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How to cite this paper
APA: Babatunde, S & Ighravwe, D. (2019). A fuzzy multi-criteria approach for hosting-right selection: A case study of sport event. International Journal of Data and Network Science, 3(1), 1-12.
Chicago/Turabian: Babatunde, S & Ighravwe, D. 2019. "A fuzzy multi-criteria approach for hosting-right selection: A case study of sport event." International Journal of Data and Network Science 3, no. 1 (2019): 1-12.
AMA: Babatunde, S & Ighravwe, D. A fuzzy multi-criteria approach for hosting-right selection: A case study of sport event. International Journal of Data and Network Science. 2019;3(1):1-12.
References
Askarifar, K., Motaffef, Z., & Aazaami, S. (2018). An investment development framework in Iran's seashores using TOPSIS and best-worst multi-criteria decision making methods. Decision Science Letters, 7(1), 55-64.
Govindan, K., Khodaverdi, R., & Jafarian, A. (2013). A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. Journal of Cleaner production, 47, 345-354.
Hiller, H. H. (2000). Mega‐events, urban boosterism and growth strategies: an analysis of the objectives and legitimations of the Cape Town 2004 Olympic Bid. International journal of urban and regional research, 24(2), 449-458.
Hung, C-C., & Chen, L-H. (2009). A Fuzzy TOPSIS Decision Making Model with Entropy Weight under Intuitionistic Fuzzy Environment. Proceedings of the International Multi Conference of Engi-neers and Computer Scientists 2009 Vol I IMECS 2009, March 18 - 20, 2009, Hong Kong.
Hsu, H. M., & Chen, C. T. (1996). Aggregation of fuzzy opinions under group decision making. Fuzzy sets and systems, 79(3), 279-285.
Ighravwe, D.E., & Oke S.A. (2016). A multi-attribute framework for determining the competitive ad-vantages of products for business survival: A fuzzy TOPSIS approach. Total Quality Management and Business Excellence, 29(7), 762-785.
Kahraman, C., & Kaya, T. (2011). Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology. Expert Systems with Applications, 36(8), 6577-6585.
Karaca, C., Ulutaş, A., Yamaner, G., & Topal, A. (2019). The selection of the best olympic place for Turkey using an integrated MCDM model. Decision Science Letters, 8(1), 1-16.
Lenskyj, H. J. (1996). When winners are losers: Toronto and Sydney bids for the Summer Olym-pics. Journal of Sport and Social Issues, 20(4), 392-410.
Liu, J. C. (2012). The strategy of city cultural governance: 2009 Kaohsiung world games and globalized city cultural images. Journal of Leisure Studies, 10(1), 47-71.
Saghafian, S., & Hejazi, S. R. (2005, November). Multi-criteria group decision making using a modified fuzzy TOPSIS procedure. In Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on (Vol. 2, pp. 215-221). IEEE.
Wang, Y. J., & Lee, H. S. (2007). Generalizing TOPSIS for fuzzy multiple-criteria group decision-making. Computers & Mathematics with Applications, 53(11), 1762-1772.
Wang, Y.J., Lee, H.S., & Lin, K. (2003). Fuzzy TOPSIS for multi-criteria decision-making. Interna-tional Mathematical Journal, I(4), 367-379.
Whitson, D., & Macintosh, D. (1996). The global circus: International sport, tourism, and the marketing of cities. Journal of Sport and Social Issues, 20(3), 278-295.
Yuan, G., Jin, H., Li, H., & Liu, S. (2011). Strategies to Avoid Corruptions in FIFA. International Journal of Business and Management, 6(6), 215-217.
Appendix A
Step 1: Determine the number of technical and economic criteria for the evaluation process
Step 2: Select the number of potential locations for the sporting programme
Step 3: Determine the number of decision-maker for the evaluation process
Step 4: Select the linguistic terms and the type of fuzzy number for the evaluation process
Step 5: Evaluate the technical and economic criteria importance as well as the alternatives technical and economic criteria values.
Step 6: Set-up decision matrix for the evaluation process using the information in Step 5 (See Eq. 1).
Step 7: Aggregate the decision-makers responses (see Eq. 2) for the criteria importance and their alter-native values
Step 8: Create the normalised decision matrix (see Eqs. (3-4)).
