Advertisement

New multi-criteria LNN WASPAS model for evaluating the work of advisors in the transport of hazardous goods

  • Dragan PamučarEmail author
  • Siniša Sremac
  • Željko Stević
  • Goran Ćirović
  • Dejan Tomić
Original Article
  • 32 Downloads

Abstract

Successfully organizing the transport of hazardous materials and handling them correctly is a very important logistical task that affects both the overall flow of transport and the environment. Safety advisors for the transport of hazardous materials have a very important role to play in the proper and safe development of the transport flow for these materials; their task is primarily to use their knowledge and effort to prevent potential accidents from happening. In this research, a total of 21 safety advisors for the transport of hazardous materials in Serbia are assessed using a new model that integrates Linguistic Neutrosophic Numbers (LNN) and the WASPAS (Weighted Aggregated Sum Product Assessment) method. In this way, two important contributions are made, namely a completely new methodology for assessing the work of advisors and the new LNN WASPAS model, which enriches the field of multi-criteria decision making. The advisors are assessed by seven experts on the basis of nine criteria. After performing a sensitivity analysis on the results, validation of the model is carried out. The results obtained by the LNN WASPAS model are validated by comparing them with the results obtained by LNN extensions of the TOPSIS (Technique for Order Performance by Similarity to Ideal Solution), LNN CODAS (COmbinative Distance-based ASsessment), LNN VIKOR (Multi-criteria Optimization and Compromise Solution) and LNN MABAC (Multi-Attributive Border Approximation area Comparison) models. The LNN CODAS, LNN VIKOR and LNN MABAC are also further developed in this study, which is an additional contribution made by the paper. After the sensitivity analysis, the SCC (Spearman Correlation Coefficient) is calculated which confirms the stability of the previously obtained results.

Keywords

Linguistic neutrosophic numbers WASPAS Multi-criteria decision making Hazardous goods 

Notes

Acknowledgements

The work reported in this paper is a part of the investigation within the research projects TR 36017 and VA-TT/4/17-19 supported by the Ministry for Science and Technology (Republic of Serbia), Ministry of Defence (Republic of Serbia) and the University of defence in Belgrade. This support is gratefully acknowledged.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

