Top

Neural Computing and Applications

Published in:

26-04-2022 | Original Article

An integrated interval-valued intuitionistic fuzzy AHP-TOPSIS methodology to determine the safest route for cash in transit operations: a real case in Istanbul

Authors: Aslihan Yildiz, Ali Fuat Guneri, Coskun Ozkan, Ertugrul Ayyildiz, Alev Taskin

Published in: Neural Computing and Applications | Issue 18/2022

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

The Cash in Transit (CIT) deals with the money distribution and picking up between depot(s), central bank, bank branches, Automated Teller Machines (ATMs), jewelry stores, and exchange offices, safely and quickly. It is critical for companies carrying out CIT activities to identify risks and their priorities. It is one of the problems to be resolved for CIT companies to determine the risks that may occur on the route for each vehicle that will carry money from a certain center to the demand points. It is important for decision makers how critical these routes are concerning risks. Within the scope of the study, determining the risks, the weights of these risks, and high-risk routes accordingly are discussed. The literature review related to the problem is first consulted. After the literature review, interviews are made with experts on their subject. The main criteria and sub-criteria to define the risks are determined. Then, these criteria are weighted by the Interval-Valued Intuitionistic Fuzzy Analytical Hierarchy Process (IVIF-AHP), and risk assessment is made to alternative routes based on these weighted risks. The routes are evaluated by Interval-Valued Intuitionistic Fuzzy Technique for Order Preference by Similarity to Ideal Solution (IVIF-TOPSIS) methodology, and thus, it is determined which route has the highest risk and which route has the lowest risk. In this way, it is stated on which route should be used for the vehicles to distribute money and which routes should be improved.

previous article LW-Net: an interpretable network with smart lifting wavelet kernel for mechanical feature extraction and fault diagnosis

next article Wavelet-based self-supervised learning for multi-scene image fusion

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Agrell PJ, Lindroth R, Norrman A (2004) Risk, information and incentives in telecom supply chains. Int J Prod Econ 90(1):1–16. https://doi.org/10.1016/S0925-5273(02)00471-1CrossRef

Chan FTS, Kumar N (2007) Global supplier development considering risk factors using fuzzy extended AHP-based approach. Omega 35(4):417–431. https://doi.org/10.1016/j.omega.2005.08.004CrossRef

OHSAS (2007) OHSAS 18001: 2007 Occupational health and safety management systems–requirements

Li YZ, Hu H, Huang DZ (2013) Developing an effective fuzzy logic model for managing risks in marine oil transport. Int J Shipp Transp Logist 5(4–5):485–499. https://doi.org/10.1504/IJSTL.2013.055286CrossRef

Gul M, Guneri AF, Nasirli SM (2019) A fuzzy-based model for risk assessment of routes in oil transportation. Int J Environ Sci Technol 16(8):4671–4686. https://doi.org/10.1007/s13762-018-2078-zCrossRef

Ayyildiz E, Taskin Gumus A (2021) Pythagorean fuzzy AHP based risk assessment methodology for hazardous material transportation: an application in Istanbul. Environ Sci Pollut Res. https://doi.org/10.1007/s11356-021-13223-yCrossRef

Liu Y, Yu J, Sun H, Li T, He X, Lin Z (2022) Screening and prioritizing substances in groundwater in the Beijing–Tianjin–Hebei region of the North China Plain based on exposure and hazard assessments. J Hazard Mater 423:127142. https://doi.org/10.1016/j.jhazmat.2021.127142CrossRef

Lintukangas K, Kähkönen AK, Ritala P (2016) Supply risks as drivers of green supply management adoption. J Clean Prod 112:1901–1909. https://doi.org/10.1016/j.jclepro.2014.10.089CrossRef

Pourjavad E, Shahin A (2020) A hybrid model for analyzing the risks of green supply chain in a fuzzy environment. J Ind Prod Eng 37(8):422–433. https://doi.org/10.1080/21681015.2020.1833995CrossRef

10.

