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A novel modified Delphi-based spherical fuzzy AHP integrated spherical fuzzy CODAS methodology for vending machine location selection problem: a real-life case study in İstanbul

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Abstract

Timely delivery of products to customers is the great importance for retailers in supply chain management. Vending machines are one of the most effective retail tools for consumers to get the product they want at any time. With the developing technology, retailers tend to establish distribution with vending machines to provide faster service to the consumer. At this point, one of the primary objectives is to make the location selection decisions for these vending machines. The most suitable location selection decision includes the evaluation of each alternative under the specified criteria in a problem where there are multiple alternative locations. The present paper proposes a novel modified Delphi-based spherical fuzzy analytical hierarchy process (SFAHP) integrated spherical fuzzy combinative distance-based assessment (SFCODAS) methodology to the vending machine location selection (VMLS) problem. After determining the main and sub-criteria hierarchically, the Modified Delphi method is used to consolidate the opinions of the experts regarding the criteria and integrate them into the study. The main and sub-criteria weights are provided by the SFAHP method. And the most suitable location is determined by the SFCODAS method by ranking among the alternative locations according to these weighted criteria. The case study of VMLS is presented for a retail firm in Istanbul. Sensitivity analysis is performed to measure the flexibility of the proposed methodology. To validate the applicability of the proposed methodology, comparison analysis is presented with the results of the spherical fuzzy weighted aggregated sum-product assessment (SFWASPAS) method. Finally, the conclusions and future directions are discussed in the study.

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References

  1. Agarwal A, Shankar R, Tiwari MK (2006) Modeling the metrics of lean, agile and leagile supply chain: an ANP-based approach. Eur J Oper Res 173(1):211–225. https://doi.org/10.1016/J.EJOR.2004.12.005

    Article  MathSciNet  Google Scholar 

  2. Dey B, Bairagi B, Sarkar B, Sanyal SK (2016) Warehouse location selection by fuzzy multi-criteria decision making methodologies based on subjective and objective criteria. Int J Manag Sci Eng Manag 11(4):262–278. https://doi.org/10.1080/17509653.2015.1086964

    Article  Google Scholar 

  3. Chou SY, Chang YH, Shen CY (2008) A fuzzy simple additive weighting system under group decision-making for facility location selection with objective/subjective attributes. Eur J Oper Res 189(1):132–145. https://doi.org/10.1016/J.EJOR.2007.05.006

    Article  Google Scholar 

  4. Ertuǧrul I, Karakaşoǧlu N (2008) Comparison of fuzzy AHP and fuzzy TOPSIS methods for facility location selection. Int J Adv Manuf Technol 39(7–8):783–795. https://doi.org/10.1007/s00170-007-1249-8

    Article  Google Scholar 

  5. Chakraborty R, Ray A, Dan PK (2013) Multi criteria decision making methods for location selection of distribution centers. Int J Ind Eng Comput 4(4):491–504. https://doi.org/10.5267/j.ijiec.2013.06.006

    Article  Google Scholar 

  6. Cagri Tolga A, Tuysuz F, Kahraman C (2013) A fuzzy multi-criteria decision analysis approach for retail location selection. Int J Inf Technol Decis Mak 12(4):729–755. https://doi.org/10.1142/S0219622013500272

    Article  Google Scholar 

  7. Yilmaz M, Atan T (2021) Hospital site selection using fuzzy EDAS method: case study application for districts of Istanbul. J Intell Fuzzy Syst 41(2):2591–2602. https://doi.org/10.3233/JIFS-201757

    Article  Google Scholar 

  8. Wu CR, Lin CT, Chen HC (2009) Integrated environmental assessment of the location selection with fuzzy analytical network process. Qual Quant 43(3):351–380. https://doi.org/10.1007/s11135-007-9125-z

    Article  Google Scholar 

  9. He Y, Wang X, Lin Y, Zhou F, Zhou L (2017) Sustainable decision making for joint distribution center location choice. Transp Res Part D Transp Environ 55:202–216. https://doi.org/10.1016/J.TRD.2017.07.001

    Article  Google Scholar 

  10. Daneshvar Rouyendegh B, Yildizbasi A, Arikan ÜZB (2018) Using ıntuitionistic fuzzy TOPSIS in site selection of wind power plants in Turkey. Adv Fuzzy Syst. https://doi.org/10.1155/2018/6703798

