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Criteria Assessment in Sustainable Macromanagement of Housing Provision Problem by a Multi-phase Decision Approach with DEMATEL and Dynamic Uncertainty

  • H. Salarpour
  • G. Ghodrati AmiriEmail author
  • S. Meysam Mousavi
Research Article - Systems Engineering
  • 12 Downloads

Abstract

Macromanagement of housing provision based on sustainable development characteristics may be required for applying some different strategies to ensure the housing preparation regarding economic, environmental, and social competencies. In this regard, there are some challenges, including defining the criteria in a hierarchical structure to check the problem in all aspects, computing experts’ weights for reducing judgments’ errors, computing interdependencies effects among criteria to increase the precise of criteria weight determination, dynamic criteria’ weights evaluation to analyze the criteria importance in some different periods, and uncertainty modeling of criteria weights computations in the research area, in which recent studies did not focus them simultaneously. To address the issue, this study proposes a multi-phase weighting approach by combining the collective index and decision-making trial and evaluation laboratory (DEMATEL) methodologies via dynamic interval-valued hesitant fuzzy sets (DIVHFSs) theory. Considering dynamic uncertainty is needed for covering the incomplete information in various periods because the significance and impact of evaluation criteria may be changed in each period. On the other hand, DIVHFSs theory could assist the experts by assigning some dynamic interval-valued hesitant fuzzy (DIVHF)-membership degrees for an element under a set to decrease the errors. In proposed DIVHF-collective index method, criteria local weights are obtained based on correlation and standard deviation approaches under a DIVHF-environment. In addition, the extended DEMATEL methodology is prepared for taking account of hierarchical structure and criteria interdependencies relations. Moreover, a novel DIVHF-utility degree method is presented to compute the weight of each expert. Then, experts’ weights are taken into account in the presented multi-phase weighting approach. In addition, the DIVHF-multi-phase weighting approach based on collective index and DEMATEL (DIVHF-MPW-CI–DEMATEL) methodologies is proposed via a last aggregation approach to keep away from the data loss. Meanwhile, some needed operations on DIVHFS theory for presenting DIVHF-MPW-CI–DEMATEL methodology are extended. Finally, a real case study about the determining criteria’ weights for strategy selection in macromanagement of housing provision problem based on sustainable development properties is prepared to indicate the feasibility and applicability of proposed DIVHF-MPW-CI–DEMATELs approach.

Keywords

Sustainable development Macromanagement of housing provision problem Dynamic interval-valued hesitant fuzzy sets Criteria assessment DEMATEL 

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Notes

Acknowledgements

The authors would like to thank anonymous reviewers for valuable comments on the primary version.

Author contributions

Authors of this research confirm the change on authorship based on their contributions in the revised version.

