Application of multiple criteria decision making methods in construction: a systematic literature review

    Xingyu Zhu   Affiliation
    ; Xianhai Meng   Affiliation
    ; Min Zhang   Affiliation


Decision making is a key to business or project success in any sectors, especially in construction that requires handling numerous information and knowledge. Multiple criteria decision making (MCDM) is an important tool for decision problem solving due to simultaneous consideration of multiple criteria and objectives. Various MCDM methods are continually emerging and tend to be increasingly adopted to address the real-world construction problems. Therefore, it is urged to systematically review the existing body of literature to demonstrate the evolution of the mainstream MCDM methods in general and their application status in construction. A total of 530 construction articles published from 2000 to 2019 are selected in this study and then categorized into seven major application areas using a novel systematic literature review (SLR) methodology. The bibliometric analysis is then used to describe the research trend. Subsequently, the qualitative discussion by themes is conducted to analyze the application of MCDM methods in construction. A further discussion makes it possible to identify the potential challenges (e.g. applicability, robustness, postpone effect, dynamic and prospective challenges and scale problem) to existing research. It also contributes to the recommendation of future directions for the development of MCDM methods that would benefit construction research and practice.

Keyword : decision support system, construction, multiple criteria decision making, multiple attribute decision making, multiple objective decision making, systematic literature review

How to Cite
Zhu, X., Meng, X., & Zhang, M. (2021). Application of multiple criteria decision making methods in construction: a systematic literature review. Journal of Civil Engineering and Management, 27(6), 372-403.
Published in Issue
Jul 15, 2021
Abstract Views
PDF Downloads
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.


Abbasianjahromi, H., & Rajaie, H. (2012). Developing a project portfolio selection model for contractor firms considering the risk factor. Journal of Civil Engineering and Management, 18(6), 879–889.

Abbasianjahromi, H., Rajaie, H., & Shakeri, E. (2013). A framework for subcontractor selection in the construction industry. Journal of Civil Engineering and Management, 19(2), 158–168.

Adrian, A. M., Utamima, A., & Wang, K.-J. (2015). A comparative study of GA, PSO and ACO for solving construction site layout optimization. KSCE Journal of Civil Engineering, 19(3), 520–527.

Afshari, A. R. (2015). Selection of construction project manager by using Delphi and fuzzy linguistic decision making. Journal of Intelligent & Fuzzy Systems, 28(6), 2827–2838.

Afshari, A. R. (2017). Methods for selection of construction project manager: Case study. Journal of Construction Engineering and Management, 143(12), 06017003.

Al-Humaidi, H. M. (2016). Construction projects bid or not bid approach using the fuzzy technique for order preference by similarity FTOPSIS method. Journal of Construction Engineering and Management, 142(12), 4016068.

Alireza, V., Mohammadreza, Y., Zin, R. M., Yahaya, N., & Noor, N. M. (2014). An enhanced multi-objective optimization approach for risk allocation in public-private partnership projects: A case study of Malaysia. Canadian Journal of Civil Engineering, 41(2), 164–177.

Almeida-Dias, J., Figueira, J. R., & Roy, B. (2010). Electre TriC: A multiple criteria sorting method based on characteristic reference actions. European Journal of Operational Research, 204(3), 565–580.

Almeida-Dias, J., Figueira, J. R., & Roy, B. (2012). A multiple criteria sorting method where each category is characterized by several reference actions: The Electre Tri-nC method. European Journal of Operational Research, 217(3), 567–579.

An, X., Wang, Z., Li, H., & Ding, J. (2018). Project delivery system selection with interval-valued intuitionistic fuzzy set group decision-making method. Group Decision and Negotiation, 27(4), 689–707.

Angilella, S., Greco, S., Lamantia, F., & Matarazzo, B. (2004). Assessing non-additive utility for multicriteria decision aid. European Journal of Operational Research, 158(3), 734–744.

Antuchevičienė, J., Zavadskas, E. K., & Zakarevičius, A. (2010). Multiple criteria construction management decisions considering relations between criteria. Technological and Economic Development of Economy, 16(1), 109–125.

Ardeshir, A., Mohseni, N., Behzadian, K., & Errington, M. (2014a). Selection of a bridge construction site using fuzzy analytical hierarchy process in geographic information system. Arabian Journal for Science and Engineering, 39(6), 4405–4420.

Ardeshir, A., Amiri, M., Ghasemi, Y., & Errington, M. (2014b). Risk assessment of construction projects for water conveyance tunnels using fuzzy fault tree analysis. International Journal of Civil Engineering, 12(4), 396–412.

Atanassov, K. T. (1986). Intuitionistic fuzzy sets. Fuzzy Sets and Systems, 20(1), 87–96.

Atanassov, K., & Gargov, G. (1989). Interval valued intuitionistic fuzzy sets. Fuzzy Sets and Systems, 31(3), 343–349.

Bakht, M. N., & El-Diraby, T. E. (2015). Synthesis of decisionmaking research in construction. Journal of Construction Engineering and Management, 141(9), 4015027.

Bana e Costa, C. A., & Vansnick, J. C. (1994). MACBETH: an interactive path towards the construction of cardinal value functions. International Transactions in Operational Research, 1(4), 489–500.

Banani, R., Vandati, M. M., Shahrestani, M., & ClementsCroome, D. (2016). The development of building assessment criteria framework for sustainable non-residential buildings in Saudi Arabia. Sustainable Cities and Society, 26, 289–305.