References

  1. 1.
    Abdel-Basset M, Manogaran G, Gamal A, Smarandache F (2018) A hybrid approach of neutrosophic sets and DEMATEL method for developing supplier selection criteria. Des Autom Embed Syst 22:1–22Google Scholar
  2. 2.
    Ali M, Dat LQ, Smarandache F (2018) Interval complex neutrosophic set: formulation and applications in decision-making. Int J Fuzzy Syst 20(3):986–999Google Scholar
  3. 3.
    Atanassov KT (1986) Intuitionistic fuzzy sets. Fuzzy Sets Syst 20:87–96zbMATHGoogle Scholar
  4. 4.
    Baušys R, Juodagalvienė B (2017) Garage location selection for residential house by WASPAS-SVNS method. J Civ Eng Manag 23(3):421–429Google Scholar
  5. 5.
    Bausys R, Zavadskas EK (2015) Multicriteria decision making approach by vikor under interval neutrosophic set environment. Econ Comput Econ Cybern Stud Res 49(4):33–48Google Scholar
  6. 6.
    Bausys R, Zavadskas EK, Kaklauskas A (2015) Application of neutrosophic set to multicriteria decision making by COPRAS. Econ Comput Econ Cybern Stud Res 49(2):91–106Google Scholar
  7. 7.
    Biswas P, Pramanik S, Giri CB (2016) TOPSIS method for multi-attribute group decision-making under single-valued neutrosophic environment. Neural Comput Appl 27:727–737.  https://doi.org/10.1007/s00521-015-1891-2 Google Scholar
  8. 8.
    Bolturk E, Kahraman C (2018) A novel interval-valued neutrosophic AHP with cosine similarity measure. Soft Comput 22:1–18Google Scholar
  9. 9.
    Chen ZC, Liu PH, Pei Z (2015) An approach to multiple attribute group decision making based on linguistic intuitionistic fuzzy numbers. Int J Comput Intell Syst 8:747–760Google Scholar
  10. 10.
    Directive 2008/68/EC Of the European Parliament and of the Council of 24 September 2008 On the inland transport of dangerous goods, 2008Google Scholar
  11. 11.
    Ebrahimi H, Tadic M (2018) Optimization of dangerous goods transport in urban zone. Decis Mak Appl Manag Eng 1(2):131–152.  https://doi.org/10.31181/dmame1802138e Google Scholar
  12. 12.
    European agreement concerning the international carriage of dangerous goods by inland waterways (ADN) 2017, including the annexed regulations, Applicable as from 1 January 2017, Inland Transport Committee of the Economic Commission for Europe, 2017Google Scholar
  13. 13.
    Fan C, Ye J, Hu K, Fan E (2017) Bonferroni mean operators of linguistic neutrosophic numbers and their multiple attribute group decision-making methods. Information 8:107Google Scholar
  14. 14.
    Fang Z, Ye J (2017) Multiple attribute group decision-making method based on linguistic neutrosophic numbers. Symmetry 9:111MathSciNetGoogle Scholar
  15. 15.
    Ghaderi SF, Azadeh A, Nokhandan BP, Fathi E (2012) Behavioral simulation and optimization of generation companies in electrical markets by fuzzy cognitive map. Expert Syst Appl 39:4635–4646Google Scholar
  16. 16.
    Graham M, Walter TS, Yawson A, Zhang H (2017) The value-added role of industry specialist advisors in M&As. J Bank Finance 81:81–104.  https://doi.org/10.1016/j.jbankfin.2017.04.010 Google Scholar
  17. 17.
    Hashemkhani Zolfani S, Aghdaie MH, Derakhti A, Zavadskas EK, Varzandeh MHM (2013) Decision making on business issues with foresight perspective; an application of new hybrid MCDM model in shopping mall locating. Expert Syst Appl 40:7111–7121Google Scholar
  18. 18.
    Herrera F, Herrera-Viedma E, Verdegay L (1996) A model of consensus in group decision making under linguistic assessments. Fuzzy Sets Syst 79(1):73–87MathSciNetzbMATHGoogle Scholar
  19. 19.
    Herrera F, Herrera-Viedma E (2000) Linguistic decision analysis: steps for solving decision problems under linguistic information. Fuzzy Sets Syst 115:67–82MathSciNetzbMATHGoogle Scholar
  20. 20.
    Huang YH, Wei GW, Wei C (2017) VIKOR method for interval neutrosophic multiple attribute group decision-making. Information 8(4):144MathSciNetGoogle Scholar
  21. 21.
    Hwang CL, Yoon K (1981) Multiple attribute decision making: methods and applications. Springer, New YorkzbMATHGoogle Scholar
  22. 22.
    Ji P, Zhang HY, Wang JQ (2018) A projection-based TODIM method under multi-valued neutrosophic environments and its application in personnel selection. Neural Comput Appl 29(1):221–234Google Scholar
  23. 23.
    Karaşan A, Kahraman C (2017) Interval-valued neutrosophic extension of EDAS method. In: Kacprzyk J, Szmidt E, Zadrożny S, Atanassov KT, Krawczak M (eds) Advances in fuzzy logic and technology 2017, Warsaw, Poland, 13–15 September 2017. Springer, Cham, pp 343–357Google Scholar
  24. 24.