References

  1. 1.
    Herrera-Viedma, E.; Herrera, F.; Chiclana, F.: A consensus model for multiperson decision making with different preference structures. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 32(3), 394–402 (2002)zbMATHGoogle Scholar
  2. 2.
    Xu, Z.: A method based on linguistic aggregation operators for group decision making with linguistic preference relations. Inf. Sci. 166(1), 19–30 (2004)MathSciNetzbMATHGoogle Scholar
  3. 3.
    Herrera-Viedma, E.; Martinez, L.; Mata, F.; Chiclana, F.: A consensus support system model for group decision-making problems with multigranular linguistic preference relations. IEEE Trans. Fuzzy Syst. 13(5), 644–658 (2005)Google Scholar
  4. 4.
    Xu, Z.: Multiple-attribute group decision making with different formats of preference information on attributes. IEEE Trans. Syst. Man Cybern. Part B Cybern. 37(6), 1500–1511 (2007)Google Scholar
  5. 5.
    Xu, Z.: Group decision making based on multiple types of linguistic preference relations. Inf. Sci. 178(2), 452–467 (2008)MathSciNetzbMATHGoogle Scholar
  6. 6.
    Chiclana, F.; Herrera-Viedma, E.; Alonso, S.; Herrera, F.: Cardinal consistency of reciprocal preference relations: a characterization of multiplicative transitivity. IEEE Trans. Fuzzy Syst. 17(1), 14–23 (2009)Google Scholar
  7. 7.
    Pérez, I.J.; Cabrerizo, F.J.; Herrera-Viedma, E.: A mobile decision support system for dynamic group decision-making problems. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 40(6), 1244–1256 (2010)Google Scholar
  8. 8.
    İç, Y.T.: An experimental design approach using TOPSIS method for the selection of computer-integrated manufacturing technologies. Robot. Comput. Integr. Manuf. 28(2), 245–256 (2012)Google Scholar
  9. 9.
    Du, Y.; Gao, C.; Hu, Y.; Mahadevan, S.; Deng, Y.: A new method of identifying influential nodes in complex networks based on TOPSIS. Physica A Stat. Mech. Appl. 399, 57–69 (2014)Google Scholar
  10. 10.
    Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)zbMATHGoogle Scholar
  11. 11.
    Doria, S.: Characterization of a coherent upper conditional prevision as the Choquet integral with respect to its associated Hausdorff outer measure. Ann. Oper. Res. 195(1), 33–48 (2012)MathSciNetzbMATHGoogle Scholar
  12. 12.
    Paksoy, T.; Pehlivan, N.Y.; Kahraman, C.: Organizational strategy development in distribution channel management using fuzzy AHP and hierarchical fuzzy TOPSIS. Expert Syst. Appl. 39(3), 2822–2841 (2012)Google Scholar
  13. 13.
    Melin, P.; Castillo, O.: A review on the applications of type-2 fuzzy logic in classification and pattern recognition. Expert Syst. Appl. 40(13), 5413–5423 (2013)Google Scholar
  14. 14.
    Melin, P.; Castillo, O.: A review on type-2 fuzzy logic applications in clustering, classification and pattern recognition. Appl. Soft Comput. 21, 568–577 (2014)Google Scholar
  15. 15.
    Greco, S.; Matarazzo, B.; Giove, S.: The Choquet integral with respect to a level dependent capacity. Fuzzy Sets Syst. 175(1), 1–35 (2011)MathSciNetzbMATHGoogle Scholar
  16. 16.
    Keramitsoglou, I.; Kiranoudis, C.T.; Maiheu, B.; De Ridder, K.; Daglis, I.A.; Manunta, P.; Paganini, M.: Heat wave hazard classification and risk assessment using artificial intelligence fuzzy logic. Environ. Monit. Assess. 185(10), 8239–8258 (2013)Google Scholar
  17. 17.
    Keršulienė, V.; Turskis, Z.: A hybrid linguistic fuzzy multiple criteria group selection of a chief accounting officer. J. Bus. Econ. Manag. 15(2), 232–252 (2014)Google Scholar