    Article  Google Scholar 

  11. Agwa AM, Shaaban SM (2021) Siting hydropower plant by rough set and combinative distance-based assessment|(Planowanie usytuowania elektrowni wodnej metodą wstępną i kombinowanąocena na podstawie odległości). Prz Elektrotechniczny 97(3):15–20. https://doi.org/10.15199/48.2021.03.3

    Article  Google Scholar 

  12. Karagöz S, Deveci M, Simic V, Aydin N (2021) Interval type-2 fuzzy ARAS method for recycling facility location problems. Appl Soft Comput 102:107107. https://doi.org/10.1016/J.ASOC.2021.107107

    Article  Google Scholar 

  13. Sherif SU, Asokan P, Sasikumar P, Mathiyazhagan K, Jerald J (2022) An integrated decision making approach for the selection of battery recycling plant location under sustainable environment. J Clean Prod 330:129784. https://doi.org/10.1016/J.JCLEPRO.2021.129784

    Article  Google Scholar 

  14. Zhang Y, Zhang Y, Li Y, Liu S, Yang J (2017) A study of rural logistics center location based on ıntuitionistic fuzzy TOPSIS. Math Probl Eng. https://doi.org/10.1155/2017/2323057

    Article  Google Scholar 

  15. Karagoz S, Deveci M, Simic V, Aydin N, Bolukbas U (2020) A novel intuitionistic fuzzy MCDM-based CODAS approach for locating an authorized dismantling center: a case study of Istanbul. Waste Manag Res 38(6):660–672. https://doi.org/10.1177/0734242X19899729

    Article  Google Scholar 

  16. Albayrak K (2021) A hybrid fuzzy decision making approach for sitting a solid waste energy production plant. Soft Comput. https://doi.org/10.1007/s00500-021-06563-x

    Article  Google Scholar 

  17. Kaya T (2021) Small hotel location selection problem the case of Cappadocia. Adv Hosp Tour Res 9100:368–389. https://doi.org/10.30519/ahtr.899626

    Article  Google Scholar 

  18. Erol I, Sencer S, Özmen A, Searcy C (2014) Fuzzy MCDM framework for locating a nuclear power plant in Turkey. Energy Policy 2014(67):186–197. https://doi.org/10.1016/j.enpol.2013.11.056

    Article  Google Scholar 

  19. Ayyildiz E, Erdogan M, Taskin GA (2021) A Pythagorean fuzzy number-based integration of AHP and WASPAS methods for refugee camp location selection problem: a real case study for Istanbul, Turkey. Neural Comput Appl 33(22):15751–15768. https://doi.org/10.1007/s00521-021-06195-0

    Article  Google Scholar 

  20. Temur GT (2016) A novel multi attribute decision making approach for location decision under high uncertainty. Appl Soft Comput J 40:674–682. https://doi.org/10.1016/j.asoc.2015.12.027

    Article  Google Scholar 

  21. Emeç Ş, Akkaya G (2018) Stochastic AHP and fuzzy VIKOR approach for warehouse location selection problem. J Enterp Inf Manag 31(6):950–962. https://doi.org/10.1108/JEIM-12-2016-0195

    Article  Google Scholar 

  22. Ehsanifar M, Wood DA, Babaie A (2021) UTASTAR method and its application in multi-criteria warehouse location selection. Oper Manag Res 14(1–2):202–215. https://doi.org/10.1007/s12063-020-00169-6

    Article  Google Scholar 

  23. Zavadskas EK, Turskis Z, Kildiene S (2014) State of art surveys of overviews on MCDM/MADM methods. Technol Econ Dev Econ 20(1):165–179. https://doi.org/10.3846/20294913.2014.892037

    Article  Google Scholar 

  24. Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353. https://doi.org/10.1016/S0019-9958(65)90241-X

    Article  Google Scholar 

  25. Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20(1):87–96. https://doi.org/10.1016/S0165-0114(86)80034-3

    Article  MathSciNet  Google Scholar 

  26. Gündoǧdu FK, Kahraman C (2019) Spherical fuzzy sets and spherical fuzzy TOPSIS method. J Intell Fuzzy Syst 36(1):337–352. https://doi.org/10.3233/JIFS-181401

    Article  Google Scholar 

  27. Yager RR (2013) Pythagorean fuzzy subsets. In: Proceedings of the 2013 joint IFSA World congress and NAFIPS annual meeting, IFSA/NAFIPS 2013, pp 57–61. https://doi.org/10.1109/IFSA-NAFIPS.2013.6608375

