzystaniem stałych punktów odniesienia oraz teorii zbiorów rozmytych. Metoda ta jest całkowicie odporna na zjawisko rank reversal, czyli odwracania rankingów przy dodaniu nowej alternatywy lub w momencie usunięcia alternatywy ze zbioru już rozpatrywanych obiektów. Za pomocą metody obiektów charakterystycznych identyfikowany jest model oceny ryzyka wystąpienia ataku serca u pacjenta w okresie najbliższych 10 lat, w celu lepszego zobrazowania działania metody COMET.
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Bibliografia

1.Afshari A., Mojahed M., Yusuff R. (2010), Simple Additive Weighting approach to Personnel Selection problem, „International Journal of Innovation, Management andTechnology”, vol. 1, iss. 5, s. 511–515.
2.Amaral T.M., Costa A.P.C. (2014), Improving decision-making and management of hospital resources: An application of the PROMETHEE II method in an Emergency Department, „Operations Research for Health Care”, vol. 3, no. 1, s. 1–6.
3.Blair A.R., Mandelker G.N., Saaty T.L., Whitaker R. (2010), Forecasting the resurgence of the u.s. economy in 2010: An expert judgment approach, „Socio-Economic Planning Sciences”, vol. 44, no. 3, s. 114–121.
4.Brito A.J., de Almeida A.T., Mota C.M. (2010), A multicriteria model for risk sorting of natural gas pipelines based on ELECTRE TRI integrating utility theory, „European Journal of Operational Research”, vol. 200, no. 3, s. 812–821.
5.Dong Y., Zhang G., Hong W.C., Xu Y. (2010), Consensus models for AHP group decision making under row geometric mean prioritization method, „Decision Support Systems”, vol. 49, no. 3, s. 281–289.
6.Eppe S., De Smet Y. (2014), Approximating Promethee IIs net flow scores by piecewise linear value functions, „European Journal of Operational Research”, vol. 233, no. 3, s. 651–659.
7.French S. (2009), Decision behavior, analysis and support, Cambridge, New York.
8.Goodwin P., Wright G. (2009), Decision Analysis for Management Judgment, John Wiley & Sons, Chichester.
9.Hatami-Marbini A., Tavana M. (2011), An extension of the ELECTRE I method for group decision-making under a fuzzy environment, „Omega, vol. 39, no. 4, s. 373–386.
10.Huang Y.S., Chang W.C., Li W.H., Lin Z.L. (2013), Aggregation of utility-based individual preferences for group decision-making, „European Journal of Operational Research”, vol. 229, no. 2, s. 462–469.
11.Kim Y., Chung E.S., Jun S.M., Kim S.U. (2013), Prioritizing the best sites for treated wastewater instream use in an urban watershed using fuzzy TOPSIS, „Resources Conservation and Recycling”, vol. 73, s. 23–32.
12.Kumar A., Singh P., Kaur A., Kaur P. (2010), RM approach for ranking of generalized trapezoidal Fuzzy numbers, „Fuzzy Information and Engineering”, vol. 2, iss. 1, s. 37–47.
13.Kuo R.J., Wu Y.H., Hsu T.S. (2012), Integration of fuzzy set theory and TOPSIS into HFMEA to improve outpatient service for elderly patients in Taiwan, „Journal of the Chinese Medical Association”, vol. 75, no. 7, s. 341–348.
14.Kwanyoung I., Hyunbo C. (2013), A systematic approach for developing a new business model using morphological analysis and integrated fuzzy approach, „Expert Systems with Applications”, vol. 40, no. 11, s. 4463–4477.
15.La Scalia G., Aiello G., Rastellini C., Micale R., Cicalese L. (2011), Multi-criteria decision making support system for pancreatic islet transplantation, „Expert Systems with Applications”, vol. 38, no. 4, s. 3091–3097.
16.Makan A., Mountadar M. (2013), Sustainable management of municipal solid waste in Morocco: Application of PROMETHEE method for choosing the optimal management scheme, „African Journal of Environmental and Waste Management”, vol. 1, no. 1, s. 1–13.