  18. 18.
    Qin, J.; Liu, X.; Pedrycz, W.: A multiple attribute interval type-2 fuzzy group decision making and its application to supplier selection with extended LINMAP method. Soft Comput. 21(12), 3207–3226 (2017)zbMATHGoogle Scholar
  19. 19.
    Zegordi, S.; Nik, E.; Nazari, A.: Power plant project risk assessment using a fuzzy-anp and fuzzy-topsis method. Int. J. Eng. Trans. B Appl. 25(2), 107 (2012)Google Scholar
  20. 20.
    Mousavi, S.M.: A new interval-valued hesitant fuzzy pairwise comparison–compromise solution methodology: an application to cross-docking location planning. Neural Comput. Appl. 1–15 (2018). https://doi.org/10.1007/s00521-018-3355-y
  21. 21.
    Gitinavard, H.; Mousavi, S.M.; Vahdani, B.: Soft computing-based new interval-valued hesitant fuzzy multi-criteria group assessment method with last aggregation to industrial decision problems. Soft Comput. 21(12), 3247–3265 (2017)zbMATHGoogle Scholar
  22. 22.
    Ebrahimnejad, S.; Naeini, M.; Gitinavard, H.; Mousavi, S.M.: Selection of IT outsourcing services’ activities considering services cost and risks by designing an interval-valued hesitant fuzzy-decision approach. J. Intell. Fuzzy Syst. 32(6), 4081–4093 (2017)zbMATHGoogle Scholar
  23. 23.
    Parreiras, R.; Ekel, P.Y.; Martini, J.; Palhares, R.M.: A flexible consensus scheme for multicriteria group decision making under linguistic assessments. Inf. Sci. 180(7), 1075–1089 (2010)Google Scholar
  24. 24.
    Vahdani, B.; Zandieh, M.: Selecting suppliers using a new fuzzy multiple criteria decision model: the fuzzy balancing and ranking method. Int. J. Prod. Res. 48(18), 5307–5326 (2010)zbMATHGoogle Scholar
  25. 25.
    Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96 (1986)MathSciNetzbMATHGoogle Scholar
  26. 26.
    Xu, Z.: Intuitionistic preference relations and their application in group decision making. Inf. Sci. 177(11), 2363–2379 (2007)MathSciNetzbMATHGoogle Scholar
  27. 27.
    Gitinavard, H.; Mousavi, S.; Vahdani, B.: A balancing and ranking method based on hesitant fuzzy sets for solving decision-making problems under uncertainty. IJE Trans. B Appl. 28(2), 214–223 (2015)Google Scholar
  28. 28.
    Foroozesh, N.; Gitinavard, H.; Mousavi, S.M.; Vahdani, B.: A hesitant fuzzy extension of VIKOR method for evaluation and selection problems under uncertainty. Int. J. Appl. Manag. Sci. 9(2), 95–113 (2017)Google Scholar
  29. 29.
    Mousavi, M.; Gitinavard, H.; Mousavi, S.: A soft computing based-modified ELECTRE model for renewable energy policy selection with unknown information. Renew. Sustain. Energy Rev. 68, 774–787 (2017)Google Scholar
  30. 30.
    Tanino, T.: Fuzzy preference orderings in group decision making. Fuzzy Sets Syst. 12(2), 117–131 (1984)MathSciNetzbMATHGoogle Scholar
  31. 31.
    Wang, W.-C.; Xu, D.-M.; Chau, K.-W.; Lei, G.-J.: Assessment of river water quality based on theory of variable fuzzy sets and fuzzy binary comparison method. Water Resour. Manag. 28(12), 4183–4200 (2014)Google Scholar
  32. 32.
    Sefeedpari, P.; Rafiee, S.; Akram, A.; Chau, K.-W.; Pishgar-Komleh, S.H.: Prophesying egg production based on energy consumption using multi-layered adaptive neural fuzzy inference system approach. Comput. Electron. Agric. 131, 10–19 (2016)Google Scholar
  33. 33.
    Chen, X.-Y.; Chau, K.-W.; Wang, W.-C.: A novel hybrid neural network based on continuity equation and fuzzy pattern-recognition for downstream daily river discharge forecasting. J. Hydroinform. 17(5), 733–744 (2015)Google Scholar
