Solving construction project selection problem by a new uncertain weighting and ranking based on compromise solution with linear assignment approach
Abstract
Selecting a suitable construction project is a significant issue for contractors to decrease their costs. In real cases, the imprecise and uncertain information lead to decisions made based on vagueness. Fuzzy sets theory could help decision makers (DMs) to address incomplete information. However, this article develops a new integrated multi-criteria group decision-making model based on compromise solution and linear assignment approaches with interval-valued intuitionistic fuzzy sets (IVIFSs). IVIFSs by presenting a membership and non-membership degree for each candidate based on appraisement criteria could decrease the vagueness of selection decisions. The proposed algorithm involves a new decision process under uncertain conditions to determine the importance of criteria and DMs, separately. In this regard, no subjective or additional information is needed for this process; only the input information required is an alternative assessment matric. In this approach, weights of criteria and DMs are specified based on novel indexes to increase the reliability of obtained results. In this respect, the criteria’ weights are computed regarding entropy concepts. The basis for calculating the weight of each DM is the distance between each DM and an average of the DMs’ community. Furthermore, the linear assignment model is extended to rank the candidates. A case study about the construction project selection problem (CPSP) is illustrated to indicate the application of proposed model.
Keyword : construction project selection problem, experts’ weights, interval-valued intuitionistic fuzzy sets, compromise solution, incomplete information, linear assignment
How to Cite
Davoudabadi, R., Mousavi, S. M., Šaparauskas, J., & Gitinavard, H. (2019). Solving construction project selection problem by a new uncertain weighting and ranking based on compromise solution with linear assignment approach. Journal of Civil Engineering and Management, 25(3), 241-251. https://doi.org/10.3846/jcem.2019.8656
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Afsordegan, A., Sánchez, M., Agell, N., Zahedi, S., & Cremades, L. V. (2016). Decision making under uncertainty using a qualitative TOPSIS method for selecting sustainable energy alternatives. International Journal of Environmental Science and Technology, 13(6), 1419-1432. https://doi.org/10.1007/s13762-016-0982-7
Borgonovo, E., & Plischke, E. (2016). Sensitivity analysis: a review of recent advances. European Journal of Operational Research, 248(3), 869-887. https://doi.org/10.1016/j.ejor.2015.06.032
Chang, P. T., & Lee, J. H. (2012). A fuzzy DEA and knapsack formulation integrated model for project selection. Computers & Operations Research, 39(1), 112-125. https://doi.org/10.1016/j.cor.2010.10.021
Dorfeshan, Y., Mousavi, S. M., Mohagheghi, V., & Vahdani, B. (2018). Selecting project-critical path by a new interval type-2 fuzzy decision methodology based on MULTIMOORA, MOOSRA and TPOP methods. Computers & Industrial Engineering, 120, 160-178. https://doi.org/10.1016/j.cie.2018.04.015
Dorfeshan, Y., & Mousavi, S. M. (2019). A new interval type-2 fuzzy decision method with an extended relative preference relation and entropy to project critical path selection. International Journal of Fuzzy System Applications, 8(1), 19-47. https://doi.org/10.4018/IJFSA.2019010102
Doria, S. (2012). Characterization of a coherent upper conditional prevision as the Choquet integral with respect to its associated Hausdorff outer measure. Annals of Operations Research, 195(1), 33-48. https://doi.org/10.1007/s10479-011-0899-y
Düğenci, M. (2016). A new distance measure for interval valued intuitionistic fuzzy sets and its application to group decision making problems with incomplete weights information. Applied Soft Computing, 41, 120-134. https://doi.org/10.1016/j.asoc.2015.12.026
Ebrahiminejad, M., Shakeri, E., Ardeshir, A., & Zarandi, M. F. (2018). An object-oriented model for construction method selection in buildings using fuzzy information. Energy and Buildings, 178, 228-241. https://doi.org/10.1016/j.enbuild.2018.08.002
Eraslan, S. (2015). A decision making method via TOPSIS on soft sets. Journal of New Results in Science, 4(8).