  28. Smarandache F (2000) A unifying field in logics: neutrosophic logic. Neutrosophy, neutrosophic set, neutrosophic probability. ISBN 1-879585-76-6 contents: preface by Charles TL: 3

  29. De Tre G, Hallez A, Bronselaer A (2014) Performance optimization of object comparison. Int J Intell Syst 29(2):495–524. https://doi.org/10.1002/int

    Article  Google Scholar 

  30. Senapati T, Yager RR (2020) Fermatean fuzzy sets. J Ambient Intell Humaniz Comput 11(2):663–674. https://doi.org/10.1007/s12652-019-01377-0

    Article  Google Scholar 

  31. Bolturk E (2018) Pythagorean fuzzy CODAS and its application to supplier selection in a manufacturing firm. J Enterp Inf Manag 31(4):550–564. https://doi.org/10.1108/JEIM-01-2018-0020

    Article  Google Scholar 

  32. Jafarzadeh Ghoushchi S, Garg H, Rahnamay Bonab S, Rahimi A (2023) An integrated SWARA-CODAS decision-making algorithm with spherical fuzzy information for clean energy barriers evaluation. Expert Syst Appl 223:119884. https://doi.org/10.1016/J.ESWA.2023.119884

    Article  Google Scholar 

  33. Biswas S, Chatterjee S, Majumder S (2022) A spherical fuzzy framework for sales personnel selection. J Comput Cogn Eng 00(August):1–13. https://doi.org/10.47852/bonviewJCCE2202357

    Article  Google Scholar 

  34. Mathew M, Chakrabortty RK, Ryan MJ (2020) A novel approach integrating AHP and TOPSIS under spherical fuzzy sets for advanced manufacturing system selection. Eng Appl Artif Intell 96(September):103988. https://doi.org/10.1016/j.engappai.2020.103988

    Article  Google Scholar 

  35. Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning-III. Inf Sci (NY) 9(1):43–80. https://doi.org/10.1016/0020-0255(75)90017-1

    Article  MathSciNet  Google Scholar 

  36. Cu’ò’ng BC (2015) Picture fuzzy sets. J Comput Sci Cybern. https://doi.org/10.15625/1813-9663/30/4/5032

    Article  Google Scholar 

  37. Saaty TL, Saaty TL (2015) Decision making with the analytic hierarchy process. Int J Serv Sci. https://doi.org/10.1504/IJSSCI.2008.017590

    Article  Google Scholar 

  38. Yildiz A, Ayyildiz E, Taskin Gumus A, Ozkan C (2020) A modified balanced scorecard based hybrid Pythagorean fuzzy AHP-topsis methodology for ATM site selection problem. Int J Inf Technol Decis Mak 19(2):365–384. https://doi.org/10.1142/S0219622020500017

    Article  Google Scholar 

  39. Gulum P, Ayyildiz E, Taskin GA (2021) A two level interval valued neutrosophic AHP integrated TOPSIS methodology for post-earthquake fire risk assessment: an application for Istanbul. Int J Disaster Risk Reduct 61(May):102330. https://doi.org/10.1016/j.ijdrr.2021.102330

    Article  Google Scholar 

  40. Shi S-F, Lin M-C, Chen Y-Y (2019) The research on knowledge sharing and location selection of pharmaceutical logistics supply. DEStech Trans Econ Bus Manag. https://doi.org/10.12783/dtem/ssemr2019/30900

    Article  Google Scholar 

  41. Zhixiong C, Juanping C, Jinsha Y, Nan X, Dongsheng H (2019) Network access selection algorithm based on balanced profits between users and network. Wirel Commun Mob Comput. https://doi.org/10.1155/2019/6981657

    Article  Google Scholar 

  42. Dweiri F, Khan SA, Almulla A (2018) A multi-criteria decision support system to rank sustainable desalination plant location criteria. Desalination 444(May):26–34. https://doi.org/10.1016/j.desal.2018.07.007

    Article  Google Scholar 

  43. Mishra R, Abhyankar N, Vahdat V (2018) Optimal site selection using multi-criteria decision analysis and optimization. In: 39th international annual conference of the American society for engineering management, ASEM 2018: bridging the gap between engineering and business, no 2016, pp 94–103

  44. Govindan K, Garg K, Gupta S, Jha PC (2016) Effect of product recovery and sustainability enhancing indicators on the location selection of manufacturing facility. Ecol Indic 67:517–532. https://doi.org/10.1016/J.ECOLIND.2016.01.035

