Abstract
The inherent role of the Main Contractors (MCs) and their responsibility to employers force them to consider an appropriate mechanism for evaluating the subcontractors (SCs). The past few decades witnessed the gradual prevalence of fuzzy set theory in different industries. The construction industry was not an exception either. It is in use now for SCs prequalification thanks to its competence in covering the flaws underlying the SC selection methods. Most previous researches employed Type-1 Fuzzy Sets (T1FSs) in SCs prequalification models. But, the result of the applying TIFSs to solve a SC prequalification could be simply misleading because it is principally a group (Multi-Criteria Decision Making) MCDM problem. An important limitation of T1FS assures the fact that it can handle the decision-making problem of only one person because its membership function is crisp, relying on his subjective viewpoint, and deterring it from revealing differing views among the experts. We aim to introduce a novel SCs prequalification method using Interval Type-2 Fuzzy Sets (IT2FSs) in a bid to rectify the linguistic flaws as well as differences of the experts’ views. Therefore, an integration of two methods, namely “Shannon Entropy Weight” and “Technique for Order Performance by Similarity to Ideal Solutions,” is used under IT2FS for solving the SC prequalification problem. In addition, a real case is considered to demonstrate the effectiveness of the proposed method.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abbasianjahromi H, Rajaie H, Shakeri E (2013) A framework for subcontractor selection in the construction industry. J Civil Eng Manag 19(2):158–168. https://doi.org/10.3846/13923730.2012.743922
Abbasianjahromi H, Sepehri M, Abbasi O (2018) A decision-making framework for subcontractor selection in construction projects. Eng Manag J 30(2):141–152. https://doi.org/10.1080/10429247.2018.1448967
Acheamfour VK, Kissi E, Adjei-Kumi T (2019) Ascertaining the impact of contractors pre-qualification criteria on project success criteria. Eng Constr Architect Manag 26(4):618–632. https://doi.org/10.1108/ECAM-03-2018-0110
Afshar MR, Alipouri Y, Sebt MH, Chan WT (2017) A type-2 fuzzy set model for contractor prequalification. Autom Constr 84:356–366. https://doi.org/10.1016/j.autcon.2017.10.003
Aje I (2012) The impact of contractors’ prequalification on construction project delivery in Nigeria. Eng Constr Architect Manag 19(2):159–172. https://doi.org/10.1108/09699981211206098
Albino V, Garavelli AC (1998) A neural network application to subcontractor rating in construction firms. Int J Project Manage 16(1):9–14. https://doi.org/10.1016/S0263-7863(97)00007-0
Anh N, Sundaram S, Pratama M, Srikanth N (2019) Interval prediction of wave energy characteristics using meta-cognitive interval type-2 fuzzy inference system. Knowl Based Syst 169:28–38. https://doi.org/10.1016/j.knosys.2019.01.025
Arslan G, Kivrak S, Birgonul MT, Dikmen I (2008) Improving sub-contractor selection process in construction projects: web-based sub-contractor evaluation system (WEBSES). Autom Constr 17(4):480–488. https://doi.org/10.1016/j.autcon.2007.08.004
Baykasoğlu A, Gölcük İ (2017) Development of an interval type-2 fuzzy sets based hierarchical MADM model by combining DEMATEL and TOPSIS. Expert Syst Appl 70:37–51. https://doi.org/10.1016/j.eswa.2016.11.001
Birjandi AK, Akhyani F, Sheikh R, Sana SS (2019) Evaluation and selecting the contractor in bidding with incomplete information using MCGDM method. Soft Comput 1:17. https://doi.org/10.1007/s00500-019-04050-y
Celik E, Akyuz E (2018) An interval type-2 fuzzy AHP and TOPSIS methods for decision-making problems in maritime transportation engineering: the case of ship loader. Ocean Eng 155:371–381. https://doi.org/10.1016/j.oceaneng.2018.01.039
Chen SM, Lee LW (2010) Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method. Expert Syst Appl 37(4):2790–2798. https://doi.org/10.1016/j.eswa.2009.09.012
Chen L, Peng J, Zhang B, Rosyida I (2017a) Diversified models for portfolio selection based on uncertain semivariance. Int J Syst Sci 48(3):637–648. https://doi.org/10.1080/00207721.2016.1206985
Chen L, Peng J, Liu Z, Zhao R (2017b) Pricing and effort decisions for a supply chain with uncertain information. Int J Prod Res 55(1):264–284. https://doi.org/10.1080/00207543.2016.1204475
Cheng MY, Tsai HC, Sudjono E (2011) Evaluating subcontractor performance using evolutionary fuzzy hybrid neural network. Int J Project Manage 3:349–356. https://doi.org/10.1016/j.ijproman.2010.03.005
Choudhry RM, Hinze JW, Arshad M, Gabriel HF (2012) Subcontracting Practices in the Construction Industry of Pakistan. J Constr Eng Manage 138:1353–1359. https://doi.org/10.1061/(ASCE)CO.1943-7862.0000562