Erdogan, S. A., Šaparauskas, J., & Turskis, Z. (2017). Decision making in construction management: AHP and expert choice approach. Procedia Engineering, 172, 270-276. https://doi.org/10.1016/j.proeng.2017.02.111
Foroozesh, N., Gitinavard, H., Mousavi, S. M., & Vahdani, B. (2017). A hesitant fuzzy extension of VIKOR method for evaluation and selection problems under uncertainty. International Journal of Applied Management Science, 9(2), 95-113. https://doi.org/10.1504/IJAMS.2017.084946
Garg, H. (2016). A new generalized improved score function of interval-valued intuitionistic fuzzy sets and applications in expert systems. Applied Soft Computing, 38, 988-999. https://doi.org/10.1016/j.asoc.2015.10.040
Gitinavard, H., & Zarandi, M. H. F. (2016). A mixed expert evaluation system and dynamic interval-valued hesitant fuzzy selection approach. International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, 10, 337-345.
Gitinavard, H., Makui, A., & Jabbarzadeh, A. (2016). Interval-valued hesitant fuzzy method based on group decision analysis for estimating weights of decision makers. Journal of Industrial and Systems Engineering, 9(3), 96-110.
Gitinavard, H., Mousavi, S. M., & Vahdani, B. (2016). A new multi-criteria weighting and ranking model for group decision-making analysis based on interval-valued hesitant fuzzy sets to selection problems. Neural Computing and Applications, 27(6), 1593-1605. https://doi.org/10.1007/s00521-015-1958-0
Greco, S., Matarazzo, B., & Giove, S. (2011). The Choquet integral with respect to a level dependent capacity. Fuzzy Sets and Systems, 175(1), 1-35. https://doi.org/10.1016/j.fss.2011.03.012
Ibadov, N. (2016). Construction project selection with the use of fuzzy preference relation. In AIP Conference Proceedings, 1738(1) (pp. 200005). AIP Publishing. https://doi.org/10.1063/1.4951977
Kaya, İ., & Kahraman, C. (2014). A comparison of fuzzy multicriteria decision making methods for intelligent building assessment. Journal of Civil Engineering and Management, 20(1), 59-69. https://doi.org/10.3846/13923730.2013.801906
Kaya, T., & Kahraman, C. (2011). Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology. Expert Systems with Applications, 38(6), 6577-6585. https://doi.org/10.1016/j.eswa.2010.11.081
Keramitsoglou, I., Kiranoudis, C. T., Maiheu, B., De Ridder, K., Daglis, I. A., Manunta, P., & Paganini, M. (2013). Heat wave hazard classification and risk assessment using artificial intelligence fuzzy logic. Environmental Monitoring and Assessment, 185(10), 8239-8258. https://doi.org/10.1007/s10661-013-3170-y
Khalili-Damghani, K., & Sadi-Nezhad, S. (2013). A hybrid fuzzy multiple criteria group decision making approach for sustainable project selection. Applied Soft Computing, 13(1), 339-352. https://doi.org/10.1016/j.asoc.2012.07.030
Leśniak, A., Kubek, D., Plebankiewicz, E., Zima, K., & Belniak, S. (2018). Fuzzy AHP application for supporting contractors’ bidding decision. Symmetry, 10(11), 642. https://doi.org/10.3390/sym10110642
Melin, P., & Castillo, O. (2013). A review on the applications of type-2 fuzzy logic in classification and pattern recognition. Expert Systems with Applications, 40(13), 5413-5423. https://doi.org/10.1016/j.eswa.2013.03.020
Melin, P., & Castillo, O. (2014). A review on type-2 fuzzy logic applications in clustering, classification and pattern recognition. Applied Soft Computing, 21, 568-577. https://doi.org/10.1016/j.asoc.2014.04.017
Mohagheghi, V., Mousavi, S. M., & Vahdani B. (2017b). Analyzing project cash flow by a new interval type-2 fuzzy model with an application to construction industry. Neural Computing and Applications, 28, 3393–3411. https://doi.org/10.1007/s00521-016-2235-6
Mohagheghi, V., Mousavi, S. M., & Vahdani, B. (2017a). Enhancing decision-making flexibility by introducing a new last aggregation evaluating approach based on multi-criteria group decision making and Pythagorean fuzzy sets. Applied Soft Computing, 61, 527-535. https://doi.org/10.1016/j.asoc.2017.08.003
Mohagheghi, V., Mousavi, S. M., Aghamohagheghi M., & Vahdani B. (2017). A new approach of multi-criteria analysis for the evaluation and selection of sustainable transport investment projects under uncertainty: A case study. International Journal of Computational Intelligence Systems, 10, 605-626. https://doi.org/10.2991/ijcis.2017.10.1.41
Mohagheghi, V., Mousavi, S. M., Vahdani, B., & Siadat, A. (2017). A mathematical modeling approach for high and new technology-project portfolio selection under uncertain environments. Journal of Intelligent and Fuzzy Systems, 32, 4069-4079. https://doi.org/10.3233/JIFS-152510
Moradi, N., Mousavi, S. M., & Vahdani, B. (2017). An earned value model with risk analysis for project management under uncertain conditions. Journal of Intelligent and Fuzzy Systems, 32, 97–113. https://doi.org/10.3233/JIFS-151139
Moradi, N., Mousavi, S. M., & Vahdani, B. (2018). An interval type-2 fuzzy model for project-earned value analysis under uncertainty. Journal of Multiple-Valued Logic and Soft Computing, 30, 79-103.