Banker, R. D., Charnes, A., & Cooper, W. W. (1984). Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management Science, 30(9), 1078–1092.

Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management Science, 17(4), 141–164.

Bernoulli, D. (1738). Specimen theoriae novae de mensura sortis. Commentarri Academiae Scientiarum Imperialis Petropolitanae, 5, 175–192.

Bigaret, S., Hodgett, R. E., Meyer, P., Mironova, T., & Olteanu, A.-L. (2017). Supporting the multi-criteria decision aiding process: R and the MCDA package. EURO Journal on Decision Processes, 5(1–4), 169–194.

Bouyssou, D., Marchant, T., Pirlot, M., Tsoukiàs, A., & Vincke, P. (2006). Evaluation and dcision models with multiple criteria (Vol. 86). Kluwer Academic Publishers.

Brans, J. P. (1982). L’ingénierie de la décision élaboration d’instruments d’aide à la décision. In R. Nadeau & M. Landry (Eds.), L’Aide à la décision: nature, instruments et perspectives d’avenir (pp. 183–214). Presses de l’Université Laval.

Brans, J. P., & Mareschal, B. (1992). PROMETHEE V: MCDM problems with segmentation constraints. INFOR: Information Systems and Operational Research, 30(2), 85–96.

Brans, J. P., & Mareschal, B. (1995). The PROMETHEE VI procedure: how to differentiate hard from soft multicriteria problems. Journal of Decision Systems, 4(3), 213–223.

Brauers, W. K. M., & Zavadskas, E. K. 2006. The MOORA method and its application to privatization in a transition economy. Control and Cybernetics, 35(2), 443–468.

Brauers, W. K. M., & Zavadskas, E. K. 2010. Project management by MULTIMOORA as an instrument for transition economies. Technological and Economic Development of Economy, 16(1), 5–24.

Buchanan, J. T. (1997). A naïve approach for solving MCDM problems: The GUESS method. Journal of the Operational Research Society, 48(2), 202–206.

Cadena, P. C. B., & Magro, J. M. V. (2015). Setting the weights of sustainability criteria for the appraisal of transport projects. Transport, 30(3), 298–306.

Cariaga, I., El-Diraby, T., & Osman, H. (2007). Integrating value analysis and quality function deployment for evaluating design alternatives. Journal of Construction Engineering and Management, 133(10), 761–770.

Cascales, M. del S. G., Lozano, J. M. S., Arredondo, A. D. M., & Corona, C. C. (2015). Soft computing applications for renewable energy and energy efficiency. IGI Global.

Cavalcante, C. A. V., Alencar, M. H., & Lopes, R. S. (2017). Multicriteria model to support maintenance planning in residential complexes under warranty. Journal of Construction Engineering and Management, 143(4), 4016110.

Cebi, S., Celik, M., & Kahraman, C. (2010). Structuring ship design project approval mechanism towards installation of operator-system interfaces via fuzzy axiomatic design principles. Information Sciences, 180(6), 886–895.

Cha, H., & Lee, D. (2018). Determining value at risk for estimating renovation building projects by application of probability-based fuzzy set theory. Journal of Asian Architecture and Building Engineering, 17(1), 63–70.

Chalekaee, A., Turskis, Z., Khanzadi, M., Ghodrati Amiri, G., & Keršulienė, V. (2019). A new hybrid MCDM model with grey numbers for the construction delay change response problem. Sustainability, 11(3), 776.

Charnes, A., Cooper, W. W., & Ferguson, R. O. (1955). Optimal estimation of executive compensation by linear programming. Management Science, 1(2), 138–151.

Charnes, A., Cooper, W. W., Golany, B., Seiford, L., & Stutz, J. (1985). Foundations of data envelopment analysis for ParetoKoopmans efficient empirical production functions. Journal of Econometrics, 30(1–2), 91–107.

Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2(6), 429–444.

Charnes, A., Cooper, W. W., Rousseau, J., & Semple, J. (1987). Data envelopment analysis and axiomatic notions of efficiency and reference sets. University of Texas at Austin.

Chen, T.-Y. (2018). An interval-valued pythagorean fuzzy compromise approach with correlation-based closeness indices for multiple-criteria decision analysis of bridge construction methods. Complexity, Article ID 6463039.

Chen, Y.-W., & Hsieh, H.-E. (2006). Fuzzy multi-stage De-Novo programming problem. Applied Mathematics and Computation, 181(2), 1139–1147.

Chen, S.-J., & Hwang, C.-L. (1992). Fuzzy multiple attribute decision making - Methods and applications (Vol. 375). Springer.

Chen, L., & Pan, W. (2016). BIM-aided variable fuzzy multicriteria decision making of low-carbon building measures selection. Sustainable Cities and Society, 27, 222–232.

Chen, M.-F., & Tzeng, G.-H. (2004). Combining grey relation and TOPSIS concepts for selecting an expatriate host country. Mathematical and Computer Modelling, 40(13), 1473–1490.

Chen, T.-T., & Wu, T.-C. (2012). Construction project partnering using fuzzy based decision making methodology. Journal of the Chinese Institute of Engineers, 35(3), 269–284.

Chen, Y., Okudan, G. E., & Riley, D. R. (2010). Decision support for construction method selection in concrete buildings: Prefabrication adoption and optimization. Automation in Construction, 19(6), 665–675.

