Retail Chain Stores Location using Integrated Interval-Valued Intuitionistic Fuzzy AHP and TOPSIS: Case Study Ofogh Kourosh Stores

Document Type : Original Article

Authors

1 PhD student, Department of Industrial Engineering, Faculty of Technology and Engineering, Al-Zahra University, Tehran, Iran.

2 Associate Professor, Department of Industrial Engineering, Faculty of Technology and Engineering, Al-Zahra University, Tehran, Iran.

Abstract

Introduction: Retailers are powerful agents in product distribution due to their proximity to final consumers and their potential to create markets. Choosing the location of a retail store is a strategic decision and a long-term investment, impacting both customer satisfaction and company profitability amidst market changes and fierce competition. This study aims to develop a method for selecting the best retail store locations for Ofogh Kourosh by strategically ranking potential locations using criteria such as population, store location characteristics, economic considerations, and competition.
Methods: Given the increasing complexity of retail store location selection and the uncertainty in evaluating criteria, a multi-criteria decision-making structure is used alongside a fuzzy intuitionistic approach. Intuitionistic fuzzy numbers extend traditional fuzzy numbers by incorporating a hesitation degree in addition to membership and non-membership degrees, better modeling the uncertainty faced by decision-makers. In this study, five main criteria are considered: cost, competition, traffic density, vehicle traffic volume, physical characteristics, and store location. These criteria were identified through expert interviews. Twelve sub-criteria, including rent cost, equipment cost, competitor strength, number of competitors, distance to competitors, vehicle traffic volume, pedestrian traffic volume, store size, parking space, proximity to main streets, proximity to commercial centers, and proximity to residential complexes, were selected to choose the best location among five potential sites. The proposed method integrates the Analytical Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) based on interval-valued intuitionistic fuzzy sets to evaluate criteria and rank the proposed options. The AHP method, based on interval-valued intuitionistic fuzzy sets, was used to consider uncertainty in decision-making and to calculate the weight of the criteria. The TOPSIS method was applied to prioritize the proposed options for locating a new retail store. Region 4 of Tehran city, the most populous area in the city, was considered for the case study. Ofogh Kourosh has 40 stores in this region, supplied by two large warehouses.
Results and discussion: The numerical results indicate that the sub-criteria of rent cost (from the cost criterion) and proximity to commercial centers (from the store location criterion) were the most and least important criteria, respectively. Among the five candidate locations, locations four and one were ranked highest and lowest for establishing new stores. To validate the proposed method, the evaluation results were compared with those obtained using the AHP-WASPAS method based on interval-valued intuitionistic fuzzy sets. Both methods identified location four as the best site for a new retail store and location one as the least suitable due to its location and competitor conditions.
Conclusion: The study demonstrates that using a combined AHP-TOPSIS method based on interval-valued intuitionistic fuzzy sets is effective for evaluating and ranking potential retail store locations. This approach accounts for the uncertainty in decision-making and provides a comprehensive evaluation of various criteria, ultimately aiding strategic planning and investment decisions in the retail sector.