Özcan, E., Hamurcu, M., Alakaş, H. M., & Eren, T. (2018). Project selection by using constraint programming. Journal of Trends in the Development of Machinery and Associated Technology, 21(1), 89-92.
Oztaysi, B. (2015). A group decision making approach using interval type-2 fuzzy AHP for enterprise information systems project selection. Journal of Multiple-Valued Logic & Soft Computing, 24(5), 1-20.
Paksoy, T., Pehlivan, N. Y., & Kahraman, C. (2012). Organizational strategy development in distribution channel management using fuzzy AHP and hierarchical fuzzy TOPSIS. Expert Systems with Applications, 39(3), 2822-2841. https://doi.org/10.1016/j.eswa.2011.08.142
Papapostolou, A., Karakosta, C., & Doukas, H. (2017). Analysis of policy scenarios for achieving renewable energy sources targets: A fuzzy TOPSIS approach. Energy & Environment, 28(1–2), 88-109. https://doi.org/10.1177/0958305X16685474
Polat, G., Eray, E., & Bingol, B. N. (2017). An integrated fuzzy MCGDM approach for supplier selection problem. Journal of Civil Engineering and Management, 23(7), 926-942. https://doi.org/10.3846/13923730.2017.1343201
Prascevic, N., & Prascevic, Z. (2017). Application of fuzzy AHP for ranking and selection of alternatives in construction project management. Journal of Civil Engineering and Management, 23(8), 1123-1135. https://doi.org/10.3846/13923730.2017.1388278
Qin, J., & Liu, X. (2013). Study on interval intuitionistic fuzzy multi-attribute group decision making method based on Choquet integral. Procedia Computer Science, 17, 465-472. https://doi.org/10.1016/j.procs.2013.05.060
Salehi, K. (2015). A hybrid fuzzy MCDM approach for project selection problem. Decision Science Letters, 4(1), 109-116. https://doi.org/10.5267/j.dsl.2014.8.003
Salehi, K. (2018). Fuzzy multi-objective project selection problem using additive weighted fuzzy programming. Industrial Engineering Frontiers, 1(1), 1-15.
