Software implementation of the optimal temporal intuitionistic fuzzy algorithm for franchisee selection

• Autorzy: Traneva Velichka , Mavrov Deyan , Tranev Stoyan

Identyfikatory

DOI

10.15439/2022F149

• Konferencja: Federated Conference on Computer Science and Information Systems (17 ; 04-07.09.2022 ; Sofia, Bulgaria)
• Języki publikacji: EN

Abstrakty

EN

The selection of the most suitable franchisee applicant in an uncertain environment in a particular moment of time is a key decision for a franchisor and the success of a franchising business. In this work, for the first time, we describe a problem for choosing the optimal candidate for the franchise chain and algorithm for a solution in terms of temporal intuitionistic fuzzy pairs and index matrices as a means for data analysis in uncertain conditions over time. We also use our software utility to demonstrate the proposed algorithm and to apply the decision support approach to a franchisee selection for the largest fast food restaurant chain in Bulgaria.

Słowa kluczowe

EN

computer science; franchising; data analysis; software algorithm; decision making; software; index

PL

informatyka; franczyza; analiza danych; algorytm oprogramowania; podejmowanie decyzji; oprogramowanie; indeks

• Wydawca: Polskie Towarzystwo Informatyczne
• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 30
• Strony: 387--390
• Opis fizyczny: Bibliogr. 21 poz., wz.

Twórcy

autor

Traneva Velichka

• Prof. Asen Zlatarov University 1 Prof. Yakimov Blvd, Burgas 8000, Bulgaria

autor

Mavrov Deyan

• Prof. Asen Zlatarov University 1 Prof. Yakimov Blvd, Burgas 8000, Bulgaria

autor

Tranev Stoyan

• Prof. Asen Zlatarov University 1 Prof. Yakimov Blvd, Burgas 8000, Bulgaria

Bibliografia

• 1. B. Elango, “A bibliometric analysis of franchising research (1988–2017),” The Journal of Entrepreneurship, vol. 28 (2), 2019, pp. 223–249.
• 2. D. Mavrov, “An Application for Performing Operations on Two-Dimensional Index Matrices,” Annual of “Informatics” Section, Union of Scientists in Bulgaria, vol. 10, 2019 / 2020, pp. 66-80.
• 3. D. Mavrov, V. Atanassova, V. Bureva, O. Roeva, P. Vassilev, R. Tsvetkov, D. Zoteva, E. Sotirova, K. Atanassov, A. Alexandrov, H. Tsakov, “Application of Game Method for Modelling and Temporal Intuitionistic Fuzzy Pairs to the Forest Fire Spread in the Presence of Strong Wind,” Mathematics, vol. 10, 2022, pp. 1280. ttps://doi.org/10.3390/mat10081280
• 4. E. Szmidt, J. Kacprzyk, “Amount of information and its reliability in the ranking of Atanassov’s intuitionistic fuzzy alternatives,” in: Rakus-Andersson, E., Yager, R., Ichalkaranje, N., etc. (eds.), Recent Advances in Decision Making, SCI, Springer, Heidelberg, vol. 222, 2009, pp. 7–19.
• 5. K. Atanassov, “Index Matrices: Towards an Augmented Matrix Calculus,” Studies in Computational Intelligence, Springer, vol. 573, 2014.
• 6. K. Atanassov, “On Intuitionistic Fuzzy Sets Theory,” STUDFUZZ, vol. 283, Springer, Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29127-2.
• 7. K. Atanassov, D. Mavrov, V. Atanassova, “Intercriteria decision making: a new approach for multicriteria decision making, based on index matrices and intuitionistic fuzzy sets,” Issues in IFSs and Generalized Nets, vol. 11, 2014, pp. 1-8.
• 8. K. Atanassov, E. Szmidt, J. Kacprzyk, V. Atanassova, “An approach to a constructive simplication of multiagent multicriteria decision making problems via ICrA,” Comptes rendus de lAcademie bulgare des Sciences, vol. 70 (8), 2017, pp. 1147-1156.
• 9. K. Atanassov, P. Vassilev, O. Roeva, “Level Operators over Intuitionistic Fuzzy Index Matrices,” Mathematics, vol. 9, 2021, pp. 366.
• 10. L. Zadeh, “Fuzzy Sets,” Information and Control, vol. 8 (3), 1965, pp. 338-353.
• 11. N. Ikonomov, P. Vassilev, O. Roeva, “ICrAData - Software for InterCriteria Analysis,” Int. Journal Bioautomation, vol. 22, 2018, pp. 1-10.
• 12. P. Hsu, B. Chen, “Developing and Implementing a Selection Model for Bedding Chain Retail Store Franchisee Using Delphi and Fuzzy AHP,” Quality & Quantity, vol. 41, 2007, pp. 275-290.
• 13. R. J. Kuo, S. C. Chi, S. S. Kao, “A decision support system for selecting convenience store location through integration of FAHP and artificial neural network,” Computers in Industry, vol. 47 (2), 2002, pp. 199-214.
• 14. S. Fidanova, M. Ganzha, O. Roeva, “InterCriteria analyzis of hybrid ant colony optimization algorithm for multiple knapsack problem,” In: Proceedings of the 16th Conference on Computer Science and Information Systems (FedCSIS), Science and intelligence systems, Sofia, Bulgaria, vol. 25, 2021, pp. 173–180.
• 15. V. Atanassova, O. Roeva, “Computational complexity and influence of numerical precision on the results of ICrA in the decision making process,” Notes on IFSs, vol. 24 (3), 2018, pp. 53-63.
• 16. V. Traneva, S. Tranev, D. Mavrov, “Interval-Valued Intuitionistic Fuzzy Decision-Making Method using Index Matrices and Application in Outsourcing,” In: Proceedings of the 16th Conference on Computer Science and Information Systems (FedCSIS), Sofia, Bulgaria, vol. 25, 2021, pp. 251–255.
• 17. V. Traneva, S. Tranev, M. Stoenchev, K. Atanassov, “Scaled aggregation operations over two- and three-dimensional index matrices,” Soft computing, vol. 22, 2019, pp. 5115-5120.
• 18. V. Traneva, S. Tranev, Index Matrices as a Tool for Managerial Decision Making, Publ. House of the USB; 2017 (in Bulgarian).
• 19. V. Traneva, S. Tranev, “IF Algorithm for Optimal Selection of Franchisees,” in: Kahraman C. (eds) Infus2022, Lecture Notes in Networks and Systems, vol. 504, Springer, Cham, pp. 632-640, 2022.
• 20. V. Traneva, S. Tranev, “IF Analysis of Variance of Ticket Sales,” in: Kahraman, C. (eds.) INFUS 2020, Advances in Intelligent Systems and Computing, vol. 1197, Springer, Cham, 2021, pp. 363-340.
• 21. V. Traneva, S. Tranev, D. Mavrov, “Application of an Interval-Valued Intuitionistic Fuzzy Decision-Making Method in Outsourcing Using a Software Program,” Studies in computational intelligence, Springer, 2023 (in press)

Uwagi

1. Short article

2. Track 6: 15th International Workshop on Computational Optimization

3. Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).