Meng Q, Liu X, Song Y, Wang W (2019) An extended generalized TODIM method for risk assessment of supply chain in social commerce under interval type-2 fuzzy environment. J Intell Fuzzy Syst 37(6):8551–8565. https://doi.org/10.3233/JIFS-190061CrossRef

11.

Farughi H, Mostafayi S (2017) A hybrid approach based on ANP, ELECTRE and SIMANP metaheuristic method for outsourcing manufacturing procedures according to supply chain risks—case study: a medical equipment manufacturer company in Iran. Decis Sci Lett 6(1):77–94. https://doi.org/10.5267/j.dsl.2016.6.003CrossRef

12.

Simic V, Gokasar I, Deveci M, Isik M (2021) Fermatean fuzzy group decision-making based CODAS approach for taxation of public transit investments. IEEE Trans Eng Manag pp 1–16

13.

Linkov I, Satterstrom FK, Steevens J, Ferguson E, Pleus RC (2007) Multi-criteria decision analysis and environmental risk assessment for nanomaterials. J Nanoparticle Res 9(4):543–554. https://doi.org/10.1007/s11051-007-9211-0CrossRef

14.

Ganin AA et al (2020) Multicriteria decision framework for cybersecurity risk assessment and management. Risk Anal 40(1):183–199. https://doi.org/10.1111/risa.12891CrossRef

15.

Senthil S, Murugananthan K, Ramesh A (2018) Analysis and prioritisation of risks in a reverse logistics network using hybrid multi-criteria decision making methods. J Clean Prod 179:716–730. https://doi.org/10.1016/j.jclepro.2017.12.095CrossRef

16.

Gaudenzi B, Borghesi A (2006) Managing risks in the supply chain using the AHP method. Int J Logist Manag 17(1):114–136. https://doi.org/10.1108/09574090610663464CrossRef

17.

Zhou F, Chen TY (2021) An extended pythagorean fuzzy VIKOR method with risk preference and a novel generalized distance measure for multicriteria decision-making problems. Neural Comput Appl 33(18):11821–11844. https://doi.org/10.1007/s00521-021-05829-7CrossRef

18.

Ansari ZN, Kant R, Shankar R (2020) Evaluation and ranking of solutions to mitigate sustainable remanufacturing supply chain risks: a hybrid fuzzy SWARA-fuzzy COPRAS framework approach. Int J Sustain Eng 13(6):473–494. https://doi.org/10.1080/19397038.2020.1758973CrossRef

19.

Ulutaş A, Topal A, Karabasevic D, Stanujkic D, Popovic G, Smarandache F (2022) Prioritization of logistics risks with plithogenic PIPRECIA method. Lect Notes Networks Syst 308:663–670. https://doi.org/10.1007/978-3-030-85577-2_78CrossRef

20.

Zhou X, Cheng L, Li M (2020) Assessing and mapping maritime transportation risk based on spatial fuzzy multi-criteria decision making: A case study in the South China sea. Ocean Eng 208:107403. https://doi.org/10.1016/J.OCEANENG.2020.107403CrossRef

21.

Radivojević G, Gajović V (2014) Supply chain risk modeling by AHP and Fuzzy AHP methods. J Risk Res 17(3):337–352. https://doi.org/10.1080/13669877.2013.808689CrossRef

22.

Chand M, Raj T, Shankar R (2015) A comparative study of multi criteria decision making approaches for risks assessment in supply chain. Int J Bus Inf Syst 18(1):67–84. https://doi.org/10.1504/IJBIS.2015.066128CrossRef

23.

Sattayaprasert W, Taneerananon P, Hanaoka S, Pradhananga R (2008) Creating a risk-based network for HazMat logistics by route prioritization with AHP—case study: gasoline logistics in Rayong, Thailand. IATSS Res 32(1):74–87. https://doi.org/10.1016/S0386-1112(14)60202-8CrossRef

24.

Soeanu A et al (2015) Transportation risk analysis using probabilistic model checking. Expert Syst Appl 42(9):4410–4421. https://doi.org/10.1016/j.eswa.2014.12.052CrossRef

25.