Shao, Z. Z., Ma, Z. J., Sheu, J. B., & Gao, H. O. (2018). Evaluation of large-scale transnational high-speed railway construction priority in the belt and road region. Transportation Research Part E: Logistics and Transportation Review, 117, 40-57. https://doi.org/10.1016/j.tre.2017.07.007
Tabrizi, B. H., Torabi, S. A., & Ghaderi, S. F. (2016). A novel project portfolio selection framework: An application of fuzzy DEMATEL and multi-choice goal programming. Scientia Iranica, 23(6), 2945-2958. https://doi.org/10.24200/sci.2016.4004
Taylan, O., Bafail, A. O., Abdulaal, R. M., & Kabli, M. R. (2014). Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing, 17, 105-116. https://doi.org/10.1016/j.asoc.2014.01.003
Wang, J., Jing, Y., Zhang, C., & Zhao, J. (2009). Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renewable and Sustainable Energy Reviews, 13, 2263-2278. https://doi.org/10.1016/j.rser.2009.06.021
Wang, L., Zhang, H. Y., Wang, J. Q., & Li, L. (2018). Picture fuzzy normalized projection-based VIKOR method for the risk evaluation of construction project. Applied Soft Computing, 64, 216-226. https://doi.org/10.1016/j.asoc.2017.12.014
Wang, W. M., Lee, A. H., Peng, L. P., & Wu, Z. L. (2013). An integrated decision making model for district revitalization and regeneration project selection. Decision Support Systems, 54(2), 1092-1103. https://doi.org/10.1016/j.dss.2012.10.035
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 3. https://doi.org/10.1016/S0019-9958(65)90241-X
Zolfaghari, S., & Mousavi, S. M. (2018). Construction-project risk assessment by a new decision model based on De-Novo multi-approaches analysis and hesitant fuzzy sets under uncertainty. Journal of Intelligent and Fuzzy Systems, 35, 639–649. https://doi.org/10.3233/JIFS-162013
Borgonovo, E., & Plischke, E. (2016). Sensitivity analysis: a review of recent advances. European Journal of Operational Research, 248(3), 869-887. https://doi.org/10.1016/j.ejor.2015.06.032
Chang, P. T., & Lee, J. H. (2012). A fuzzy DEA and knapsack formulation integrated model for project selection. Computers & Operations Research, 39(1), 112-125. https://doi.org/10.1016/j.cor.2010.10.021
Dorfeshan, Y., Mousavi, S. M., Mohagheghi, V., & Vahdani, B. (2018). Selecting project-critical path by a new interval type-2 fuzzy decision methodology based on MULTIMOORA, MOOSRA and TPOP methods. Computers & Industrial Engineering, 120, 160-178. https://doi.org/10.1016/j.cie.2018.04.015
Dorfeshan, Y., & Mousavi, S. M. (2019). A new interval type-2 fuzzy decision method with an extended relative preference relation and entropy to project critical path selection. International Journal of Fuzzy System Applications, 8(1), 19-47. https://doi.org/10.4018/IJFSA.2019010102
Doria, S. (2012). Characterization of a coherent upper conditional prevision as the Choquet integral with respect to its associated Hausdorff outer measure. Annals of Operations Research, 195(1), 33-48. https://doi.org/10.1007/s10479-011-0899-y
Düğenci, M. (2016). A new distance measure for interval valued intuitionistic fuzzy sets and its application to group decision making problems with incomplete weights information. Applied Soft Computing, 41, 120-134. https://doi.org/10.1016/j.asoc.2015.12.026
Ebrahiminejad, M., Shakeri, E., Ardeshir, A., & Zarandi, M. F. (2018). An object-oriented model for construction method selection in buildings using fuzzy information. Energy and Buildings, 178, 228-241. https://doi.org/10.1016/j.enbuild.2018.08.002
Eraslan, S. (2015). A decision making method via TOPSIS on soft sets. Journal of New Results in Science, 4(8).
Erdogan, S. A., Šaparauskas, J., & Turskis, Z. (2017). Decision making in construction management: AHP and expert choice approach. Procedia Engineering, 172, 270-276. https://doi.org/10.1016/j.proeng.2017.02.111
Foroozesh, N., Gitinavard, H., Mousavi, S. M., & Vahdani, B. (2017). A hesitant fuzzy extension of VIKOR method for evaluation and selection problems under uncertainty. International Journal of Applied Management Science, 9(2), 95-113. https://doi.org/10.1504/IJAMS.2017.084946
Garg, H. (2016). A new generalized improved score function of interval-valued intuitionistic fuzzy sets and applications in expert systems. Applied Soft Computing, 38, 988-999. https://doi.org/10.1016/j.asoc.2015.10.040
Gitinavard, H., & Zarandi, M. H. F. (2016). A mixed expert evaluation system and dynamic interval-valued hesitant fuzzy selection approach. International Journal of Mathematical, Computational, Physical, Electrical and Computer Engineering, 10, 337-345.
Gitinavard, H., Makui, A., & Jabbarzadeh, A. (2016). Interval-valued hesitant fuzzy method based on group decision analysis for estimating weights of decision makers. Journal of Industrial and Systems Engineering, 9(3), 96-110.