Chen, Y., Liu, J., Li, B., & Lin, B. (2011). Project delivery system selection of construction projects in China. Expert Systems with Applications, 38(5), 5456–5462.

Chen, J.-H., Hsu, S.-C., Luo, Y.-H., & Skibniewski, M. J. (2012). Knowledge management for risk hedging by construction material suppliers. Journal of Management in Engineering, 28(3), 273–280.

Chen, R.-H., Lin, Y., & Tseng, M.-L. (2015). Multicriteria analysis of sustainable development indicators in the construction minerals industry in China. Resources Policy, 46, 123–133.

Chen, J.-H., Hsu, S.-C., Wang, R., & Chou, H.-A. (2017). Improving hedging decisions for financial risks of construction material suppliers using grey system theory. Journal of Management in Engineering, 33(4), 04017016.

Cheng, M.-Y., & Lien, L.-C. (2012). Hybrid artificial intelligencebased PBA for benchmark functions and facility layout design optimization. Journal of Computing in Civil Engineering, 26(5), 612–624.

Cheng, M.-Y., Tsai, H.-C., Ko, C.-H., & Chang, W.-T. (2008). Evolutionary fuzzy neural inference system for decision making in geotechnical engineering. Journal of Computing in Civil Engineering, 22(4), 272–280.

Chien, K.-F., Wu, Z.-H., & Huang, S.-C. (2014). Identifying and assessing critical risk factors for BIM projects: Empirical study. Automation in Construction, 45, 1–15.

Choquet, G. (1954). Theory of capacities. Annales de l’institut Fourier, 5, 131–295.

Chou, J. S., Pham, A. D., & Wang, H. (2013). Bidding strategy to support decision-making by integrating fuzzy AHP and regression-based simulation. Automation in Construction, 35, 517–527.

Cinelli, M., Coles, S. R., & Kirwan, K. (2014). Analysis of the potentials of multi criteria decision analysis methods to conduct sustainability assessment. Ecological Indicators, 46, 138–148.

Ćirović, G., & Plamenac, D. (2006). Construction machines: Optimal choice of options using mathematical modelling. Kybernetes, 35(9), 1348–1368.

Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of the American Society for Information Science and Technology, 62(7), 1382–1402.

Cohon, J. L., & Marks, D. H. (1975). A review and evaluation of multiobjective programing techniques. Water Resources Research, 11(2), 208–220.

Cook, W. D., Tone, K., & Zhu, J. (2014). Data envelopment analysis: Prior to choosing a model. Omega, 44, 1–4.

Cuadrado, J., Zubizarreta, M., Rojí, E., Larrauri, M., & Álvarez, I. (2016). Sustainability assessment methodology for industrial buildings: Three case studies. Civil Engineering and Environmental Systems, 33(2), 106–124.

Cuong, B. C. (2014). Picture fuzzy sets. Journal of Computer Science and Cybernetics, 30(4), 409–420.

Curiel-Esparza, J., & Canto-Perello, J. (2013). Selecting utilities placement techniques in urban underground engineering. Archives of Civil and Mechanical Engineering, 13(2), 276–285.

Das, I., & Dennis, J. E. (1998). Normal-boundary intersection: A new method for generating the Pareto surface in nonlinear multicriteria optimization problems. SIAM Journal on Optimization, 8(3), 631–657.

de Azevedo, R. C., de Oliveira Lacerda, R. T., Ensslin, L., Jungles, A. E., & Ensslin, S. R. (2013). Performance measurement to aid decision making in the budgeting process for apartment building construction: Case study using MCDA-C. Journal of Construction Engineering and Management, 139(2), 225–235.

Deb, K., & Jain, H. (2014). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: Solving problems with box constraints. IEEE Transactions on Evolutionary Computation, 18(4), 577–601.

Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.

Deng, J.-L. (1982). Control problems of grey systems. Systems & Control Letters, 1(5), 288–294.

Diao, X., Li, H., Zeng, S., Tam, V. W. Y., & Guo, H. (2011). A Pareto multi-objective optimization approach for solving timecost-quality tradeoff problems. Technological and Economic Development of Economy, 17(1), 22–41.

Díaz-Cuevas, P., Camarillo-Naranjo, J. M., & Pérez-Alcántara, J. P. (2018). Relational spatial database and multi-criteria decision methods for selecting optimum locations for photovoltaic power plants in the province of Seville (southern Spain). Clean Technologies and Environmental Policy, 20(8), 1889–1902.

Dikmen, I., Birgonul, M. T., & Ozorhon, B. (2007). Project appraisal and selection using the analytic network process. Canadian Journal of Civil Engineering, 34(7), 786–792.

Dosal, E., Coronado, M., Munoz, I., Viguri, J. R., & Andres, A. (2012). Application of multi-criteria decision-making tool to locate construction and demolition waste (C&Dw) recycling facilities in a Northern Spanish region. Environmental Engineering and Management Journal, 11(3), 545–556.

Doyle, J., & Green, R. (1994). Efficiency and cross-efficiency in DEA: Derivations, meanings and uses. The Journal of the Operational Research Society, 45(5), 567–578.

Durach, C. F., Kembro, J., & Wieland, A. (2017). A new paradigm for systematic literature reviews in supply chain management. Journal of Supply Chain Management, 53(4), 67–85.

Edwards, W. (1971). Social utilities. Engineering Economist, 6, 119–129.