Anbuudayasankar SP, Ganesh K, Lenny-Koh SC, Ducq Y (2012) Modified savings heuristics and genetic algorithm for bi-objective vehicle routing problem with forced backhauls. Expert Syst Appl 39(3):2296–2305. https://doi.org/10.1016/j.eswa.2011.08.009CrossRef

26.

Zelenka J, Budinska I, Didekova Z (2012) A combination of heuristic and non-heuristic approaches for modified vehicle routing problem. In: Proceedings LINDI 2012–4th IEEE international symposium on logistics and industrial informatics, 2012, pp 107–112. doi: https://doi.org/10.1109/LINDI.2012.6319471

27.

Van Anholt RG, Coelho LC, Laporte G, Vis IFA (2016) An inventory-routing problem with pickups and deliveries arising in the replenishment of automated teller machines. Transp Sci 50(3):1077–1091. https://doi.org/10.1287/trsc.2015.0637CrossRef

28.

Bati S, Gozupek D (2019) Joint optimization of cash management and routing for new-generation automated teller machine networks. IEEE Trans. Syst. Man Cybern. Syst. 49(12):2724–2738. https://doi.org/10.1109/TSMC.2017.2710359CrossRef

29.

Larrain H, Coelho LC, Cataldo A (2017) A Variable MIP Neighborhood Descent algorithm for managing inventory and distribution of cash in automated teller machines. Comput Oper Res 85:22–31. https://doi.org/10.1016/j.cor.2017.03.010MathSciNetCrossRefMATH

30.

Koç Ç, Erbaş M, Özceylan E (2018) A rich vehicle routing problem arising in the replenishment of automated teller machines. Int J Optim Control Theor Appl 8(2):276–287. https://doi.org/10.11121/ijocta.01.2018.00572MathSciNetCrossRef

31.

Xu G, Li Y, Szeto WY, Li J (2019) A cash transportation vehicle routing problem with combinations of different cash denominations. Int Trans Oper Res 26(6):2179–2198. https://doi.org/10.1111/itor.12640MathSciNetCrossRef

32.

Yildiz A, Ayyildiz E, Gumus AT, 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(02):365–384. https://doi.org/10.1142/S0219622020500017CrossRef

33.

Yildiz A, Ayyildiz E, Taskin-Gumus A, Ozkan C (2021) A framework to prioritize the public expectations from water treatment plants based on trapezoidal type-2 fuzzy ahp method. Environ Manage 67(3):439–448. https://doi.org/10.1007/s00267-020-01367-5CrossRef

34.

Ayyildiz E, Taskin-Gumus A, Erkan M (2020) Individual credit ranking by an integrated interval type-2 trapezoidal fuzzy Electre methodology. Soft Comput 24(21):16149–16163. https://doi.org/10.1007/s00500-020-04929-1CrossRef

35.

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/s11356-020-09640-0CrossRef

36.

Wang H, Qian G, Feng X (2011) An intuitionistic fuzzy AHP based on synthesis of eigenvectors and its application. Inf Technol J 10(10):1850–1866. https://doi.org/10.3923/itj.2011.1850.1866CrossRef

37.

Zhang Q, Huang Y (2012) Early warning index selection and weight assignment for city significant emergency in uncertain environment. Adv Inf Sci Serv Sci 4(23):490–497. https://doi.org/10.4156/AISS.vol4.issue23.61CrossRef

38.

Wu J, bin-Huang H, wei-Cao Q (2013) Research on AHP with interval-valued intuitionistic fuzzy sets and its application in multi-criteria decision making problems. Appl Math Model 37(24):9898–9906. https://doi.org/10.1016/j.apm.2013.05.035MathSciNetCrossRefMATH

39.

Xu Z, Liao H (2014) Intuitionistic fuzzy analytic hierarchy process. IEEE Trans Fuzzy Syst 22(4):749–761. https://doi.org/10.1109/TFUZZ.2013.2272585CrossRef

40.

Tavana M, Zareinejad M, Di Caprio D, Kaviani MA (2016) An integrated intuitionistic fuzzy AHP and SWOT method for outsourcing reverse logistics. Appl Soft Comput J 40:544–557. https://doi.org/10.1016/j.asoc.2015.12.005CrossRef

41.