  34. 34.
    Zhang, S.; Chau, K.-W.: Dimension reduction using semi-supervised locally linear embedding for plant leaf classification. In: International Conference on Intelligent Computing. Springer (2009)Google Scholar
  35. 35.
    Taormina, R.; Chau, K.-W.; Sivakumar, B.: Neural network river forecasting through baseflow separation and binary-coded swarm optimization. J. Hydrol. 529, 1788–1797 (2015)Google Scholar
  36. 36.
    Wu, C.; Chau, K.: Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis. J. Hydrol. 399(3–4), 394–409 (2011)Google Scholar
  37. 37.
    Kacprzyk, J.; Fedrizzi, M.; Nurmi, H.: Group decision making and consensus under fuzzy preferences and fuzzy majority. Fuzzy Sets Syst. 49(1), 21–31 (1992)MathSciNetzbMATHGoogle Scholar
  38. 38.
    Wang, Y.-M.; Elhag, T.: Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment. Expert Syst. Appl. 31(2), 309–319 (2006)Google Scholar
  39. 39.
    Chen, T.-Y.; Tsao, C.-Y.: The interval-valued fuzzy TOPSIS method and experimental analysis. Fuzzy Sets Syst. 159(11), 1410–1428 (2008)MathSciNetzbMATHGoogle Scholar
  40. 40.
    Mata, F.; Martínez, L.; Herrera-Viedma, E.: An adaptive consensus support model for group decision-making problems in a multigranular fuzzy linguistic context. IEEE Trans. Fuzzy Syst. 17(2), 279–290 (2009)Google Scholar
  41. 41.
    Chen, S.-M.; Niou, S.-J.: Fuzzy multiple attributes group decision-making based on fuzzy preference relations. Expert Syst. Appl. 38(4), 3865–3872 (2011)Google Scholar
  42. 42.
    Kaya, T.; Kahraman, C.: Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology. Expert Syst. Appl. 38(6), 6577–6585 (2011)Google Scholar
  43. 43.
    Devi, K.; Yadav, S.P.: A multicriteria intuitionistic fuzzy group decision making for plant location selection with ELECTRE method. Int. J. Adv. Manuf. Technol. 66(9–12), 1219–1229 (2013)Google Scholar
  44. 44.
    Igoulalene, I.; Benyoucef, L.; Tiwari, M.K.: Novel fuzzy hybrid multi-criteria group decision making approaches for the strategic supplier selection problem. Expert Syst. Appl. 42(7), 3342–3356 (2015)Google Scholar
  45. 45.
    Büyüközkan, G.; Güleryüz, S.: A new integrated intuitionistic fuzzy group decision making approach for product development partner selection. Comput. Ind. Eng. 102, 383–395 (2016)Google Scholar
  46. 46.
    Chen, N.; Xu, Z.; Xia, M.: Interval-valued hesitant preference relations and their applications to group decision making. Knowl. Based Syst. 37, 528–540 (2013)Google Scholar
  47. 47.
    Farhadinia, B.: Information measures for hesitant fuzzy sets and interval-valued hesitant fuzzy sets. Inf. Sci. 240, 129–144 (2013)MathSciNetzbMATHGoogle Scholar
  48. 48.
    Zhang, X.; Xu, Z.: Interval programming method for hesitant fuzzy multi-attribute group decision making with incomplete preference over alternatives. Comput. Ind. Eng. 75, 217–229 (2014)Google Scholar
  49. 49.
    Peng, D.-H.; Wang, H.: Dynamic hesitant fuzzy aggregation operators in multi-period decision making. Kybernetes 43(5), 715–736 (2014)MathSciNetGoogle Scholar
  50. 50.
    Gitinavard, H.; Zarandi, M.H.F.: A mixed expert evaluation system and dynamic interval-valued hesitant fuzzy selection approach. World Acad. Sci. Eng. Technol. Int. J. Math. Comput. Phys. Electr. Comput. Eng. 10(7), 260–268 (2016)Google Scholar
  51. 51.
    Xu, X.: A note on the subjective and objective integrated approach to determine attribute weights. Eur. J. Oper. Res. 156(2), 530–532 (2004)zbMATHGoogle Scholar