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References

  1. Atanassov, K. T. (1986). Intuitionistic Fuzzy Sets, Fuzzy Sets and Systems, 20(1), 87-96.
  2. Atanassov, K. T.; Gargov, G. 1989. Interval valued intuitionistic fuzzy sets, Fuzzy Sets and Systems 31(3), 343-349.
  3. Behzadian, M., Otaghsara, S. K., Yazdani, M., & Ignatius, J. (2012). A state-of the-art survey of TOPSIS applications. Expert Systems with applications, 39(17), 13051-13069.
  4. Burnaz, S., & Topcu, Y. I. (2006). A multiple‐criteria decision‐making approach for the evaluation of retail location. Journal of Multi‐Criteria Decision Analysis, 14(1‐3), 67-76.
  5. Chang, H. J., & Hsieh, C. M. (2014). A TOPSIS model for chain store location selection. Review of Integrative Business and Economics Research, 4(1), 410-416.
  6. Cheng, E. W., Li, H., & Yu, L. (2005). The analytic network process (ANP) approach to location selection: a shopping mall illustration. Construction Innovation, 5(2), 83-97.
  7. Durak, İ., Yıldız, M. S., Akar, Y. O., & Yemenici, A. D. (2017). Warehouse Site Selection in Retail Sector: An Application of Ahp (Analytical Hierarchy Process) and VIKOR Methods. International Journal of Business and Management Invention (IJBMI), 12(6), 65-73.
  8. Einy - Sarkalleh, Gholamreza; Hafezalkotob, Ashkan; Tavakkoli - Moghaddam, Reza &Najafi, Esmaeil (2022). Identifying the Main Obstacles to Carrying Out Bi-directional Contracts in Supply Chains by Adopting the Best-worst Method and Undertaking Weighted Aggregates Sum Product Assessment: A Fuzzy Approach. The Journal of Industrial Management Journal, 14(2), 310-336. (In Persian)
  9. Erbıyık, H., Özcan, S., & Karaboğa, K. (2012). Retail store location selection problem with multiple analytical hierarchy process of decision making an application in Turkey. Procedia-Social and Behavioral Sciences, 58, 1405-1414.
  10. Eryuruk, S. H., Kalaoglu, F., & Baskak, M. (2011). Logistics as a competitive strategy analysis of the clothing industry in terms of logistics. Fibres & Textiles in Eastern Europe, 19(1), 12-17.
  11. Ge, D., Hu, L., Jiang, B., Su, G., & Wu, X. (2019). Intelligent site selection for bricks-and-mortar stores. Modern Supply Chain Research and Applications, 1(1), 88-102.
  12. Ghorui, N., Ghosh, A., Algehyne, E. A., Mondal, S. P., & Saha, A. K. (2020). AHP-TOPSIS inspired shopping mall site selection problem with fuzzy data. Mathematics, 8(8), 1380.
  13. Goceri, M. S. (2020). Customer shopping experience using ahp weighted topsis method for selection of retail store in turkey. South Florida Journal of Development, 1(3), 150-156.
  14. Hartati, V., & Islamiati, F. A. (2019). Analysis of Location Selection of Fish Collection Center Using AHPmethod in National Fish Logistic System. Civil Engineering and Architecture, 7(3A), 41-49.
  15. Hwang, C. L., Yoon, K., Hwang, C. L., & Yoon, K. (1981). Methods for multiple attribute decision making. Multiple attribute decision making: methods and applications a state-of-the-art survey, 58-191.
  16. Ilbahar, E., & Kahraman, C. (2018). Retail store performance measurement using a novel interval-valued Pythagorean fuzzy WASPAS method. Journal of Intelligent & Fuzzy Systems, 35(3), 3835-3846.
  17. Jones, M. A., Mothersbaugh, D. L., & Beatty, S. E. (2003). The effects of locational convenience on customer repurchase intentions across service types. Journal of Services Marketing, 17(7), 701-712.
  18. Ka, B. (2011). Application of fuzzy AHP and ELECTRE to China dry port location selection. The Asian Journal of Shipping and Logistics, 27(2), 331-353.
  19. Kabir, G., & Sumi, R. S. (2014). Power substation location selection using fuzzy analytic hierarchy process and PROMETHEE: A case study from Bangladesh. Energy, 72, 717-730.
  20. Kahraman, C., Öztayşi, B., & Onar, S. Ç. (2020). An integrated intuitionistic fuzzy AHP and TOPSIS approach to evaluation of outsource manufacturers. Journal of Intelligent Systems, 29(1), 283-297.
  21. 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, 73, 23-32.
  22. Mardani, A., Jusoh, A., Nor, K., Khalifah, Z., Zakwan, N., & Valipour, A. (2015). Multiple criteria decision-making techniques and their applications–a review of the literature from 2000 to 2014. Economic research-Ekonomska istraživanja, 28(1), 516-571.
  23. Mihajlović, J., Rajković, P., Petrović, G., & Ćirić, D. (2019). The selection of the logistics distribution center location based on MCDM methodology in southern and eastern region in Serbia. Operational Research in Engineering Sciences: Theory and Applications, 2(2), 72-85.
  24. Mulliner, E., Malys, N., & Maliene, V. (2016). Comparative analysis of MCDM methods for the assessment of sustainable housing affordability. Omega, 59, 146-156.