17.Montazer G.A., Saremi H.Q., Ramezani M. (2009), Design a new mixed expert decision aiding system using fuzzy ELECTRE III method for vendor selection, „Expert Systems with Applications”, vol. 36, no. 8, s. 10837–10847.
18.Mosavi A. (2014), Decision-Making in Complicated Geometrical Problems, „International Journal of Computer Applications”, vol. 87, iss. 19, s. 22–25.
19.NIH National Heart, Lung, Blood Institute, http://cvdrisk.nhlbi.nih.gov (15.05. 2015).
20.Norese M.F., Carbone V. (2014), An Application of ELECTRE Tri to Support Innovation, „Journal of Multi-Criteria Decision Analysis”, vol. 21, iss. 1–2, s. 77–93.
21.Pedrycz W., Ekel P., Parreiras R. (2011), Fuzzy Multicriteria Decision-making: Models, Methods and Applications, John Wiley & Sons, Chichester.
22.Piegat A. (2009), Modelowanie i sterowanie rozmyte, Akademicka Oficyna Wydawnicza EXIT, Warszawa.
23.Saaty T.L., Brandy C. (2009), The encyclicon, volume 2: a dictionary of complex decisions using the analytic network process, RWS Publications, Pittsburgh.
24.Saaty T.L., Shang J.S. (2011), An innovative orders-of-magnitude approach to AHP-based mutli-criteria decision making: Prioritizing divergent intangible humane acts, „European Journal of Operational Research”, vol. 214, no. 3, s. 703–715.
25.Salih Y., See O., Ibrahim R., Yussof S., Iqbal A. (2015), A Novel Noncooperative Game Competing Model Using Generalized Simple Additive Weighting Method to Perform Network Selection in Heterogeneous Wireless Networks, „International Journal of Communicati
26.Sałabun W. (2012), The use of Fuzzy logic to evaluate the nonlinearity of human multicriteria used in decision making, „Przegląd Elektrotechniczny”, vol. 88, iss. 10b, s. 235–238.
27.Sałabun W. (2013), The mean error estimation of TOPSIS method using a fuzzy reference models, „Journal of Theoretical and Applied Computer Science”, vol. 7, no. 3, s. 40–50.
28.Sałabun W. (2014a), Application of the Fuzzy Multi-criteria Decision-Making Method to Identify Nonlinear Decision Models, „International Journal of Computer Applications”, vol. 89, iss. 15, s. 1–6.
29.Sałabun W. (2014b), Reduction in the Number of Comparisons Required to Create Matrix of Expert Judgment in the Comet Method, „Management and Production Engineering Review”, vol. 5, iss. 3, s. 62–69.
30.Sałabun W. (2015), The Characteristic Objects Method: A New Distance-based Approach to Multicriteria Decision-making Problems, „Journal of Multi‐Criteria Decision Analysis”, vol. 22, iss. 1–2, s. 37–50.
31.Sun Y.F., Liang Z.S., Shan C.J., Viernstein H., Unger F. (2011), Comprehensive evaluation of natural antioxidants and antioxidant potentials in Ziziphus jujuba Mill. var. spinosa (Bunge) Huex H. F. Chou fruits based on geographical origin by TOPSIS method
32.Triantaphyllou E., Mann S.H. (1989), An Examination of the Effectiveness of Multi-Dimensional Decision-Making Methods: A Decision-Making Paradox, „International Journal of Decision Support Systems”, vol. 5, s. 303–312.
33.Wang G., Wang H. (2001), Non-fuzzy versions of fuzzy reasoning in classical logics, „Information Sciences”, vol. 138, iss. 1–4, s. 211–236.
34.Zimmermann H.J. (2001), Fuzzy Set Theory – and Its Applications, Kluwer, Boston.
35.Ziolkowska J.R. (2013), Evaluating sustainability of biofuels feedstocks: A multiobjective framework for supporting decision-making, „Biomass and Bioenergy”,
36.vol. 55, s. 425–440.