    Karnik NN, Mendel JM (2001) Operations on type-2 fuzzy sets. Fuzzy Sets Syst 122(2):327–348MathSciNetzbMATHGoogle Scholar
  25. 25.
    Keshavarz Ghorabaee M, Zavadskas EK, Olfat L, Turskis Z (2015) Multi-criteria inventory classification using a new method of evaluation based on distance from average solution (EDAS). Informatica 26(3):435–451Google Scholar
  26. 26.
    Kour D, Basu K (2017) Selection of transportation companies and their mode of transportation for interval valued data. Neutrosophic Sets Syst 18:67–79Google Scholar
  27. 27.
    Lahtinen KD, Shipe S (2017) Readability of financial advisor disclosures. J Empir Finance 44(C):36–42.  https://doi.org/10.1016/j.jempfin.2017.08.002 Google Scholar
  28. 28.
    Liang R, Wang J, Zhang H (2017) Evaluation of e-commerce websites: an integrated approach under a single-valued trapezoidal neutrosophic environment. Knowl Based Syst 135:44–59Google Scholar
  29. 29.
    Liang W, Zhao G, Hong C (2018) Selecting the optimal mining method with extended multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA) approach. Neural Comput Appl 3405-5:1–16Google Scholar
  30. 30.
    Liang W, Zhao G, Wu H (2017) Evaluating investing risks of metallic mines using an extended TOPSIS method with linguistic neutrosophic numbers. Symmetry 9:149Google Scholar
  31. 31.
    Nettle R, Crawford A, Brightling P (2018) How private-sector farm advisors change their practices: an Australian case study. J Rural Stud 58:20–27.  https://doi.org/10.1016/j.jrurstud.2017.12.027 Google Scholar
  32. 32.
    Nie RX, Wang JQ, Zhang HY (2017) Solving solar-wind power station location problem using an extended weighted aggregated sum product assessment (WASPAS) technique with interval neutrosophic sets. Symmetry 9(7):106Google Scholar
  33. 33.
    Nunić Z (2018) Evaluation and selection of the PVC carpentry Manufacturer using the FUCOM-MABAC model. Oper Res Eng Sci Theory Appl 1(1):13–28Google Scholar
  34. 34.
    Opricović S, Tzeng GH (2004) Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. Eur J Oper Res 156(2):445–455zbMATHGoogle Scholar
  35. 35.
    Otay I, Kahraman C (2017) Six sigma project selection using interval neutrosophic TOPSIS. In: Kacprzyk J, Szmidt E, Zadrożny S, Atanassov KT, Krawczak M (eds) Advances in fuzzy logic and technology 2017, Warsaw, Poland, 13–15 September 2017. Springer, Cham, pp 83–93Google Scholar
  36. 36.
    Pamucar D, Bozanic D, Lukovac V, Komazec N (2018) Normalized weighted geometric bonferroni mean operator of interval rough numbers: application in interval rough DEMATEL-COPRAS. Mech Eng 16(2):171–191Google Scholar
  37. 37.
    Pamučar D, Ćirović G (2015) The selection of transport and handling resources in logistics centres using multi-attributive border approximation area comparison (MABAC). Expert Syst Appl 42:3016–3028Google Scholar
  38. 38.
    Pamucar D, Mihajlovic M, Obradovic R, Atanaskovic P (2017) Novel approach to group multi-criteria decision making based on interval rough numbers: hybrid DEMATEL-ANP-MAIRCA model. Expert Syst Appl 88:58–80Google Scholar
  39. 39.
    Pan K, Blankley AI, Mazzei JM, Frownfelter Lohrke C, Marshall JB, Carson CM (2018) Surveying industry advisors to select data analytics topics for all business majors. Int J Manag Educ 16(3):483–492.  https://doi.org/10.1016/j.ijme.2018.09.001 Google Scholar
  40. 40.
    Peng JJ, Wang JQ, Yang LJ, Qian J (2017) A novel multi-criteria group decision-making approach using simplified neutrosophic information. Int J Uncertain Quantif 7(4):355–376Google Scholar
  41. 41.
    Peng X, Dai J (2018) Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function. Neural Comput Appl 29(10):939–954Google Scholar
  42. 42.
    Popovic M, Kuzmanovic M, Savic G (2018) A comparative empirical study of analytic hierarchy process and conjoint analysis: literature review. Decis Mak Appl Manag Eng 1(2):153–163.  https://doi.org/10.31181/dmame1802160p Google Scholar
  43. 43.
    Radwan NM, Senousy MB, Alaa El Din MR (2016) Neutrosophic AHP multi criteria decision making method applied on the selection of learning management system. Int J Adv Comput Technol 8(5):95–105Google Scholar
  44. 44.
    Regulations concerning the International Carriage of Dangerous Goods by Rail (RID), Convention concerning International Carriage by Rail (COTIF) Appendix C, Intergovernmental Organisation for International Carriage by Rail (OTIF), 2017Google Scholar
  45. 45.
    Rizk-Allah RM, Hassanien AE, Elhoseny M (2018) A multi-objective transportation model under neutrosophic environment. Comput Electr Eng 69:705–719Google Scholar
  46. 46.