Gitinavard, H., Mousavi, S. M., & Vahdani, B. (2016). A new multi-criteria weighting and ranking model for group decision-making analysis based on interval-valued hesitant fuzzy sets to selection problems. Neural Computing and Applications, 27(6), 1593-1605. https://doi.org/10.1007/s00521-015-1958-0
Greco, S., Matarazzo, B., & Giove, S. (2011). The Choquet integral with respect to a level dependent capacity. Fuzzy Sets and Systems, 175(1), 1-35. https://doi.org/10.1016/j.fss.2011.03.012
Ibadov, N. (2016). Construction project selection with the use of fuzzy preference relation. In AIP Conference Proceedings, 1738(1) (pp. 200005). AIP Publishing. https://doi.org/10.1063/1.4951977
Kaya, İ., & Kahraman, C. (2014). A comparison of fuzzy multicriteria decision making methods for intelligent building assessment. Journal of Civil Engineering and Management, 20(1), 59-69. https://doi.org/10.3846/13923730.2013.801906
Kaya, T., & Kahraman, C. (2011). Multicriteria decision making in energy planning using a modified fuzzy TOPSIS methodology. Expert Systems with Applications, 38(6), 6577-6585. https://doi.org/10.1016/j.eswa.2010.11.081
Keramitsoglou, I., Kiranoudis, C. T., Maiheu, B., De Ridder, K., Daglis, I. A., Manunta, P., & Paganini, M. (2013). Heat wave hazard classification and risk assessment using artificial intelligence fuzzy logic. Environmental Monitoring and Assessment, 185(10), 8239-8258. https://doi.org/10.1007/s10661-013-3170-y
Khalili-Damghani, K., & Sadi-Nezhad, S. (2013). A hybrid fuzzy multiple criteria group decision making approach for sustainable project selection. Applied Soft Computing, 13(1), 339-352. https://doi.org/10.1016/j.asoc.2012.07.030
Leśniak, A., Kubek, D., Plebankiewicz, E., Zima, K., & Belniak, S. (2018). Fuzzy AHP application for supporting contractors’ bidding decision. Symmetry, 10(11), 642. https://doi.org/10.3390/sym10110642
Melin, P., & Castillo, O. (2013). A review on the applications of type-2 fuzzy logic in classification and pattern recognition. Expert Systems with Applications, 40(13), 5413-5423. https://doi.org/10.1016/j.eswa.2013.03.020
Melin, P., & Castillo, O. (2014). A review on type-2 fuzzy logic applications in clustering, classification and pattern recognition. Applied Soft Computing, 21, 568-577. https://doi.org/10.1016/j.asoc.2014.04.017
Mohagheghi, V., Mousavi, S. M., & Vahdani B. (2017b). Analyzing project cash flow by a new interval type-2 fuzzy model with an application to construction industry. Neural Computing and Applications, 28, 3393–3411. https://doi.org/10.1007/s00521-016-2235-6
Mohagheghi, V., Mousavi, S. M., & Vahdani, B. (2017a). Enhancing decision-making flexibility by introducing a new last aggregation evaluating approach based on multi-criteria group decision making and Pythagorean fuzzy sets. Applied Soft Computing, 61, 527-535. https://doi.org/10.1016/j.asoc.2017.08.003
Mohagheghi, V., Mousavi, S. M., Aghamohagheghi M., & Vahdani B. (2017). A new approach of multi-criteria analysis for the evaluation and selection of sustainable transport investment projects under uncertainty: A case study. International Journal of Computational Intelligence Systems, 10, 605-626. https://doi.org/10.2991/ijcis.2017.10.1.41
Mohagheghi, V., Mousavi, S. M., Vahdani, B., & Siadat, A. (2017). A mathematical modeling approach for high and new technology-project portfolio selection under uncertain environments. Journal of Intelligent and Fuzzy Systems, 32, 4069-4079. https://doi.org/10.3233/JIFS-152510
Moradi, N., Mousavi, S. M., & Vahdani, B. (2017). An earned value model with risk analysis for project management under uncertain conditions. Journal of Intelligent and Fuzzy Systems, 32, 97–113. https://doi.org/10.3233/JIFS-151139
Moradi, N., Mousavi, S. M., & Vahdani, B. (2018). An interval type-2 fuzzy model for project-earned value analysis under uncertainty. Journal of Multiple-Valued Logic and Soft Computing, 30, 79-103.