Büyüközkan G, Güleryüz S (2016) A new integrated intuitionistic fuzzy group decision making approach for product development partner selection. Comput Ind Eng 102:383–395. https://doi.org/10.1016/j.cie.2016.05.038CrossRef

42.

Tooranloo HS, Iranpour A (2017) Supplier selection and evaluation using interval-valued intuitionistic fuzzy AHP method. Int J Procure Manag 10(5):539–554. https://doi.org/10.1504/IJPM.2017.086399CrossRef

43.

Nirmala G, Uthra G (2017) Quality of online shopping websites in india: A study using intuitionistic fuzzy AHP. J Adv Res Dyn Control Syst 9(4):117–124

44.

Otay İ, Oztaysi B, Cevik-Onar S, Kahraman C (2017) Multi-expert performance evaluation of healthcare institutions using an integrated intuitionistic fuzzy AHP&DEA methodology. Knowledge-Based Syst 133:90–106. https://doi.org/10.1016/j.knosys.2017.06.028CrossRef

45.

Sun J, Peng X, Xu Y, Sun B, Ma N, Lv P (2017) VIKOR-GRA based intuitionistic fuzzy multi-attribute WEB service selection. Bol Tec Bull 55(17):982–996

46.

Tooranloo HS, Ayatollah AS, Iranpour A (2018) A model for supplier evaluation and selection based on integrated interval-valued intuitionistic fuzzy AHP-TOPSIS approach. Int J Math Oper Res 13(3):401–417. https://doi.org/10.1504/IJMOR.2018.094854CrossRefMATH

47.

Cebi S, Ilbahar E (2018) Warehouse risk assessment using interval valued intuitionistic fuzzy AHP. Int J Anal Hierarchy Process 10(2):243–253. https://doi.org/10.13033/ijahp.v10i2.549CrossRef

48.

Ouyang X, Guo F (2018) Intuitionistic fuzzy analytical hierarchical processes for selecting the paradigms of mangroves in municipal wastewater treatment. Chemosphere 197:634–642. https://doi.org/10.1016/j.chemosphere.2017.12.102CrossRef

49.

Karasan A, Erdogan M, Ilbahar E (2018) Prioritization of production strategies of a manufacturing plant by using an integrated intuitionistic fuzzy AHP & TOPSIS approach. J Enterp Inf Manag 31(4):510–528. https://doi.org/10.1108/JEIM-01-2018-0001CrossRef

50.

Hinduja A, Pandey M (2018) Assessment of healthcare waste treatment alternatives using an integrated decision support framework. Int J Comput Intell Syst 12(1):318–333. https://doi.org/10.2991/ijcis.2018.125905685CrossRef

51.

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/10.1155/2019/6981657CrossRef

52.

Karasan A (2019) A novel hesitant intuitionistic fuzzy linguistic AHP method and its application to prioritization of investment alternatives. Int J Anal Hierarchy Process 11(1):127–142. https://doi.org/10.13033/ijahp.v11i1.610MathSciNetCrossRef

53.

Büyüközkan G, Göçer F, Karabulut Y (2019) A new group decision making approach with IF AHP and IF VIKOR for selecting hazardous waste carriers. Meas J Int Meas Confed 134:66–82. https://doi.org/10.1016/j.measurement.2018.10.041CrossRef

54.

Taherkhani N, Sepehri MM, Shafaghi S, Khatibi T (2019) Identification and weighting of kidney allocation criteria: a novel multi-expert fuzzy method. BMC Med Inform Decis Mak 19(1):182. https://doi.org/10.1186/s12911-019-0892-yCrossRef

55.

Hinduja A, Pandey M (2019) An integrated intuitionistic fuzzy MCDM approach to select cloud-based ERP system for SMEs. Int J Inf Technol Decis Mak 18(6):1875–1908. https://doi.org/10.1142/S0219622019500378CrossRef

56.

Kahraman C, Öztayşi B, Çevik Onar S (2020) An integrated intuitionistic fuzzy AHP and TOPSIS approach to evaluation of outsource manufacturers. J Intell Syst 29(1):283–297. https://doi.org/10.1515/jisys-2017-0363CrossRef

57.