Step 9: Based on the results in normalised decision matrix, create the weighted normalised decision ma-trix (see Eq. (7)).
Step 10: Determine the alternatives ideal and not-ideal solutions using the expression in Eqs. (10-11).
Step 11: Determine alternative closeness coefficient and rank using Eq. (12).
Govindan, K., Khodaverdi, R., & Jafarian, A. (2013). A fuzzy multi criteria approach for measuring sustainability performance of a supplier based on triple bottom line approach. Journal of Cleaner production, 47, 345-354.
Hiller, H. H. (2000). Mega‐events, urban boosterism and growth strategies: an analysis of the objectives and legitimations of the Cape Town 2004 Olympic Bid. International journal of urban and regional research, 24(2), 449-458.
Hung, C-C., & Chen, L-H. (2009). A Fuzzy TOPSIS Decision Making Model with Entropy Weight under Intuitionistic Fuzzy Environment. Proceedings of the International Multi Conference of Engi-neers and Computer Scientists 2009 Vol I IMECS 2009, March 18 - 20, 2009, Hong Kong.
Hsu, H. M., & Chen, C. T. (1996). Aggregation of fuzzy opinions under group decision making. Fuzzy sets and systems, 79(3), 279-285.
Ighravwe, D.E., & Oke S.A. (2016). A multi-attribute framework for determining the competitive ad-vantages of products for business survival: A fuzzy TOPSIS approach. Total Quality Management and Business Excellence, 29(7), 762-785.
Kahraman, C., & Kaya, T. (2011). Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology. Expert Systems with Applications, 36(8), 6577-6585.
Karaca, C., Ulutaş, A., Yamaner, G., & Topal, A. (2019). The selection of the best olympic place for Turkey using an integrated MCDM model. Decision Science Letters, 8(1), 1-16.
Lenskyj, H. J. (1996). When winners are losers: Toronto and Sydney bids for the Summer Olym-pics. Journal of Sport and Social Issues, 20(4), 392-410.
Liu, J. C. (2012). The strategy of city cultural governance: 2009 Kaohsiung world games and globalized city cultural images. Journal of Leisure Studies, 10(1), 47-71.
Saghafian, S., & Hejazi, S. R. (2005, November). Multi-criteria group decision making using a modified fuzzy TOPSIS procedure. In Computational Intelligence for Modelling, Control and Automation, 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, International Conference on (Vol. 2, pp. 215-221). IEEE.
Wang, Y. J., & Lee, H. S. (2007). Generalizing TOPSIS for fuzzy multiple-criteria group decision-making. Computers & Mathematics with Applications, 53(11), 1762-1772.
Wang, Y.J., Lee, H.S., & Lin, K. (2003). Fuzzy TOPSIS for multi-criteria decision-making. Interna-tional Mathematical Journal, I(4), 367-379.
Whitson, D., & Macintosh, D. (1996). The global circus: International sport, tourism, and the marketing of cities. Journal of Sport and Social Issues, 20(3), 278-295.
Yuan, G., Jin, H., Li, H., & Liu, S. (2011). Strategies to Avoid Corruptions in FIFA. International Journal of Business and Management, 6(6), 215-217.
Appendix A
Step 1: Determine the number of technical and economic criteria for the evaluation process
Step 2: Select the number of potential locations for the sporting programme
Step 3: Determine the number of decision-maker for the evaluation process
Step 4: Select the linguistic terms and the type of fuzzy number for the evaluation process
Step 5: Evaluate the technical and economic criteria importance as well as the alternatives technical and economic criteria values.
Step 6: Set-up decision matrix for the evaluation process using the information in Step 5 (See Eq. 1).
Step 7: Aggregate the decision-makers responses (see Eq. 2) for the criteria importance and their alter-native values
Step 8: Create the normalised decision matrix (see Eqs. (3-4)).
Step 9: Based on the results in normalised decision matrix, create the weighted normalised decision ma-trix (see Eq. (7)).
Step 10: Determine the alternatives ideal and not-ideal solutions using the expression in Eqs. (10-11).
Step 11: Determine alternative closeness coefficient and rank using Eq. (12).