  52. 52.
    Deng, H.; Yeh, C.-H.; Willis, R.J.: Inter-company comparison using modified TOPSIS with objective weights. Comput. Oper. Res. 27(10), 963–973 (2000)zbMATHGoogle Scholar
  53. 53.
    Wu, Z.; Chen, Y.: The maximizing deviation method for group multiple attribute decision making under linguistic environment. Fuzzy Sets Syst. 158(14), 1608–1617 (2007)MathSciNetzbMATHGoogle Scholar
  54. 54.
    Wei, G.-W.: Maximizing deviation method for multiple attribute decision making in intuitionistic fuzzy setting. Knowl. Based Syst. 21(8), 833–836 (2008)Google Scholar
  55. 55.
    Tzeng, G.-H.; Huang, J.-J.: Multiple Attribute Decision Making: Methods and Applications. CRC Press, Boca Raton (2011)zbMATHGoogle Scholar
  56. 56.
    Bottomley, P.A.; Doyle, J.R.: A comparison of three weight elicitation methods: good, better, and best. Omega 29(6), 553–560 (2001)Google Scholar
  57. 57.
    Goodwin, P.; Wright, G.; Phillips, L.D.: Decision Analysis for Management Judgment. Wiley, London (2004)Google Scholar
  58. 58.
    Ahn, B.S.; Park, K.S.: Comparing methods for multiattribute decision making with ordinal weights. Comput. Oper. Res. 35(5), 1660–1670 (2008)zbMATHGoogle Scholar
  59. 59.
    Barron, F.H.; Barrett, B.E.: Decision quality using ranked attribute weights. Manag. Sci. 42(11), 1515–1523 (1996)zbMATHGoogle Scholar
  60. 60.
    Solymosi, T.; Dombi, J.: A method for determining the weights of criteria: the centralized weights. Eur. J. Oper. Res. 26(1), 35–41 (1986)MathSciNetGoogle Scholar
  61. 61.
    Roberts, R.; Goodwin, P.: Weight approximations in multi-attribute decision models. J. Multi-Criteria Decis. Anal. 11(6), 291–303 (2002)zbMATHGoogle Scholar
  62. 62.
    Doyle, J.R.; Green, R.H.; Bottomley, P.A.: Judging relative importance: direct rating and point allocation are not equivalent. Organ. Behav. Hum. Decis. Process. 70(1), 65–72 (1997)Google Scholar
  63. 63.
    Alilou, H.; Rahmati, O.; Singh, V.P.; Choubin, B.; Pradhan, B.; Keesstra, S.; Ghiasi, S.S.; Sadeghi, S.H.: Evaluation of watershed health using Fuzzy-ANP approach considering geo-environmental and topo-hydrological criteria. J. Environ. Manag. 232, 22–36 (2019)Google Scholar
  64. 64.
    Chen, L.; Ren, J.: Multi-attribute sustainability evaluation of alternative aviation fuels based on fuzzy ANP and fuzzy grey relational analysis. J. Air Transp. Manag. 68, 176–186 (2018)Google Scholar
  65. 65.
    Sangaiah, A.K.; Gopal, J.; Basu, A.; Subramaniam, P.R.: An integrated fuzzy DEMATEL, TOPSIS, and ELECTRE approach for evaluating knowledge transfer effectiveness with reference to GSD project outcome. Neural Comput. Appl. 28(1), 111–123 (2017)Google Scholar
  66. 66.
    Lin, K.-P.; Tseng, M.-L.; Pai, P.-F.: Sustainable supply chain management using approximate fuzzy DEMATEL method. Resourc. Conserv. Recycl. 128, 134–142 (2018)Google Scholar
  67. 67.
    Wang, Y.-M.; Luo, Y.: Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making. Math. Comput. Model. 51(1), 1–12 (2010)MathSciNetzbMATHGoogle Scholar
  68. 68.
    Ma, J.; Fan, Z.-P.; Huang, L.-H.: A subjective and objective integrated approach to determine attribute weights. Eur. J. Oper. Res. 112(2), 397–404 (1999)zbMATHGoogle Scholar
  69. 69.
    Gitinavard, H.; Mousavi, S.M.; Vahdani, B.: A new multi-criteria weighting and ranking model for group decision-making analysis based on interval-valued hesitant fuzzy sets to selection problems. Neural Comput. Appl. 27(6), 1593–1605 (2016)Google Scholar