Dogan O, Deveci M, Canıtez F, Kahraman C (2019) A corridor selection for locating autonomous vehicles using an interval-valued intuitionistic fuzzy AHP and TOPSIS method. Soft Comput. https://doi.org/10.1007/s00500-019-04421-5CrossRef

58.

Büyüközkan G, Havle CA, Feyzioğlu O (2020) “A new digital service quality model and its strategic analysis in aviation industry using interval-valued intuitionistic fuzzy AHP. J Air Transp Manag. https://doi.org/10.1016/j.jairtraman.2020.101817CrossRef

59.

Murat-Ar I, Erol I, Peker I, Ihsan Ozdemir A, Durmus Medeni T, Tolga Medeni I (2020) Evaluating the feasibility of blockchain in logistics operations: a decision framework. Expert Syst Appl. https://doi.org/10.1016/j.eswa.2020.113543CrossRef

60.

Tumsekcali E, Ayyildiz E, Taskin A (2021) Interval valued intuitionistic fuzzy AHP-WASPAS based public transportation service quality evaluation by a new extension of SERVQUAL Model: P-SERVQUAL 4.0. Expert Syst Appl 186:115757. https://doi.org/10.1016/J.ESWA.2021.115757CrossRef

61.

Boran FE, Genç S, Kurt M, Akay D (2009) A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Syst Appl 36(8):11363–11368. https://doi.org/10.1016/j.eswa.2009.03.039CrossRef

62.

Boran F (2011) An integrated intuitionistic fuzzy multi criteria decision making method for facility location selection. Math Comput Appl 16(2):487–496. https://doi.org/10.3390/mca16020487MathSciNetCrossRefMATH

63.

Ning X, Lam KC, Lam MCK (2011) A decision-making system for construction site layout planning. Autom Constr 20(4):459–473. https://doi.org/10.1016/j.autcon.2010.11.014CrossRef

64.

Boran FE, Genç S, Akay D (2011) Personnel selection based on intuitionistic fuzzy sets. Hum Factors Ergon Manuf Serv Ind 21(5):493–503. https://doi.org/10.1002/hfm.20252CrossRef

65.

Su ZX, Chen MY, Xia GP, Wang L (2011) An interactive method for dynamic intuitionistic fuzzy multi-attribute group decision making. Expert Syst Appl 38(12):15286–15295. https://doi.org/10.1016/j.eswa.2011.06.022CrossRef

66.

Daneshvar-Rouyendegh B (2011) The DEA and intuitionistic fuzzy TOPSIS approach to departments’ performances: a pilot study. J Appl Math 2011:16. https://doi.org/10.1155/2011/712194CrossRefMATH

67.

Boran FE, Boran K, Menlik T (2012) The evaluation of renewable energy technologies for electricity generation in Turkey using intuitionistic fuzzy TOPSIS. Energy Sources Part B Econ Plan Policy 7(1):81–90. https://doi.org/10.1080/15567240903047483CrossRef

68.

Rouyendegh BD (2012) Evaluating projects based on intuitionistic fuzzy group decision making. J Appl Math. https://doi.org/10.1155/2012/824265CrossRefMATH

69.

Zhang Q, Li X, Huang Y (2012) The study of alarm severity priority ordering method for the network public sentiment emergency in uncertain environment. Int J Adv Comput Technol 4(23):129–137. https://doi.org/10.4156/ijact.vol4.issue23.15CrossRef

70.

Bai ZY (2013) An interval-valued intuitionistic fuzzy TOPSIS method based on an improved score function. Sci World J. https://doi.org/10.1155/2013/879089CrossRef

71.

Wu Y, Xu H, Geng S (2014) Research on the optimization of solar-wind hybrid power station project selection based on intuitionistic fuzzy TOPSIS method. Energy Educ Sci Technol. Energy Sci Res 32(6):7039–7052

72.

Joshi D, Kumar S (2014) Intuitionistic fuzzy entropy and distance measure based TOPSIS method for multi-criteria decision making. Egypt Informatics J 15(2):97–104. https://doi.org/10.1016/j.eij.2014.03.002CrossRef

73.