    Article  Google Scholar 

  45. Kuo RJ, Chi SC, Kao SS (2002) A decision support system for selecting convenience store location through integration of fuzzy AHP and artificial neural network. Comput Ind 47(2):199–214. https://doi.org/10.1016/S0166-3615(01)00147-6

    Article  Google Scholar 

  46. Mohamadghasemi A, Hadi-Vencheh A (2013) Erratum: a decision support system for selecting convenience store location through integration of fuzzy AHP and artificial neural network (computers in Industry (2002) 47(199–214)). Comput Ind 64(3):350. https://doi.org/10.1016/j.compind.2012.10.003

    Article  Google Scholar 

  47. Ayyildiz E, Taskin Gumus A (2020) A novel spherical fuzzy AHP-integrated spherical WASPAS methodology for petrol station location selection problem: a real case study for İstanbul. Environ Sci Pollut Res 27(29):36109–36120. https://doi.org/10.1007/s11356-020-09640-0

    Article  Google Scholar 

  48. Keshavarz Ghorabaee M, Zavadskas EK, Turskis JAZ (2016) A new combinative distance-based assessment. Econ Comput Econ Cybern Stud Res 50(1):39–68

    Google Scholar 

  49. Tadic S, Krstic M, Roso V, Brnjac N (2020) Dry port terminal location selection by applying the hybrid grey MCDM model. Sustainability. https://doi.org/10.3390/su12176983

    Article  Google Scholar 

  50. Ghorabaee MK, Amiri M, Zavadskas EK, Hooshmand R, Antuchevičienė J (2017) Fuzzy extension of the CODAS method for multi-criteria market segment evaluation. J Bus Econ Manag 18(1):1–19. https://doi.org/10.3846/16111699.2016.1278559

    Article  Google Scholar 

  51. Bolturk E, Kahraman C (2018) Interval-valued intuitionistic fuzzy CODAS method and its application to wave energy facility location selection problem. J Intell Fuzzy Syst 35(4):4865–4877. https://doi.org/10.3233/JIFS-18979

    Article  Google Scholar 

  52. Karaşan A, Boltürk E, Kahraman C (2019) A novel neutrosophic CODAS method: selection among wind energy plant locations. J Intell Fuzzy Syst 36(2):1491–1504. https://doi.org/10.3233/JIFS-181255

    Article  Google Scholar 

  53. Deveci M, Simic V, Torkayesh AE (2021) Remanufacturing facility location for automotive lithium-ion batteries: an integrated neutrosophic decision-making model. J Clean Prod 317:128438. https://doi.org/10.1016/J.JCLEPRO.2021.128438

    Article  Google Scholar 

  54. Karaşan A, Boltürk E, Kutlu Gündoğdu F (2021) Assessment of livability ındices of suburban places of Istanbul by using spherical fuzzy CODAS method. Decis Mak Spherical Fuzzy Sets Theory Appl. https://doi.org/10.1007/978-3-030-45461-6_12

    Article  Google Scholar 

  55. Saaty TL (1977) A scaling method for priorities in hierarchical structures. J Math Psychol 15(3):234–281. https://doi.org/10.1016/0022-2496(77)90033-5

    Article  MathSciNet  Google Scholar 

  56. Singer H, Özşahin Ş (2021) Prioritization of laminate flooring selection criteria from experts’ perspectives: a spherical fuzzy AHP-based model. Archit Eng Des Manag. https://doi.org/10.1080/17452007.2021.1956421

    Article  Google Scholar 

  57. Kutlu Gündoğdu F, Kahraman C (2020) Spherical fuzzy sets and decision making applications. Adv Intell Syst Comput 1029:979–987. https://doi.org/10.1007/978-3-030-23756-1_116

    Article  Google Scholar 

  58. Shishavan SAS, Kutlu Gündoğdu F, Farrokhizadeh E, Donyatalab Y, Kahraman C (2020) Novel similarity measures in spherical fuzzy environment and their applications. Eng Appl Artif Intell 94(April):103837. https://doi.org/10.1016/j.engappai.2020.103837

    Article  Google Scholar 

  59. Mishra AR, Mardani A, Rani P, Kamyab H, Alrasheedi M (2021) A new intuitionistic fuzzy combinative distance-based assessment framework to assess low-carbon sustainable suppliers in the maritime sector. Energy. https://doi.org/10.1016/j.energy.2021.121500