  70. 70.
    Fan, Z.-P.; Ma, J.; Zhang, Q.: An approach to multiple attribute decision making based on fuzzy preference information on alternatives. Fuzzy Sets Syst. 131(1), 101–106 (2002)MathSciNetzbMATHGoogle Scholar
  71. 71.
    Wang, Y.-M.; Parkan, C.: A general multiple attribute decision-making approach for integrating subjective preferences and objective information. Fuzzy Sets Syst. 157(10), 1333–1345 (2006)MathSciNetzbMATHGoogle Scholar
  72. 72.
    Chen, C.-F.; Lee, C.-L.: Determining the attribute weights of professional conference organizer selection: an application of the fuzzy AHP approach. Tour. Econ. 17(5), 1129–1139 (2011)MathSciNetGoogle Scholar
  73. 73.
    Xu, Z.; Zhang, X.: Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information. Knowl. Based Syst. 52, 53–64 (2013)Google Scholar
  74. 74.
    Feng, X.; Zuo, W.; Wang, J.; Feng, L.: TOPSIS method for hesitant fuzzy multiple attribute decision making. J. Intell. Fuzzy Syst. 26(5), 2263–2269 (2014)MathSciNetzbMATHGoogle Scholar
  75. 75.
    Zhang, Y.; Wang, Y.; Wang, J.: Objective attributes weights determining based on shannon information entropy in hesitant fuzzy multiple attribute decision making. Math. Probl. Eng. 2014, 1–7 (2014)Google Scholar
  76. 76.
    Tavakkoli-Moghaddam, R.; Gitinavard, H.; Mousavi, S.M.; Siadat, A.: An interval-valued hesitant fuzzy TOPSIS method to determine the criteria weights. In: International Conference on Group Decision and Negotiation. Springer (2015)Google Scholar
  77. 77.
    Zhang, Z.; Wang, C.; Tian, D.; Li, K.: Induced generalized hesitant fuzzy operators and their application to multiple attribute group decision making. Comput. Ind. Eng. 67, 116–138 (2014)Google Scholar
  78. 78.
    Gitinavard, H.; Mousavi, S.M.; Vahdani, B.: Soft computing based on hierarchical evaluation approach and criteria interdependencies for energy decision-making problems: A case study. Energy 118, 556–577 (2017)Google Scholar
  79. 79.
    Mohagheghi, V.; Mousavi, S.M.; Aghamohagheghi, M.; Vahdani, B.: A new approach of multi-criteria analysis for the evaluation and selection of sustainable transport investment projects under uncertainty: a case study. Int. J. Comput. Intell. Syst. 10, 605–626 (2017)Google Scholar
  80. 80.
    Mohagheghi, V.; Mousavi, S.M.; Vahdani, B.; Siadat, A.: A mathematical modeling approach for high and new technology-project portfolio selection under uncertain environments. J. Intell. Fuzzy Syst. 32, 4069–4079 (2017)zbMATHGoogle Scholar
  81. 81.
    Vahdani, B.; Salimi, M.; Mousavi, S.M.: A new compromise solution model based on dantzig-wolf decomposition for solving belief multi-objective nonlinear programming problems with block angular structure. Int. J. Inf. Technol. Decis. Mak. 16(2), 333–387 (2017)Google Scholar
  82. 82.
    Ghaderi, H.; Gitinavard, H.; Mousavi, S.M.; Vahdani, B.: A hesitant fuzzy cognitive mapping approach with risk preferences for student accommodation problems. Int. J. Appl. Manag. Sci. 9(4), 253–293 (2017)Google Scholar

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© King Fahd University of Petroleum & Minerals 2019

Authors and Affiliations

  • H. Salarpour
    • 1
  • G. Ghodrati Amiri
    • 1
    Email author
  • S. Meysam Mousavi
    • 2
  1. 1.School of Civil EngineeringIran University of Science and TechnologyTehranIran
  2. 2.Department of Industrial Engineering, Faculty of EngineeringShahed UniversityTehranIran

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