Maldonado-Macías A, Alvarado A, García JL, Balderrama CO (2014) Intuitionistic fuzzy TOPSIS for ergonomic compatibility evaluation of advanced manufacturing technology. Int J Adv Manuf Technol 70(9–12):2283–2292. https://doi.org/10.1007/s00170-013-5444-5CrossRef

74.

Rouyendegh BD (2015) Developing an integrated ANP and intuitionistic fuzzy TOPSIS model for supplier selection. J Test Eval 43(3):20130114. https://doi.org/10.1520/jte20130114CrossRef

75.

Li M, Wu C (2015) Green supplier selection based on improved intuitionistic fuzzy TOPSIS model. Metall Min Ind 7(6):193–205

76.

Li C, Zhang L, Zeng S (2015) Typhoon disaster evaluation based on extension of intuitionistic fuzzy TOPSIS method in Zhejiang Province of China. Int J Earth Sci Eng 8(2):1031–1035

77.

Yang S, Wang P, Li G, Huang K (2015) “Group decision making model for weapon selection using extended vikor method under intuitionistic fuzzy environment”, Xitong Fangzhen Xuebao /. J Syst Simul 27(9):2169–2175

78.

Omosigho SE, Omorogbe DEA (2015) Supplier selection using different metric functions. Yugosl J Oper Res 25(3):413–423. https://doi.org/10.2298/YJOR130706028OCrossRefMATH

79.

Li X, Chen X (2015) Multi-criteria group decision making based on trapezoidal intuitionistic fuzzy information. Appl Soft Comput J 30:454–461. https://doi.org/10.1016/j.asoc.2015.01.054CrossRef

80.

Wood DA (2016) Supplier selection for development of petroleum industry facilities, applying multi-criteria decision making techniques including fuzzy and intuitionistic fuzzy TOPSIS with flexible entropy weighting. J Nat Gas Sci Eng 28:594–612. https://doi.org/10.1016/j.jngse.2015.12.021CrossRef

81.

Afful-Dadzie E, Afful-Dadzie A (2016) A decision making model for selecting start-up businesses in a government venture capital scheme. Manag Decis 54(3):714–734. https://doi.org/10.1108/MD-06-2015-0226CrossRefMATH

82.

Büyüközkan G, Güleryüz S (2016) Multi criteria group decision making approach for smart phone selection using intuitionistic fuzzy TOPSIS. Int J Comput Intell Syst 9(4):709–725. https://doi.org/10.1080/18756891.2016.1204119CrossRef

83.

Aikhuele DO, Turan FBM (2016) Intuitionistic fuzzy-based model for failure detection. Springerplus 5(1):1938. https://doi.org/10.1186/s40064-016-3446-0CrossRef

84.

Aikhuele DO, Turan FM, Odofin SM, Ansah RH (2017) Interval-valued Intuitionistic Fuzzy TOPSIS-based model for troubleshooting marine diesel engine auxiliary system. Trans R Inst Nav Archit Part A Int J Marit Eng 159:107–114. https://doi.org/10.3940/rina.ijme.2016.al.402CrossRef

85.

Aikhuele DO, Turan FM (2017) Extended TOPSIS model for solving multi-attribute decision making problems in engineering. Decis Sci Lett 6(4):365–376. https://doi.org/10.5267/j.dsl.2017.2.002CrossRef

86.

Aikhuele DO, Turan FM (2017) An exponential-related function for decision-making in engineering and management. Open Eng 7(1):153–160. https://doi.org/10.1515/eng-2017-0022CrossRef

87.

Abdullah L (2017) A fuzzy decision making method in developing environmental performance index. WSEAS Trans Environ Dev 13:350–359

88.

Song X, Xu J, Shen C, Peña-Mora F, Zeng Z (2017) A decision making system for construction temporary facilities layout planning in large-scale construction projects. Int J Civ Eng 15(2):333–353. https://doi.org/10.1007/s40999-016-0107-1CrossRef

89.