Edwards, W., & Barron, F. H. (1994). SMARTS and SMARTER: Improved simple methods for multiattribute utility measurement. Organizational Behavior and Human Decision Processes, 60(3), 306–325.

El-Mashaleh, M. S. (2013). Empirical framework for making the bid/no-bid decision. Journal of Management in Engineering, 29(3), 200–205.

Emmerich, M., Beume, N., & Naujoks, B. (2005). An EMO algorithm using the hypervolume measure as selection criterion. In C. A. Coello, A. Hernández Aguirre, & E. Zitzler (Eds.), Lecture notes in computer science: Vol. 3410. Evolutionary multi-criterion optimization (pp. 62–76). Springer.

Eshtehardian, E., Afshar, A., & Abbasnia, R. (2009). Fuzzy-based MOGA approach to stochastic time-cost trade-off problem. Automation in Construction, 18(5), 692–701.

Fang, C., Marle, F., Xie, M., & Zio, E. (2013). An integrated framework for risk response planning under resource constraints in large engineering projects. IEEE Transactions on Engineering Management, 60(3), 627–639.

Fare, R., Grosskopf, S., Fare, R., & Grosskopf, S. (2000). Network DEA. Socio-Economic Planning Sciences, 34(1), 35–49.

Figueira, J., Smet, Y. D., & Brans, J. (2004). MCDA methods for sorting and clustering problems: Promethee TRI and Promethee CLUSTER.

Filip, F. G. (2020). DSS – A class of evolving information systems. In G. Dzemyda, J. Bernatavičienė, & J. Kacprzyk (Eds.), Studies in computational intelligence: Vol. 869. Data science: New issues, challenges and applications (pp. 253–277). Springer, Cham.

Fishburn, P. C. (1974). Exceptional paper – Lexicographic orders, utilities and decision rules: A Survey. Management Science, 20(11), 1442–1471.

Fontela, E., & Gabus, A. (1972). World problems an invitation to further thought within the framework of DEMATEL. Geneva, Switzerland.

Fontela, E., & Gabus, A. (1976). The DEMATEL observer. Geneva, Switzerland.

Fülöp, J. (2001). Introduction to decision making methods. Laboratory of Operations Research and Decision Systems.

Gass, S., & Saaty, T. (1955). The computational algorithm for the parametric objective function. Naval Research Logistics Quarterly, 2(1–2), 39–45.

Ghapanchi, A. H., Tavana, M., Khakbaz, M. H., & Low, G. (2012). A methodology for selecting portfolios of projects with interactions and under uncertainty. International Journal of Project Management, 30(7), 791–803.

Ghoddousi, P., Nasirzadeh, F., & Hashemi, H. (2018). Evaluating highway construction projects’ sustainability using a multicriteria group decision-making model based on bootstrap simulation. Journal of Construction Engineering and Management, 144(9), 4018092.

Gholipour, Y., Hasheminasab, H., Kharrazi, M., & Streimikis, J. (2018). Sustainability criteria assessment for life-cycle phases of petroleum refinery projects by MADM technique. E&M Economics and Management, 21(3), 75–87.

Giang, D. T. H., & Pheng, L. S. (2011). Role of construction in economic development: Review of key concepts in the past 40 years. Habitat International, 35(1), 118–125.

Gigović, L., Pamučar, D., Božanić, D., & Ljubojević, S. (2017). Application of the GIS-DANP-MABAC multi-criteria model for selecting the location of wind farms: A case study of Vojvodina, Serbia. Renewable Energy, 103, 501–521.

Ginevičius, R. (2011). A new determining method for the criteria weights in multicriteria evaluation. International Journal of Information Technology & Decision Making, 10(6), 1067–1095.

Goldberg, D. E. (1989). Genetic algorithms in search, optimization, and machine learning (1st ed.). Addison-Wesley Longman Publishing Co., Inc.

Goldenberg, M., & Shapira, A. (2007). Systematic evaluation of construction equipment alternatives: Case study. Journal of Construction Engineering and Management, 133(1), 72–85.

Govindan, K., & Jepsen, M. B. (2016). ELECTRE: A comprehensive literature review on methodologies and applications. European Journal of Operational Research, 250(1), 1–29.

Greco, S., Matarazzo, B., & Słowiński, R. (2001). Rough sets theory for multicriteria decision analysis. European Journal of Operational Research, 129(1), 1–47.

Greco, S., Matarazzo, B., & Słowiński, R. (2010). Dominancebased rough set approach to decision under uncertainty and time preference. Annals of Operations Research, 176(1), 41– 75.

Gu, X., Zhao, P., & Wang, Y. (2014). Models for multiple attribute decision making based on the Einstein correlated aggregation operators with interval-valued intuitionistic fuzzy information. Journal of Intelligent & Fuzzy Systems, 26(4), 2047–2055.

Gumusay, M. U., Koseoglu, G., & Bakirman, T. (2016). An assessment of site suitability for marina construction in Istanbul, Turkey, using GIS and AHP multicriteria decision analysis. Environmental Monitoring and Assessment, 188(12), 677.

Gunduz, M., Nielsen, Y., & Ozdemir, M. (2015). Fuzzy assessment model to estimate the probability of delay in Turkish construction projects. Journal of Management in Engineering, 31(4), 4014055.

Haimes, Y. V, Lasdon, L. S., & Wismer, D. A. (1971). On a bicriterion formulation of the problems of integrated system identification and system optimization. IEEE Transactions on Systems, Man, and Cybernetics, SMC-1(3), 296–297.