    Article  Google Scholar 

  60. Ali T, Ma H, Nahian AJ (2020) A multi-criteria decision-making approach to determine the optimal hybrid energy system in coastal off-grid areas: a case study of Bangladesh. Process Integr Optim Sustain 4(3):265–277. https://doi.org/10.1007/s41660-020-00116-9

    Article  Google Scholar 

  61. Özdaǧoǧlu A (2012) A multi-criteria decision-making methodology on the selection of facility location: fuzzy ANP. Int J Adv Manuf Technol 59(5–8):787–803. https://doi.org/10.1007/s00170-011-3505-1

    Article  Google Scholar 

  62. Wang Y, Kong WW, Wang Y, Wu FH, Wang LM (2012) The strategy of site location based on business area theory: case study of wal-mart, carrefour and metro in Xi’an. Appl Mech Mater 209–211:559–562. https://doi.org/10.4028/www.scientific.net/AMM.209-211.559

    Article  Google Scholar 

  63. Awasthi A, Chauhan SS, Goyal SK (2011) A multi-criteria decision making approach for location planning for urban distribution centers under uncertainty. Math Comput Model 53(1–2):98–109. https://doi.org/10.1016/J.MCM.2010.07.023

    Article  MathSciNet  Google Scholar 

  64. Yıldız N, Tüysüz F (2018) A hybrid multi-criteria decision making approach for strategic retail location investment: application to Turkish food retailing. Socioecon Plan Sci 2019(68):100619. https://doi.org/10.1016/j.seps.2018.02.006

    Article  Google Scholar 

  65. Ayyildiz E, Taskin Gumus A (2021) Interval-valued Pythagorean fuzzy AHP method-based supply chain performance evaluation by a new extension of SCOR model: SCOR 4.0. Complex Intell Syst 7(1):559–576. https://doi.org/10.1007/s40747-020-00221-9

    Article  Google Scholar 

  66. Roig-Tierno N, Baviera-Puig A, Buitrago-Vera J, Mas-Verdu F (2013) The retail site location decision process using GIS and the analytical hierarchy process. Appl Geogr 40:191–198. https://doi.org/10.1016/j.apgeog.2013.03.005

    Article  Google Scholar 

  67. Zavadskas EK, Turskis Z, Antucheviciene J, Zakarevicius A (2012) Optimization of weighted aggregated sum product assessment. Elektron Elektrotech 122(6):3–6. https://doi.org/10.5755/j01.eee.122.6.1810

    Article  Google Scholar 

  68. Wang J (2022) Tropical algebra with high-order matrix for multiple-noise removal. J Low Freq Noise Vib Act Control. https://doi.org/10.1177/14613484221143348

    Article  Google Scholar 

  69. Gong CM, Peng J, Wang J (2023) Tropical algebra for noise removal and optimal control. J Low Freq Noise Vib Act Control 42(1):317–324. https://doi.org/10.1177/14613484221126360

    Article  Google Scholar 

  70. Gupta P, Mehlawat MK, Ahemad F (2022) Selection of renewable energy sources: a novel VIKOR approach in an ıntuitionistic fuzzy linguistic environment, vol 2. Springer, Netherlands. https://doi.org/10.1007/s10668-022-02172-2

    Book  Google Scholar 

  71. Yang L, Sun Q, Zhang N, Li Y (2022) Indirect multi-energy transactions of energy internet with deep reinforcement learning approach. IEEE Trans Power Syst 37(5):4067–4077. https://doi.org/10.1109/TPWRS.2022.3142969

    Article  Google Scholar 

  72. Karaşan A, Kahraman C (2019) A novel intuitionistic fuzzy DEMATEL–ANP–TOPSIS integrated methodology for freight village location selection. J Intell Fuzzy Syst 36(2):1335–1352. https://doi.org/10.3233/JIFS-17169

    Article  Google Scholar 

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  1. Makine Fakültesi, Endüstri Mühendisliği, Yıldız Teknik Üniversitesi, İstanbul, Turkey

    Aslihan Yildiz & Coskun Ozkan

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Yildiz, A., Ozkan, C. A novel modified Delphi-based spherical fuzzy AHP integrated spherical fuzzy CODAS methodology for vending machine location selection problem: a real-life case study in İstanbul. Neural Comput & Applic 36, 823–842 (2024). https://doi.org/10.1007/s00521-023-09063-1

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