  25. NGUYEN, N. T. (2021). Applying AHP in Evaluation the Distribution Science of Suppliers for Retails in Vietnam: Case of Saigon Co-op Mart. Journal of Distribution Science, 19(3), 35-47.
  26. NONG, N. M. T., & HA, D. S. (2021). Hybrid Optimization for Distribution Channel Management: A Case of Retail Location Selection. Journal of Distribution Science, 19(12), 45-56.
  27. Okatan, B. S., Peker, I., & Birdogan, B. A. K. İ. (2019). An integrated DEMATEL-ANP-VIKOR approach for food distribution center site selection: A case study of Georgia. Journal of Management Marketing and Logistics, 6(1), 10-20.
  28. Önüt, S., Efendigil, T., & Kara, S. S. (2010). A combined fuzzy MCDM approach for selecting shopping center site: An example from Istanbul, Turkey. Expert systems with applications, 37(3), 1973-1980.
  29. Oztaysi, B., Onar, S. C., Goztepe, K., & Kahraman, C. (2017). Evaluation of research proposals for grant funding using interval-valued intuitionistic fuzzy sets. Soft Computing, 21, 1203-1218.
  30. Quynh, M. P., Thu, T. L., Huong, Q. D., Van, A. P. T., Van, H. N., & Van, D. N. (2020). Distribution center location selection using a novel multi criteria decision-making approach under interval neutrosophic complex sets. Infinite Study.
  31. Saaty, T. L. (1980). The Analytic Hierarchy Process Mcgraw Hill, New York.
  32. Sadeghi Moghadam, M. R., Hosseini Dehshiri, S. J., Rajabi Kafshgar, F. Z., & Sinaei, S. S. (2021). Utilization of Intuitive Fuzzy WASPAS Method with Interval Values to Evaluation of Reverse Logistics Implementation Actions in the LARG Supply Chain. The Journal of Journal of Industrial Management Perspective, 11(3), 215-242. (In Persian)
  33. Seifbarghy, M. (2022). A Multi-Objective Sustainable Closed Loop Supply Chain Model Considering Suppliers Evaluation and using SWARA-WASPAS Method. The Journal of Industrial Management Perspective, 12(3), 63-88. (in Persian)
  34. Singh, J., Tyagi, P., Kumar, G., & Agrawal, S. (2020). Convenience store locations prioritization: a fuzzy TOPSIS-GRA hybrid approach. Modern Supply Chain Research and Applications.
  35. Tabak, Ç., Yıldız, K., & Yerlikaya, M. (2019). Logistic location selection with Critic-Ahp and Vikor integrated approach. Data Science and Applications, 2(1), 21-25.
  36. Tavakoli, A., Pooya, A. R., & Alavi Tabari, S. J. (2013). Fuzzy Multi‐Criteria Decision Making Fusion Model Design for Facility Layout Selection. The Journal of Industrial Management Perspective, 3(2), 57-84. (In Persian)
  37. Turhan, G., Akalın, M., & Zehir, C. (2013). Literature review on selection criteria of store location based on performance measures. Procedia-Social and Behavioral Sciences, 99, 391-402.
  38. Tuzkaya, U. R., Yilmazer, K. B., & Tuzkaya, G. (2015). An integrated methodology for the emergency logistics centers location selection problem and its application for the Turkey case. Journal of Homeland Security and Emergency Management, 12(1), 121-144.
  39. Tzeng, G. H., & Huang, J. J. (2011). Multiple attribute decision making: methods and applications. CRC press.
  40. Utami, I. (2018). Quantitative analytical hierarchy process to marketing store location selection. In MATEC Web of Conferences(Vol. 154, p. 01075). EDP Sciences.
  41. Wang, W., & Liu, X. (2013). The multi-attribute decision making method based on interval-valued intuitionistic fuzzy Einstein hybrid weighted geometric operator. Computers & Mathematics with Applications, 66(10), 1845-1856.
  42. Wu, J., & Chiclana, F. (2012). Non-dominance and attitudinal prioritisation methods for intuitionistic and interval-valued intuitionistic fuzzy preference relations. Expert Systems with Applications, 39(18), 13409-13416.
  43. Wu, J., Huang, H. B., & Cao, Q. W. (2013). Research on AHP with interval-valued intuitionistic fuzzy sets and its application in multi-criteria decision making problems. Applied Mathematical Modelling, 37(24), 9898-9906.
  44. Yap, J. Y., Ho, C. C., & Ting, C. Y. (2018, September). Analytic Hierarchy Process (AHP) for business site selection. In AIP Conference Proceedings(Vol. 2016, No. 1, p. 020151). AIP Publishing LLC.
  45. Yıldız, N., & Tüysüz, F. (2019). A hybrid multi-criteria decision making approach for strategic retail location investment: Application to Turkish food retailing. Socio-Economic Planning Sciences, 68, 100619.
  46. Zavadskas, E. K., Kalibatas, D., & Kalibatiene, D. (2016). A multi-attribute assessment using WASPAS for choosing an optimal indoor environment. Archives of Civil and Mechanical Engineering, 16, 76-85.

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