Afful-Dadzie E, Afful-Dadzie A, Oplatková ZK (2017) Assessing commercial viability of technology start-up businesses in a government venture capital under intuitionistic fuzzy environment. Int J Fuzzy Syst 19(2):400–413. https://doi.org/10.1007/s40815-016-0141-9CrossRef

90.

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

91.

Aikhuele DO, Sorooshian S, Ansah RH, Turan FM (2017) Application of intuitionistic fuzzy topsis model for troubleshooting an offshore patrol boat engine. Polish Marit Res 24(2):68–76. https://doi.org/10.1515/pomr-2017-0051CrossRef

92.

Aikhuele D, Turan F (2017) An intuitionistic fuzzy multi-criteria decision-making method based on an exponential-related function. Int J Fuzzy Syst Appl 6(4):33–46. https://doi.org/10.4018/IJFSA.2017100103CrossRef

93.

Liu Y, Bi JW, Fan ZP (2017) A method for ranking products through online reviews based on sentiment classification and interval-valued intuitionistic fuzzy topsis. Int J Inf Technol Decis Mak 16(6):1497–1522. https://doi.org/10.1142/S021962201750033XCrossRef

94.

Babu AVS, Devi PM, Sharmila B (2018) Efficient enhanced intrusion identification and response system for MANETs. Int J Bus Inf Syst 29(4):535–546. https://doi.org/10.1504/IJBIS.2018.096036CrossRef

95.

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

96.

Sen DK, Datta S, Mahapatra SS (2018) Sustainable supplier selection in intuitionistic fuzzy environment: a decision-making perspective. Benchmarking 25(2):545–574. https://doi.org/10.1108/BIJ-11-2016-0172CrossRef

97.

Shen F, Ma X, Li Z, Xu Z, Cai D (2018) An extended intuitionistic fuzzy TOPSIS method based on a new distance measure with an application to credit risk evaluation. Inf Sci (Ny) 428:105–119. https://doi.org/10.1016/j.ins.2017.10.045MathSciNetCrossRef

98.

Memari A, Dargi A, Akbari Jokar MR, Ahmad R, Abdul Rahim AR (2019) Sustainable supplier selection: a multi-criteria intuitionistic fuzzy TOPSIS method. J Manuf Syst 50:9–24. https://doi.org/10.1016/j.jmsy.2018.11.002CrossRef

99.

El Hachami K, Alaoui YL, Tkiouat M (2019) Sectorial evaluation of islamic banking contracts: a fuzzy multi-criteria-decision-making approach. Invest Manag Financ Innov 16(2):370–382. https://doi.org/10.21511/imfi.16(2).2019.31CrossRef

100.

Aikhuele DO (2019) A flexible model for supporting designers and determining design stakeholders’ preferences at the early product design stage. Int J Data Netw Sci 3(2):109–118. https://doi.org/10.5267/j.ijdns.2018.12.004CrossRef

101.

Cavallaro F, Zavadskas EK, Streimikiene D, Mardani A (2019) Assessment of concentrated solar power (CSP) technologies based on a modified intuitionistic fuzzy topsis and trigonometric entropy weights. Technol Forecast Soc Change 140:258–270. https://doi.org/10.1016/j.techfore.2018.12.009CrossRef

102.

Anshu K, Gaur L (2019) E-satisfaction estimation: a comparative analysis using AHP and intuitionistic fuzzy TOPSIS. J Cases Inf Technol 21(2):65–87. https://doi.org/10.4018/JCIT.2019040105CrossRef

103.

Kansal Y, Kapur PK, Sachdeva N (2019) Determining best patch management software using intuitionistic fuzzy sets with TOPSIS. Int J Performability Eng 15(5):1297–1305. https://doi.org/10.23940/ijpe.19.05.p5.12971305CrossRef

104.

Pal K, Kumar V, Arora HD, Kumar S (2019) Application of TOPSIS in the diagnosis of vector borne diseases. Int J Eng Adv Technol 8(6):5217–5223. https://doi.org/10.35940/ijeat.F8585.088619CrossRef

105.