Deng Y, Sadiq R, Jiang W, Tesfamariam S (2011) Risk analysis in a linguistic environment: a fuzzy evidential reasoning-based approach. Expert Syst Appl 38(12):15438–15446. https://doi.org/10.1016/j.eswa.2011.06.018
Enshassi A, Arain FM, Tayeh B (2010) Subcontractor prequalification practices in Palestine. Int J Constr Manag 10(4):45–74. https://doi.org/10.1080/15623599.2010.10773154
García-Cascales MS, Lamata MT (2012) On rank reversal and TOPSIS method. Math Comput Model 56:123–132. https://doi.org/10.1016/j.mcm.2011.12.022
Haghighi MH, Mousavi SM, Mohagheghi V (2019) A new soft computing model based on linear assignment and linear programming technique for multidimensional analysis of preference with interval type-2 fuzzy sets. Appl Soft Comput 77:780–796. https://doi.org/10.1016/j.asoc.2019.01.048
Hao M, Mendel JM (2016) Encoding words into normal interval type-2 fuzzy sets: hM approach. IEEE Trans Fuzzy Syst 24(4):865–879. https://doi.org/10.1109/TFUZZ.2015.2486814
Hwang CL, Yoon K (1981) Multiple attribute decision making, methods and applications. Springer, New York
Iordache M, Schitea D, Deveci M, Akyurt İZ, Iordache I (2019) An integrated ARAS and interval type-2 hesitant fuzzy sets method for underground site selection: seasonal hydrogen storage in salt caverns. J Petrol Sci Eng 175:1088–1098. https://doi.org/10.1016/j.petrol.2019.01.051
Jaskowski P, Biruk S, Bucon R (2010) Assessing contractor selection criteria weights with fuzzy AHP method application in group decision environment. Autom Constr 19(2):120–126. https://doi.org/10.1016/j.autcon.2009.12.014
Kahraman C, Öztayşi B, Sarı İU, Turanoğlu E (2014) Fuzzy analytic hierarchy process with interval type-2 fuzzy sets. Knowl Based Syst 59:48–57. https://doi.org/10.1016/j.knosys.2014.02.001
Ko CH, Cheng MY, Wu TK (2007) Evaluating sub-contractors performance using EFNIM. Autom Constr 16(4):525–530. https://doi.org/10.1016/j.autcon.2006.09.005
Lam KC, Hu T, NgS Thomas, Skitmore M, Cheung SO (2001) A fuzzy neural network approach for contractor prequalification. Constr Manag Econ 19(2):175–188. https://doi.org/10.1080/01446190150505108
Mohsen O, Fereshteh N (2017) An extended VIKOR method based on entropy measure for the failure modes risk assessment–A case study of the geothermal power plant (GPP). Saf Sci 92:160–172. https://doi.org/10.1016/j.ssci.2016.10.006
Polat G (2016) Subcontractor selection using the integration of the AHP and PROMETHEE methods. J Civil Eng Manag 22(8):1042–1054. https://doi.org/10.3846/13923730.2014.948910
Rao C, Xiao X, Goh M, Zheng J, Wen J (2017) Compound mechanism design of supplier selection based on multi-attribute auction and risk management of supply chain. Comput Ind Eng 105:63–75. https://doi.org/10.1016/j.cie.2016.12.042
Shad R, Khorrami M, Ghaemi M (2017) Developing an Iranian green building assessment tool using decision making methods and geographical information system: case study in Mashhad city. Renew Sustain Energy Rev 67:324–340. https://doi.org/10.1016/j.rser.2016.09.004
Üsdiken B, Sözen Z, Enbiyaoḡlu H (1988) Strategies and boundaries: subcontracting in construction. Strateg Manag J 9(6):633–637. https://doi.org/10.1002/smj.4250090609
Verma M, Rajasankar J (2017) A thermodynamical approach towards group multi-criteria decision making (GMCDM) and its application to human resource selection. Appl Soft Comput 52:323–332. https://doi.org/10.1016/j.asoc.2016.10.033
Wu D, Mendel JM, Coupland S (2012) Enhanced interval approach for encoding words into interval type-2 fuzzy sets and its convergence analysis. IEEE Trans Fuzzy Syst 20(3):499–513. https://doi.org/10.1109/TFUZZ.2011.2177272
Xiao Q, Chen L, Xie M, Wang C (2020) Optimal contract design in sustainable supply chain: interactive impacts of fairness concern and overconfidence. J Oper Res Soc. https://doi.org/10.1080/01605682.2020.1727784
Xu Z, Qin J, Liu J, Martínez L (2019) Sustainable supplier selection based on AHPSort II in interval type-2 fuzzy environment. Inf Sci 483:273–293. https://doi.org/10.1016/j.ins.2019.01.013
Zadeh LA (1975) The concept of a linguistic variable and its application to approximate reasoning—I. Inf Sci 8(3):199–249. https://doi.org/10.1016/0020-0255(75)90036-5
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Financial Disclosures
The authors have no relevant financial disclosures.
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Communicated by V. Loia.
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Afshar, M.R., Shahhosseini, V. & Sebt, M.H. An interval type-2 fuzzy MCDM model for work package subcontractor prequalification. Soft Comput 25, 635–648 (2021). https://doi.org/10.1007/s00500-020-05173-3
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-020-05173-3