Özcan, E., Hamurcu, M., Alakaş, H. M., & Eren, T. (2018). Project selection by using constraint programming. Journal of Trends in the Development of Machinery and Associated Technology, 21(1), 89-92.
Oztaysi, B. (2015). A group decision making approach using interval type-2 fuzzy AHP for enterprise information systems project selection. Journal of Multiple-Valued Logic & Soft Computing, 24(5), 1-20.
Paksoy, T., Pehlivan, N. Y., & Kahraman, C. (2012). Organizational strategy development in distribution channel management using fuzzy AHP and hierarchical fuzzy TOPSIS. Expert Systems with Applications, 39(3), 2822-2841. https://doi.org/10.1016/j.eswa.2011.08.142
Papapostolou, A., Karakosta, C., & Doukas, H. (2017). Analysis of policy scenarios for achieving renewable energy sources targets: A fuzzy TOPSIS approach. Energy & Environment, 28(1–2), 88-109. https://doi.org/10.1177/0958305X16685474
Polat, G., Eray, E., & Bingol, B. N. (2017). An integrated fuzzy MCGDM approach for supplier selection problem. Journal of Civil Engineering and Management, 23(7), 926-942. https://doi.org/10.3846/13923730.2017.1343201
Prascevic, N., & Prascevic, Z. (2017). Application of fuzzy AHP for ranking and selection of alternatives in construction project management. Journal of Civil Engineering and Management, 23(8), 1123-1135. https://doi.org/10.3846/13923730.2017.1388278
Qin, J., & Liu, X. (2013). Study on interval intuitionistic fuzzy multi-attribute group decision making method based on Choquet integral. Procedia Computer Science, 17, 465-472. https://doi.org/10.1016/j.procs.2013.05.060
Salehi, K. (2015). A hybrid fuzzy MCDM approach for project selection problem. Decision Science Letters, 4(1), 109-116. https://doi.org/10.5267/j.dsl.2014.8.003
Salehi, K. (2018). Fuzzy multi-objective project selection problem using additive weighted fuzzy programming. Industrial Engineering Frontiers, 1(1), 1-15.
Shao, Z. Z., Ma, Z. J., Sheu, J. B., & Gao, H. O. (2018). Evaluation of large-scale transnational high-speed railway construction priority in the belt and road region. Transportation Research Part E: Logistics and Transportation Review, 117, 40-57. https://doi.org/10.1016/j.tre.2017.07.007
Tabrizi, B. H., Torabi, S. A., & Ghaderi, S. F. (2016). A novel project portfolio selection framework: An application of fuzzy DEMATEL and multi-choice goal programming. Scientia Iranica, 23(6), 2945-2958. https://doi.org/10.24200/sci.2016.4004
Taylan, O., Bafail, A. O., Abdulaal, R. M., & Kabli, M. R. (2014). Construction projects selection and risk assessment by fuzzy AHP and fuzzy TOPSIS methodologies. Applied Soft Computing, 17, 105-116. https://doi.org/10.1016/j.asoc.2014.01.003
Wang, J., Jing, Y., Zhang, C., & Zhao, J. (2009). Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renewable and Sustainable Energy Reviews, 13, 2263-2278. https://doi.org/10.1016/j.rser.2009.06.021
Wang, L., Zhang, H. Y., Wang, J. Q., & Li, L. (2018). Picture fuzzy normalized projection-based VIKOR method for the risk evaluation of construction project. Applied Soft Computing, 64, 216-226. https://doi.org/10.1016/j.asoc.2017.12.014
Wang, W. M., Lee, A. H., Peng, L. P., & Wu, Z. L. (2013). An integrated decision making model for district revitalization and regeneration project selection. Decision Support Systems, 54(2), 1092-1103. https://doi.org/10.1016/j.dss.2012.10.035
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 3. https://doi.org/10.1016/S0019-9958(65)90241-X
Zolfaghari, S., & Mousavi, S. M. (2018). Construction-project risk assessment by a new decision model based on De-Novo multi-approaches analysis and hesitant fuzzy sets under uncertainty. Journal of Intelligent and Fuzzy Systems, 35, 639–649. https://doi.org/10.3233/JIFS-162013