Hashiyama, T., Furuhashi, T., & Uchikawa, Y. (1995). A study on varying degrees of attention in multi-attribute decision making processes. Journal of Japan Society for Fuzzy Theory and Systems, 7(4), 826–838.

Hasnain, M., Thaheem, M. J., & Ullah, F. (2018). Best value contractor selection in road construction projects: ANP-based decision support system. International Journal of Civil Engineering, 16(6A), 695–714.

Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2019). Literature review: Machine learning techniques applied to financial market prediction. Expert Systems with Applications, 124, 226–251.

Hsu, C. Y., Chen, K. T., & Tzeng, G. H. (2007). FMCDM with fuzzy DEMATEL approach for customers’ choice behavior model. International Journal of Fuzzy Systems, 9(4), 236–246.

Huang, J. J., & Tzeng, G. H. (2014). New thinking of multi-objective programming with changeable space: In search of excellence. Technological and Economic Development of Economy, 20(2), 254–273.

Hwang, C.-L., & Masud, A. S. M. (1979). Lecture notes in economics and mathematical systems: Vol. 164. Multiple objective decision making – Methods and applications. A state-of-the-art survey. Springer.

Hwang, C.-L., & Yoon, K. (1981). Lecture notes in economics and mathematical systems: Vol. 186. Multiple attribute decision making. Methods and applications: A state-of-the-art survey. Springer.

Ignatius, J., Rahman, A., Yazdani, M., Šaparauskas, J., & Haron, S. H. (2016). An integrated fuzzy ANP-QFD approach for green building assessment. Journal of Civil Engineering and Management, 22(4), 551–563.

Ilce, A. C., & Ozkaya, K. (2018). An integrated intelligent system for construction industry: a case study of raised floor material. Technological and Economic Development of Economy, 24(5), 1866–1884.

Inyim, P., Rivera, J., & Zhu, Y. (2015). Integration of building information modeling and economic and environmental impact analysis to support sustainable building design. Journal of Management in Engineering, 31(1), A4014002.

Iyer, K. C., & Banerjee, P. S. (2016). Measuring and benchmarking managerial efficiency of project execution schedule performance. International Journal of Project Management, 34(2), 219–236.

Jaskowski, P., Sobotka, A., & Czarnigowska, A. (2014). Decision model for selecting supply sources of road construction aggregates. Engineering Economics, 25(1), 13–20.

Jato-Espino, D., Castillo-Lopez, E., Rodriguez-Hernandez, J., & Canteras-Jordana, J. C. (2014). A review of application of multi-criteria decision making methods in construction. Automation in Construction, 45, 151–162.

Jessop, A. (2014). IMP: A decision aid for multiattribute evaluation using imprecise weight estimates. Omega, 49, 18–29.

Kabak, M., Köse, E., Kırılmaz, O., & Burmaoğlu, S. (2014). A fuzzy multi-criteria decision making approach to assess building energy performance. Energy and Buildings, 72, 382–389.

Kahraman, C., & Otay, İ. (Eds.). (2019). Studies in fuzziness and soft computing: Vol. 369. Fuzzy multi-criteria decision-making using neutrosophic sets. Springer International Publishing.

Kaklauskas, A. (2015). Biometric and intelligent decision making support. Springer.

Kangas, J., Kangas, A., Leskinen, P., & Pykäläinen, J. (2001). MCDM methods in strategic planning of forestry on stateowned lands in Finland: Applications and experiences. Journal of Multi-Criteria Decision Analysis, 10(5), 257–271.

KarimiAzari, A., Mousavi, N., Mousavi, S. F., & Hosseini, S. (2011). Risk assessment model selection in construction industry. Expert Systems with Applications, 38(8), 9105–9111.

Kashyap, P. (2017). Machine learning for decision makers. Machine learning for decision makers. APRESS.

Keeney, R. L. (1982). Decision analysis: An overview. Operations Research, 30, 803–838.

Keeney, R. L., & Raiffa, H. (1972). A critique of formal analysis in public decision making. In A. W. Drake, R. L. Keeney, & P. M. Morse (Eds.), Analysis of public systems (pp. 64–75). MIT Press.

Keeney, R. L., & Raiffa, H. (1976). Decision analysis with multiple conflicting objectives. John Wiley.

Keršulienė, V., Zavadskas, E. K., & Turskis, Z. (2010). Selection of rational dispute resolution method by applying new stepwise weight assessment ratio analysis (SWARA). Journal of Business Economics and Management, 11(2), 243–258.

Keshavarz Ghorabaee, M., Zavadskas, E. K., 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), 435–451.

Keshavarz Ghorabaee, M., Zavadskas, E., Amiri, M., & Turskis, Z. (2016). Extended EDAS method for fuzzy multi-criteria decision-making: An application to supplier selection. International Journal of Computers Communications & Control, 11(3), 358–371.

Khanzadi, M., Nasirzadeh, F., Hassani, S. M. H., & Mohtashemi, N. N. (2016). An integrated fuzzy multi-criteria group decision making approach for project delivery system selection. Scientia Iranica, 23(3), 802–814.

Khoramshokooh, N., Veiskarami, M., Nikoo, M. R., & Roshandeh, S. P. (2018). Multi-objective hydraulic optimization of diversion dam’s cut-off. Water Resources Management, 32(11), 3723–3736.