    Şahin R, Yiğider M (2014) A multi-criteria neutrosophic group decision making metod based TOPSIS for supplier selection. arXiv preprint arXiv:1412.5077
  47. 47.
    Singh A, Kumar A, Appadoo SS (2017) Modified approach for optimization of real life transportation problem in neutrosophic environment. Math Probl Eng 2017:1–9MathSciNetGoogle Scholar
  48. 48.
    Smarandache F (1999) A unifying field in logics. Neutrosophy: neutrosophic probability, set and logic. American Research Press, RehobothzbMATHGoogle Scholar
  49. 49.
    Smarandache F (2005) A generalization of the intuitionistic fuzzy set. Int J Pure Appl Math 24:287–297MathSciNetzbMATHGoogle Scholar
  50. 50.
    Stanujkić D, Karabašević D (2018) An extension of the WASPAS method for decision-making problems with intuitionistic fuzzy numbers: a case of website evaluation. Oper Res Eng Sci Theory Appl 1(1):29–39Google Scholar
  51. 51.
    Stanujkic D, Zavadskas EK, Karabasevic D, Smarandache F (2016) Multiple criteria evaluation model based on the single valued neutrosophic set. Neutrosophic Sets Syst 14:3–6Google Scholar
  52. 52.
    Stević Ž, Pamučar D, Vasiljević M, Stojić G, Korica S (2017) Novel integrated multi-criteria model for supplier selection: case study construction company. Symmetry 9(11):279Google Scholar
  53. 53.
    Stević Ž, Pamučar D, Kazimieras Zavadskas E, Ćirović G, Prentkovskis O (2017) The selection of wagons for the internal transport of a logistics company: a novel approach based on rough BWM and rough SAW methods. Symmetry 9(11):264Google Scholar
  54. 54.
    Thamaraiselvi A, Santhi R (2016) A new approach for optimization of real life transportation problem in neutrosophic environment. Math Probl Eng 2016:1–9Google Scholar
  55. 55.
    European Agreement Concerning the International Carriage of Dangerous Goods by Inland Waterways (ADN) 2017, Including the Annexed Regulations, Applicable as from 1 January 2017, Inland Transport Committee of the Economic Commission for Europe, 2017Google Scholar
  56. 56.
    Tian ZP, Wang JQ, Zhang HY (2018) Hybrid single-valued neutrosophic MCGDM with QFD for market segment evaluation and selection. J Intell Fuzzy Syst 34(1):177–187Google Scholar
  57. 57.
    Tian ZP, Wang J, Wang JQ, Zhang HY (2017) An improved MULTIMOORA approach for multi-criteria decision-making based on interdependent inputs of simplified neutrosophic linguistic information. Neural Comput Appl 28(1):585–597Google Scholar
  58. 58.
    Tian ZP, Wang J, Zhang HY, Wang JQ (2018) Multi-criteria decision-making based on generalized prioritized aggregation operators under simplified neutrosophic uncertain linguistic environment. Int J Mach Learn Cybern 9(3):523–539Google Scholar
  59. 59.
    Xu ZS (2006) A note on linguistic hybrid arithmetic averaging operator in multiple attribute group decision making with linguistic information. Group Decis Negot 15(6):593–604Google Scholar
  60. 60.
    Xu ZS (2006) Goal programming models for multiple attribute decision making under linguistic setting. Chin J Manag Sci 9(2):9–17Google Scholar
  61. 61.
    Ye J (2015) An extended TOPSIS method for multiple attribute group decision making based on single valued neutrosophic linguistic numbers. J Intell Fuzzy Syst 28:247–255MathSciNetGoogle Scholar
  62. 62.
    Zadeh LA (1965) Fuzzy sets. Inf Control 8(3):338–353zbMATHGoogle Scholar
  63. 63.
    Zavadskas EK, Baušys R, Stanujkic D (2016) Selection of lead-zinc flotation circuit design by applying WASPAS method with single-valued neutrosophic set. Acta Montan Slovaca 21(2):85–92Google Scholar
  64. 64.
    Zavadskas EK, Bausys R, Juodagalviene B, Garnyte-Sapranaviciene I (2017) Model for residential house element and material selection by neutrosophic MULTIMOORA method. Eng Appl Artif Intell 64:315–324Google Scholar
  65. 65.
    Zavadskas EK, Bausys R, Lazauskas M (2015) Sustainable assessment of alternative sites for the construction of a waste incineration plant by applying WASPAS method with single valued neutrosophic set. Sustainability 7:15923–15936Google Scholar
  66. 66.
    Zimmermann HJ (1996) Fuzzy set theory and its applications. Kluwer Academic Publishers, BostonzbMATHGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Logistics, Military AcademyUniversity of Defence in BelgradeBelgradeSerbia
  2. 2.Faculty of Technical SciencesUniversity of Novi SadNovi SadSerbia
  3. 3.Faculty of Transport and Traffic EngineeringUniversity of East SarajevoDobojBosnia and Herzegovina
  4. 4.Provincial Secretariat for Energy, Construction and TransportAutonomous Province of VojvodinaNovi SadSerbia

Personalised recommendations