Roy SK, Midya S (2019) Multi-objective fixed-charge solid transportation problem with product blending under intuitionistic fuzzy environment. Appl Intell 49(10):3524–3538. https://doi.org/10.1007/s10489-019-01466-9CrossRef

106.

Faghih-Roohi S, Akcay A, Zhang Y, Shekarian E, de Jong E (2020) A group risk assessment approach for the selection of pharmaceutical product shipping lanes. Int J Prod Econ. https://doi.org/10.1016/j.ijpe.2020.107774CrossRef

107.

Kilic HS, Yalcin AS (2020) Modified two-phase fuzzy goal programming integrated with IF-TOPSIS for green supplier selection. Appl Soft Comput. https://doi.org/10.1016/j.asoc.2020.106371CrossRef

108.

Zhang L, Zhan J, Yao Y (2020) Intuitionistic fuzzy TOPSIS method based on CVPIFRS models: an application to biomedical problems. Inf Sci (Ny) 517:315–339. https://doi.org/10.1016/j.ins.2020.01.003MathSciNetCrossRef

109.

Rouyendegh BD, Yildizbasi A, Üstünyer P (2020) Intuitionistic Fuzzy TOPSIS method for green supplier selection problem. Soft Comput 24(3):2215–2228. https://doi.org/10.1007/s00500-019-04054-8CrossRef

110.

Rouyendegh BD, Yildizbasi A, Yilmaz I (2020) Evaluation of retail ındustry performance ability through ıntegrated ıntuitionistic fuzzy TOPSIS and data envelopment analysis approach. Soft Comput. https://doi.org/10.1007/s00500-020-04669-2CrossRef

111.

Abdullah L, Goh C, Zamri N, Othman M (2020) Application of interval valued intuitionistic fuzzy TOPSIS for flood management. J Intell Fuzzy Syst 38(1):873–881. https://doi.org/10.3233/JIFS-179455CrossRef

112.

Olivares-Aguila J, ElMaraghy H (2020) Co-development of product and supplier platform. J Manuf Syst 54:372–385. https://doi.org/10.1016/j.jmsy.2020.01.010CrossRef

113.

Gao Y, Sheng-Li D, Zhong H (2020) A novel target threat assessment method based on three-way decisions under intuitionistic fuzzy multi-attribute decision making environment. Eng Appl Artif Intell. https://doi.org/10.1016/j.engappai.2019.103276CrossRef

114.

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/s40747-020-00221-9CrossRef

115.

Uyanik C, Tuzkaya G, Kalender ZT, Oguztimur S (2020) An integrated dematel–if-topsis methodology for logistics centers’ location selection problem: an application for Istanbul metropolitan area. Transport 35(6):548–556. https://doi.org/10.3846/transport.2020.12210CrossRef

116.

Talarico L, Sörensen K, Springael J (2017) A biobjective decision model to increase security and reduce travel costs in the cash-in-transit sector. Int Trans Oper Res 24(1–2):59–76. https://doi.org/10.1111/itor.12214MathSciNetCrossRefMATH

117.

Liang Z-C, Yang Y, Liao S-G (2022) Interval-valued intuitionistic fuzzy two-sided matching model considering level of automation. Appl Soft Comput 116:108252. https://doi.org/10.1016/J.ASOC.2021.108252CrossRef

Title: An integrated interval-valued intuitionistic fuzzy AHP-TOPSIS methodology to determine the safest route for cash in transit operations: a real case in Istanbul
Authors: Aslihan Yildiz
Ali Fuat Guneri
Coskun Ozkan
Ertugrul Ayyildiz
Alev Taskin
Publication date: 26-04-2022
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 18/2022
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-022-07236-y

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 18/2022

A meta-heuristic-based energy efficient route modeling for EV on non-identical road surfaces

Efficient deep learning-based semantic mapping approach using monocular vision for resource-limited mobile robots

An improved binary sparrow search algorithm for feature selection in data classification

Fast homomorphic SVM inference on encrypted data

Image based rainfall amount estimation for auto-wiping of vehicles

An integrated cyber security risk management framework and risk predication for the critical infrastructure protection

Premium Partner