Koopmans, T. C. (1951). Efficient allocation of resources. Econometrica: Journal of the Econometric Society, 19(4), 455–465.

Korhonen, P. J., & Laakso, J. (1985). On developing a visual interactive multiple criteria method: An outline. In Y. Y. Haimes & V. Chankong (Eds.), Decision making with multiple objectives (pp. 272–281). Springer.

Korhonen, P. J., & Laakso, J. (1986). A visual interactive method for solving the multiple criteria problem. European Journal of Operational Research, 24(2), 277–287.

Korhonen, P., & Laakso, J. (1984). A visual interactive method for solving the multiple-criteria problem. In M. Grauer & A. P. Wierzbicki (Eds.), Interactive decision analysis (pp. 146–153). Springer.

Krylovas, A., Zavadskas, E. K., Kosareva, N., & Dadelo, S. (2014). New KEMIRA method for determining criteria priority and weights in solving MCDM problem. International Journal of Information Technology & Decision Making, 13(6), 1119–1133.

Kuhn, H. W., & Tucker, A. W. (1951). Nonlinear programming. In Proceedings of the Berkeley Symposium of Mathematical Statistics and Probability (pp. 481–491). University of California Press.

Kutlu Gündoğdu, F., & Kahraman, C. (2019). Spherical fuzzy sets and spherical fuzzy TOPSIS method. Journal of Intelligent & Fuzzy Systems, 36(1), 337–352.

Kutlu Gündoğdu, F., & Kahraman, C. (2020). A novel spherical fuzzy analytic hierarchy process and its renewable energy application. Soft Computing, 24(6), 4607–4621.

Latifi, M., Rakhshandehroo, G., Nikoo, M. R., & Sadegh, M. (2019). A game theoretical low impact development optimization model for urban storm water management. Journal of Cleaner Production, 241, 118323.

Li, R. J., & Lee, E. S. (1990). Multi-criteria de Novo programming with fuzzy parameters. Computers & Mathematics with Applications, 19(5), 13–20.

Li, F., Phoon, K. K., Du, X., & Zhang, M. (2013). Improved AHP method and its application in risk identification. Journal of Construction Engineering and Management, 139(3), 312–320.

Li, H., Xiong, L., Liu, Y., & Li, H. (2018). An effective genetic algorithm for the resource levelling problem with generalised precedence relations. International Journal of Production Research, 56(5), 2054–2075.

Liang, H., Zhang, S., & Su, Y. (2017a). Evaluating the efficiency of industrialization process in prefabricated residential buildings using a fuzzy multicriteria decision-making method. Mathematical Problems in Engineering, Article ID 6078490.

Liang, R., Dong, Z., Sheng, Z., Wang, X., & Wu, C. (2017b). Case study of selecting decision-making schemes in large-scale infrastructure projects. Journal of Infrastructure Systems, 23(4), 6017001.

Lin, C.-C., Wang, W.-C., & Yu, W.-D. (2008). Improving AHP for construction with an adaptive AHP approach (A3). Automation in Construction, 17(2), 180–187.

Lin, Y.-H., Chen, Y.-P., Yang, M.-D., & Su, T.-C. (2016). Multiobjective optimal design of sewerage rehabilitation by using the nondominated sorting genetic algorithm-II. Water Resources Management, 30(2), 487–503.

Liou, J. J. H., & Tzeng, G.-H. (2012). Comments on “Multiple criteria decision making (MCDM) methods in economics: An overview”. Technological and Economic Development of Economy, 18(4), 672–695.

Liu, W., & Sharp, J. (1999). DEA models via goal programming. In Data envelopment analysis in the service sector (pp. 79– 101). Deutscher Universitätsverlag.

Liu, B., Huo, T., Shen, Q., Yang, Z., Meng, J., & Xue, B. (2015a). Which owner characteristics are key factors affecting project delivery system decision making? Empirical analysis based on the rough set theory. Journal of Management in Engineering, 31(4), 5014018.

Liu, J., Liu, P., Liu, S.-F., Zhou, X.-Z., & Zhang, T. (2015b). A study of decision process in MCDM problems with large number of criteria. International Transactions in Operational Research, 22(2), 237–264.

Liu, B., Huo, T., Meng, J., Gong, J., Shen, Q., & Sun, T. (2016a). Identification of key contractor characteristic factors that affect project success under different project delivery systems: Empirical analysis based on a group of data from China. Journal of Management in Engineering, 32(1), 5015003.

Liu, J. S., Lu, L. Y. Y., & Lu, W. (2016b). Research fronts in data envelopment analysis. Omega, 58, 33–45.

Liu, Y., Li, F., Wang, Y., Yu, X., Yuan, J., & Wang, Y. (2018). Assessing the environmental impact caused by power grid projects in high altitude areas based on BWM and vague sets techniques. Sustainability, 10(6), 1768.

Loron, A. S., & Loron, M. S. (2015). An integrated fuzzy analytic hierarchy process-fuzzy data envelopment analysis (FAHPFDEA) method for intelligent building assessment. Tehnicki Vjesnik-Technical Gazette, 22(2), 383–389.

Lu, H., Wang, H., Xie, Y., & Wang, X. (2018). Study on construction material allocation policies: A simulation optimization method. Automation in Construction, 90, 201–212.

MacCrimmon, K. R. (1968). Decision making among multiple– attribute alternatives: A survey and consolidated approach (Memorandum RM-4823-ARPA). The RAND corporation.

Malekmohammadi, B., & Moghadam, N. T. (2018). Application of Bayesian networks in a hierarchical structure for environmental risk assessment: A case study of the Gabric Dam, Iran. Environmental Monitoring and Assessment, 190(5), 279.

Marcher, C., Giusti, A., & Matt, D. T. (2020). Decision support in building construction: A systematic review of methods and application areas. Buildings, 10(10), 170.

Marzouk, M., & Al Daour, I. (2018). Planning labor evacuation for construction sites using BIM and agent-based simulation. Safety Science, 109, 174–185.

Masoumi, I., Ahangari, K., & Noorzad, A. (2018). Optimal monitoring instruments selection using innovative decision support system framework. Smart Structures and Systems, 21(1), 123–137.

Mavi, R. K., & Standing, C. (2018). Critical success factors of sustainable project management in construction: A fuzzy DEMATEL-ANP approach. Journal of Cleaner Production, 194, 751–765.

Messac, A., Ismail-Yahaya, A., & Mattson, C. A. (2003). The normalized normal constraint method for generating the Pareto frontier. Structural and Multidisciplinary Optimization, 25(2), 86–98.

Miandoabchi, E., Daneshzand, F., Zanjirani Farahani, R., & Szeto, W. Y. (2015). Time-dependent discrete road network design with both tactical and strategic decisions. Journal of the Operational Research Society, 66(6), 894–913.

Michalewicz, Z. (1996). Genetic algorithms + Data structures = Evolution programs. Springer.

Miettinen, K. (1998). Nonlinear multiobjective optimization. Kluwer Academic Publishers.

Miettinen, K., & Mäkelä, M. M. (2006). Synchronous approach in interactive multiobjective optimization. European Journal of Operational Research, 170(3), 909–922.

Migilinskas, D., Pavlovskis, M., Urba, I., & Zigmund, V. (2017). Analysis of problems, consequences and solutions for BIM application in reconstruction projects. Journal of Civil Engineering and Management, 23(8), 1082–1090.

Mikhailov, L., & Singh, M. G. (2003). Fuzzy analytic network process and its application to the development of decision support systems. IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), 33(1), 33–41.

Mirahadi, F., & Zayed, T. (2016). Simulation-based construction productivity forecast using neural-network-driven fuzzy reasoning. Automation in Construction, 65, 102–115.

Monghasemi, S., Nikoo, M. R., Fasaee, M. A. K., & Adamowski, J. (2015). A novel multi criteria decision making model for optimizing time-cost-quality trade-off problems in construction projects. Expert Systems with Applications, 42(6), 3089–3104.

Nakayama, H. (1995). Aspiration level approach to interactive multi-objective programming and its applications. In P. M. Pardalos, Y. Siskos, & C. Zopounidis (Eds.), Advances in multicriteria analysis (pp. 147–174). Springer.

Nazari, A., Vandadian, S., & Abdirad, H. (2017). Fuzzy AHP model for prequalification of engineering consultants in the Iranian public procurement system. Journal of Management in Engineering, 33(2), 4016042.

Nieto-Morote, A., & Ruz-Vila, F. (2012). A fuzzy multi-criteria decision-making model for construction contractor prequalification. Automation in Construction, 25, 8–19.

Nikoo, M. R., Khorramshokouh, N., & Monghasemi, S. (2015). Optimal design of detention rockfill dams using a simulation-based optimization approach with mixed sediment in the flow. Water Resources Management, 29(15), 5469–5488.

Ning, X., Ding, L. Y., Luo, H. B., & Qi, S. J. (2016). A multi-attribute model for construction site layout using intuitionistic fuzzy logic. Automation in Construction, 72, 380–387.

Ning, X., Lam, K.-C., & Lam, M. C.-K. (2011). A decision-making system for construction site layout planning. Automation in Construction, 20(4), 459–473.

Opricovic, S. (1998). Multicriteria optimization of civil engineering systems [PhD thesis]. Faculty of Civil Engineering, University of Belgrade, Serbia.

Opricovic, S., & Tzeng, G.-H. (2002). Multicriteria planning of post‐earthquake sustainable reconstruction. Computer-Aided Civil and Infrastructure Engineering, 17, 211–220.

Opricovic, S., & Tzeng, G.-H. (2007). Extended VIKOR method in comparison with outranking methods. European Journal of Operational Research, 178(2), 514–529.

Ozcan-Deniz, G., Zhu, Y., & Ceron, V. (2012). Time, cost and environmental impact analysis on construction operation optimization using genetic algorithms. Journal of Management in Engineering, 28(3), 265–272.

Palha, R. P., de Almeida, A. T., & Alencar, L. H. (2016). A model for sorting activities to be outsourced in civil construction based on ROR-UTADIS. Mathematical Problems in Engineering, Article ID 9236414.

Pan, Y., & Zhang, L. (2021). Roles of artificial intelligence in construction engineering and management: A critical review and future trends. Automation in Construction, 122, 103517.

Pawlak, Z. (1982). Rough sets. International Journal of Computer & Information Sciences, 11(5), 341–356.

Pawlak, Z., & Słowiński, R. (1994). Rough set approach to multiattribute decision analysis. European Journal of Operational Research, 72(3), 443–459.

Perny, P., & Roy, B. (1992). The use of fuzzy outranking relations in preference modelling. Fuzzy Sets and Systems, 49(1), 33–53.

Plebankiewicz, E. (2014). Modelling decision-making processes in bidding procedures with the use of the fuzzy sets theory. International Journal of Strategic Property Management, 18(3), 307–316.

Polat, G., & Bingol, B. N. (2017). Data envelopment analysis (DEA) approach for making the bid/no bid decision: A case study in a Turkish construction contracting company. Scientia Iranica, 24(2), 497–511.

Pons, O., & Aguado, A. (2012). Integrated value model for sustainable assessment applied to technologies used to build schools in Catalonia, Spain. Building and Environment, 53, 49–58.

Pons, O., de la Fuente, A., & Aguado, A. (2016). The use of MIVES as a sustainability assessment MCDM method for architecture and civil engineering applications. Sustainability, 8(5), 460.

Purshouse, R. C., Deb, K., Mansor, M. M., Mostaghim, S., & Wang, R. (2014). A review of hybrid evolutionary multiple criteria decision making methods. In 2014 IEEE Congress on Evolutionary Computation (CEC), Beijing, China.

Radziszewska-Zielina, E. (2010). Methods for selecting the best partner construction enterprise in terms of partnering relations. Journal of Civil Engineering and Management, 16(4), 510–520.

Rahimi, Y., Tavakkoli-Moghaddam, R., Iranmanesh, S. H., & Vaez-Alaei, M. (2018). Hybrid approach to construction project risk management with simultaneous FMEA/ISO 31000/ evolutionary algorithms: Empirical optimization study. Journal of Construction Engineering and Management, 144(6), 4018043.

Rahman, S., Odeyinka, H., Perera, S., & Bi, Y. (2012). Productcost modelling approach for the development of a decision support system for optimal roofing material selection. Expert Systems with Applications, 39(8), 6857–6871.

RazaviAlavi, S., & AbouRizk, S. (2017). Genetic algorithmsimulation framework for decision making in construction site layout planning. Journal of Construction Engineering and Management, 143(1), 4016084.

Reizgevicius, M., Ustinovichius, L., Simanaviciene, R., Rasiulis, R., & Peliksa, M. (2014). The evaluation and justification of the effectiveness of 4D CAD using multi-criteria analysis. Journal of Civil Engineering and Management, 20(6), 884–892.

Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53, 49–57.

Ristić, V., Maksin, M., Nenković-Riznić, M., & Basarić, J. (2018). Land-use evaluation for sustainable construction in a protected area: A case of Sara mountain national park. Journal of Environmental Management, 206, 430–445.

Roy, B. (1968). Classement et choix en présence de points de vue multiples. Revue Française d’informatique et de Recherche Opérationnelle, 2(8), 57–75.

Roy, B. (1971). Problems and methods with multiple objective functions. Mathematical Programming, 1(1), 239–266.

Roy, B. (1976). From optimisation to multicriteria decision aid: Three main operational attitudes. In H. Thiriez & S. Zionts (Eds.), Lecture notes in economics and mathematical systems (Operations research): Vol. 130. Multiple criteria decision making (pp. 1–34). Springer.

Roy, B. (1977). Electre III, un algorithme de classement fondé sur une représentation floue des préférences en présence de critères multiples. Cahiers Du Centre d’études de Recherche Opérationnelle, 20(1), 3–24.

Roy, B. (1996). Multicriteria methodology for decision aiding (Vol. 12). Springer US.

Roy, B. & Vincke, P. (1981). Multicriteria analysis: Survey and new directions. European Journal of Operational Research, 8(3), 207–218.

Saaty, T. L. (1972). An eigenvalue allocation model for prioritization and planning (Working paper). Energy Management and Policy Center, University of Pennsylvania.

Saaty, T. L. (1992). A natural way to make momentous decisions. Journal of Scientific & Industrial Research, 51(8–9), 69–81.

Saaty, T. L. (1996). Decision making with dependence and feedback: The Analytic Network Process. RWS Publications.

Sakawa, M., Inuiguchi, M., Sunada, H., & Sawada, K. (1994). Fuzzy multiobjective combinatorial optimization through revised genetic algorithms. Journal of Japan Society for Fuzzy Theory and Systems, 6(1), 177–186.

Sakawa, M., Yumine, T., & Nango, Y. (1984). Interactive fuzzy decisionmaking for multiobjective nonlinear programming problems. Electronics and Communications in Japan (Part I: Communications), 67(4), 31–38.

Sałabun, W., Ziemba, P., & Wątróbski, J. (2016). The rank reversals paradox in management decisions: The comparison of the AHP and COMET methods. In I. Czarnowski, A. M. Caballero, R. J. Howlett, & L. C. Jain (Eds.), Smart innovation, systems and technologies: Vol. 56. Intelligent decision technologies (pp. 181–191). Springer International Publishing.

Salah, A., & Moselhi, O. (2016). Risk identification and assessment for engineering procurement construction management projects using fuzzy set theory. Canadian Journal of Civil Engineering, 43(5), 429–442.

Samantra, C., Datta, S., & Mahapatra, S. S. (2017). Fuzzy based risk assessment module for metropolitan construction project: An empirical study. Engineering Applications of Artificial Intelligence, 65, 449–464.

Sasaki, M., Gen, M., & Yamashiro, M. (1995). A method for solving fuzzy de novo programming problem by genetic algorithms. Computers & Industrial